malaria outbreak case study

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• Published: 19 June 2023

Malaria surveillance, outbreak investigation, response and its determinant factors in Waghemra Zone, Northeast Ethiopia: unmatched case–control study

• Habtu Debash 1 ,
• Marye Nigatie 3 ,
• Habtye Bisetegn 1 ,
• Daniel Getacher Feleke 4 ,
• Gebru Tesfaw 2 ,
• Askale Amha 5 ,
• Megbaru Alemu Abate 6 , 7 &
• Alemu Gedefie 1  

Scientific Reports volume  13 , Article number:  9938 ( 2023 ) Cite this article

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• Health care
• Microbiology
• Pathogenesis
• Risk factors

Malaria is a major global public health concern, with around half of the world's population at risk of infection. It is one of the most common epidemic-prone diseases, resulting in on-going epidemics and significant public health problems. On September 12, 2022, Waghemra Zone malaria monitoring data revealed that the district was suffering an unusually high number of malaria cases. Therefore, the aim of this study was to assess the occurrence of malaria outbreaks and investigate contracting factors in Waghemra Zone, Northeast Ethiopia. A community-based case–control study with a 1:1 ratio was employed at Waghemra Zone from September 14 to November 27, 2022. A total of 260 individuals (130 cases and 130 controls) were included in the study. A structured questionnaire was used to collect the data. Malaria cases were confirmed by either microscopy or malaria rapid diagnostic tests. The magnitude of the outbreak was described by place, person, and time. A multivariable logistic regression analysis was conducted to identify malaria risk factors. A total of 13,136 confirmed cases of malaria were detected in the Waghemra zone, with an overall attack rate of 26.5 per 1000 and slide positivity rate was 43.0%. The predominant species was Plasmodium falciparum accounting for 66.1%. Children under five years old (AOR = 5.1; 95% CI 2.6–23.0), the presence of artificial water-holding bodies (AOR: 2.7; 95% CI 1.340–5.420), intermittent rivers closer to the living house (AOR = 4.9; 95% CI 2.51–9.62), sleeping outside a home (AOR = 4.9; 95% CI 2.51–9.62), and a lack of knowledge about malaria transmission and prevention (AOR: 9.7; 95% CI 4.459–20.930) were factors associated with malaria contraction. The overall attack rate for malaria during this outbreak was high. Children less than five years, the presence of mosquito breeding sites, staying outdoors overnight, and a lack of knowledge on malaria transmission and prevention were predictors of malaria. Early management of local vector breeding places, as well as adequate health education on malaria transmission and prevention methods, should be provided to the community to prevent such outbreaks in the future.

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Introduction.

Malaria is a widespread and debilitating tropical disease caused by Plasmodium species and transmitted through the bites of infected female Anopheles mosquitoes 1 . According to the World Health Organization's (WHO) 2021 malaria report, the WHO African regions continue to suffer the greatest burden of malaria. The African Region accounted for 95% of all malaria cases (228 million) and 96% of all malaria deaths (602 000) in 2020, with children under the age of five accounting for 80% of all malaria deaths in the region. Malaria services were hampered beginning in 2020 because of the Covid-19 epidemic, adding to the region's malaria load 2 .

Malaria is a major public health issue in Ethiopia, where it is estimated that 68% of the population resides 3 . Despite widespread deployment of malaria prevention strategies such as early diagnosis and treatment, indoor residual spraying, and mass distribution campaigns of long-lasting insecticide-treated bed nets 4 , Ethiopia has the highest incidence of malaria cases. Malaria is mostly an endemic disease in the country, and outbreaks sometimes happen. Its transmissions peak between September and December, following the main rainy season, and between June and August 3 .

Recurrent outbreaks and epidemics are linked to cyclical weather fluctuations in the country, which lead to enhanced vector survival. Other triggering factors include exceptional local weather events and activities that result in environmental alteration, increasing vector populations, and increasing population vulnerability to famine, starvation, and conflict 3 , 5 . More than 542,000 people have been displaced as a result of internal conflict in Amhara region Ethiopia. The Waghemra zone has been severely affected by this internal conflict 6 . The conflict has led to the deterioration of health services, the interruption of anti-malarial treatments, and the movement of people, which has resulted in the failure of efforts to keep malaria under control and the likelihood of an outbreak 7 .

The Waghemra zone is one of the most malaria-prevalent areas in the Amhara region of northeast Ethiopia. On September 12, 2022, malaria monitoring data obtained from the Zone Health Office revealed that the districts were experiencing an exceptionally high number of malaria cases. In WHO epidemiologic week 36 of 2022, a total of 190 malaria cases were registered, compared to only 122 cases in the same epidemiologic week during the threshold period (2016–2020). On September 14, 2022, a rapid response team was dispatched to the affected districts to confirm the existence of the outbreak, identify risk factors, and aid in intervention actions.

Understanding the causes of outbreaks in these areas allows for early case management, identification of variables that maintain the disease, and the design of more effective preventative and control methods to facilitate malaria elimination by 2030. As a result, the goal of this study was to confirm the occurrence of the outbreak, identify gaps and risk factors that contributed to the outbreak's existence, and provide appropriate public health intervention for the outbreak in the Waghemra zone.

Materials and methods

Waghemra Zone is one of eleven zones in Amhara region of Ethiopia. The Waghemra zone is defined by the following latitude and longitude coordinates: 12° 45′ 54" N, 38° 50′ 34.8"E and has an elevation of 1498 m. In terms of health care, it has 136 health posts, 34 health centers, one general hospital, and two primary hospitals. This zone is divided into eight districts with a total population of 536,129 people. Data was collected from Ziquala, Sahala, Abergelie, Dehana, Sekota Zuria, Sekota Town and Gazgibla districts. However, due to the presence of war during data collection in the Tsagbji district and some kebeles in the Abergele district were excluded. The outbreak occurs in all districts, but the severity varies. The area's average yearly temperature and rainfall are 26 °C and 786 mm, respectively. The climate and topography of the study areas are conducive to Anopheles mosquito breeding, and malaria transmission is prevalent.

Study design and period

Community based unmatched case–control study was conducted from September 14 to November 27, 2022.

Source population, study subject and variables

People living in the Amhara region's Waghemra zone who are at risk of malaria are the source population. And the specific study subjects for these cases were febrile patients who tested positive for malaria parasites by either Rapid Diagnostic test (RDT) or a microscope. Controls, on the other hand, were classified as having no signs and symptoms of acute febrile illness one month before data collection. A non-febrile, apparently healthy person living in the same village as the active case patient from September 14 to November 27, 2022, was studied as a control subject. Controls were selected regardless of their age, gender, educational status, physiological status, and socio-economic status. The independent variables were socio-demographic and economic characteristics, behavioral factors like Insecticide-Treated Nets (ITN) use, Indoor Residual Spray (IRS), sleeping area at night and environmental factors.

Descriptive and analytical epidemiology

Confirm the diagnosis and verify the existence of the outbreak.

Malaria data from the last six years (2016–2021) were analyzed at the Waghemra zone health office to determine the epidemic threshold level. However, because of the inadequacy of the most recent year's (2021) data, the previous five years' (2016–2020) weekly malaria case reports were utilized. Then epidemic threshold level was defined by comparing weekly data with similar weeks in 2022, and an epidemic curve was produced. A rise beyond the weekly threshold was recorded, indicating an outbreak. On September 12, 2022 (week 36), an early warning alarm was received from the Waghemra zone. The Zonal public health emergency management case team decided to investigate or confirm the outbreak and intervene after receiving a request from the zone health office and analyzing regular surveillance data. A number of malaria cases have been recorded; the slide positivity rate and attack rate were calculated as the number of confirmed malaria cases per 100 and 1000 population, respectively.

Sample size determination and sampling technique

The sample size was calculated using Epi-Info version 7.2.1 by taking an 80% power,, an odds ratio of 3.32 for the presence of artificial water holding bodies near the home, the percentage of exposed controls of 21.3% 8 , and the case-to-control ratio of 1:1. The total sample size was 118. Considering a design effect of 2 and 10% non-response rate, the final sample size became 260, with 130 cases and 130 controls .

A multi-stage random sampling method was used to enrol the study participants. Waghemra zone has eight districts, and of them, three (Ziquala, Sahala, and Abergelie) were purposefully selected. In each district, two kebeles were selected randomly using a lottery method. Accordingly, Tsitsika and Netsawork, Silazge and Meharit, and Saka and Debre-brihan kebeles were selected from Ziquala, Sahala, and Abergele districts, respectively. The total households for each village were available at their nearest health center or health post, which is stored as a family card folder. Based on this, the total sample size was proportionally allocated as 60, 43, 52, 33, 47, and 25 to Tsitsika, Netsawork, Silazge, Meharit, Saka, and Debre-brihan kebeles, respectively. All cases and controls were selected from the same community or neighbour for the controls at the same time. The lottery method was applied to select individual participants in the selected household.

Data collection

Six health extension workers and six laboratory technologists collected data using a structured questionnaire under the supervision of the principal investigator and the zonal public health emergency management case team. The questionnaire utilized in the study was prepared by reviewing the literatures 7 , 8 , 9 . Data collectors and supervisors received one day of training to ensure data quality. A review of weekly Integrated Disease Surveillance and Response (IDSR) reports at various levels (district health office and health facilities) was done. For adults, selected cases and controls were interviewed directly; for children, parents were involved in the interview process. But each participant gave blood for malaria diagnosis.

Laboratory methods

At Waghemra Zone health facilities, laboratory technologists utilized a light microscope to detect malaria parasites. During power outages, RDTs were used in healthcare facilities. Furthermore, at time of outbreak investigation, health extension workers and surveillance teams employed RDTs to identify confirmed malaria cases at health posts and the community level.

Environmental and vector control assessment

The environmental impact, as well as the ownership and use of ITNs were assessed. Selected case patients and controls were asked questions regarding the existence of mosquito breeding places in and around their compound. The potential breeding sites of Anopheles mosquitoes, such as uncovered plastic water containers, old tires, stagnant water, and broken glasses in the home or outside the home were evaluated. Furthermore, we assessed for the presence of anopheles’ larvae in stagnant water.

Data processing and analysis

Data were entered into Epi-Info 7.2.0.1 and analyzed using Statistical Package for Social Science version 26 (SPSS-26). The outbreak's scope was described in terms of person, place and time. The significance of risk factors for the outbreak was determined using logistic regression. Variables with p-value < 0.25 in bivariate analysis were entered in multiple logistic regression analysis to examine the effect of an independent variables on the outcome variable. The association between dependent and independent variables was determined using Odds Ratio (OR) of 95% Confidence Interval (CI) at p-value less than 0.05 was regarded as statistically significant.

Ethical consideration

Ethical clearance was obtained from the ethical review committee of College of Medicine and Health Sciences, Wollo University on the date 16/8/2022 with a protocol number of CMHS/201/2022. Supportive letters were also obtained from the Waghemra Zone Health Office. Written informed consent and assent were obtained from participants or caregivers. Positive cases were treated according to national malaria guidelines. The information obtained was made anonymous and de-identified prior to analysis to ensure confidentiality. The study was also conducted in accordance with the Helsinki Declaration.

Socio demographic characteristics

During the study period, 260 eligible study participants were selected and interviewed, making the response rate 100. The study included 155(59.6%) males and 105 (40.4%) females. The majority of the participants were between the ages of 15 and 45. In terms of occupation and education, 124 (47.7%) were farmers, while 227 (68.8%) were illiterate (Table 1 ).

Descriptive result

Description of cases by person and place.

During the outbreak investigation period from WHO weeks 29 to 47, a total of 13,136 confirmed cases of malaria from the Waghemra zone were detected. Total slide positivity rate (TPR) and attack rate (AR) were 43.0% and 26.5%, respectively. From all malaria confirmed cases, the most affected age group was > 15 years (65.6%), followed by 5–14 years (24.0%), and below 5 years (10.4%). The districts with the largest proportions of malaria-confirmed patients were Ziquala, Sahala, and Abergele, with 37.9%, 37.2%, and 10.2%, respectively. On the other hand, the highest attack rate was observed in the Sahala, Ziquala, and Abergele districts, with rates of 172.2, 113.2, and 28.9, respectively. Plasmodium falciparum responsible for 8681 (66.1%) of the infections, while P. vivax responsible for 3875 (29.5%) (Table 2 ).

Description of cases by time

The Waghemra Zone Health Department was informed that the number of malaria cases had exceeded the threshold in the WHO epidemiologic week 36/2022. The number of malaria patients steadily increased and peaked in week 42. Then it steadily decreased from week 43 to week 47 but was not controlled till this investigation was completed (Fig.  1 ). The intervention began with mass diagnosis using RDT and microscopy, and the positive cases were treated with artemisinin-based combination therapy and chloroquine for infection with P. falciparum and P. vivax , respectively. Health education, environmental management, distribution of ITN and the use of Abet chemicals to larvicide stagnant water were also applied.

Malaria outbreak line graph by WHO epidemiologic week in Waghemra zone, Northeast Ethiopia, 2022.

Analytic results

Factors associated with malaria outbreaks.

In a multivariable analysis, children under the age of five were five times more likely than those over the age of 45 to contract malaria (Adjusted Odds ratio (AOR) = 5.1; 95% Confidence Interval (CI) 2.6–23.0). People who were living in households where artificial water-holding bodies were thus 2.7 times more at risk of getting malaria infection than their counterparts (AOR: 2.7; 95% CI 1.340–5.420). Similarly, the presence of intermittent rivers closes to the community within 1 km distance increased the likelihood of getting malaria than those far away from it (AOR: 9.4; 95% CI 4.8–18.0). Likewise, children who stayed outside at night had an almost five-fold greater risk of acquiring malaria compared to those who did not (AOR = 4.9; 95% CI 2.51–9.62). Furthermore, higher odds of malaria were noted among those who had no knowledge on malaria transmission, prevention and control mechanisms (AOR: 9.7; 95% CI 4.459–20.930) (Table 3 ).

Public health interventions

Early diagnosis and treatment.

During the investigation period, an active case detection was conducted using RDT or microscopy, as well as early case management in accordance with national malaria treatment standards 9 . Temporary diagnosis and treatment sites were established to control and prevent further transmission through early treatment.

Environmental assessment

There were many mosquito breeding sites detected in the districts, which could be the source of the outbreak. In most of houses, unnecessary weeds, fake water-holding containers, especially damaged gutters, unused cans, unused old ties and stagnant waters were observed. Environmental management such as filling, draining, and clearing were carried out in an area larger than 432,157 square meters in a selected Anopheles mosquito breeding site. The community was involved in both the opening of temporarily stagnant water and the administration of larvicide (abet insecticide) at the breeding location. In this environmental management a total of 8,654 people were participated.

Vector control activities

The zone fast response team assessed and provided vector control activities in the study area. In all households in the Waghemira zone, indoor residual spray chemicals were not sprayed due to conflict in the last year. The fast response team, sprayed anti-larval chemical (abate) on stagnant water with an approximate area of 432,157 square meters. Fifty homes from each affected kebeles were randomly selected and visited to look for new malaria cases and assess the use of insecticide-treated bed nets at night. Even though every household had at least one insecticide-treated bed net, only 42.6% of them hung it directly on the bedding, with the rest hanging it underneath the beds and elsewhere in the house Moreover, about 22.6% of the household nets were damaged. The response teams then distributed over 3100 ITNs to the community.

Health education and communication

Health professionals were mobilized and assigned to the affected village for an active case search and early case management in the community. In addition, health education was given to 15,890 people about the cause, transmission, prevention, and control of malaria. Communicating and discussing the trend of the malaria situation with health facilities, Woreda, and zone health departments, and there was also multi-sectorial integration for social mobilization and prevention of malaria.

Based on five years of epidemiological records of malaria cases, the study findings showed the presence of a malaria outbreak in the study area. The malaria outbreak investigation included WHO weeks 36 to 47. Overall, the outbreak decreased but was not controlled due to inadequate environmental and vector control interventions in affected areas. For the past year, there has been an internal conflict in the study area, which has resulted in the deterioration of the health system and the interruption of malarial prevention measures, which have kept malaria under control.

The national malaria prevention and control strategies recommend the application of the IRS at least once a year with 100% coverage and at least one ITN per two people in high malaria-risk areas 10 . Despite this fact, prior to the outbreak, IRS was not applied, early replacement of ITN was not done, and there were multiple mosquito breeding sites. Households that had been using the ITN for purposes other than their intended purpose were also observed. This could be due to poor monitoring of the communities after distributing the ITN. The districts were also inadequately prepared for the outbreak, leading to a shortage of resources. This negatively affected outbreak control and resulted in the outbreak taking longer to contain. A similar finding was reported in Binga district, Zimbabwe 11 .

The overall attack rate (AR) was 26.5 cases per 1000 population; this finding was higher than a study done in Argoba district, South Wello Zone (AR: 1.8) 12 , Laelay Adyabo district, Northern Ethiopia (AR: 12.1) 13 , and India (AR: 15.1) 14 . However, this finding was lower than a study done in the Abergelle district, North Ethiopia (AR: 33.1) 15 , Simada district, Northwest Ethiopia (AR: 200) 8 , Afar region, Ethiopia (AR: 36.7) 16 , Bolosso Sore district, Southern Ethiopia (AR: 36.4) 17 , BenaTsemay district, Southern Ethiopia (AR: 114) 18 , and Kole district, Uganda (AR = 68) 19 . This difference might be attributed to prevention and control efforts, community level of awareness, internal conflict, and area differences in the burden of malaria and duration of the disease.

The AR was highest in Sahala, Ziquala, and Abergele districts, with rates of 172.2, 113.2, and 28.9 per 1,000 populations, respectively. This might be due to the presence of multiple mosquito breeding sites near residents of these districts compared to the other districts. Moreover, these districts are extremely hot and low-land areas with a high malaria burden. This was in line with a study done in the Metema district and in the Amhara Regional State, Ethiopia 20 , 21 . This could be due to high temperatures in the area, which are conducive to mosquito development rates, biting rates, and parasite survival within the mosquito 22 .

The greatest number of malaria cases was found in patients above the age of 15 (8621 out of 13,136). This finding was in line with studies from Abergele district Northeast Ethiopia 23 , Ankasha district, North Ethiopia 9 , and BenaTsemay district, Southern Ethiopia 18 . This might be due to the fact that the majority of the adolescents were spending more time outdoors in this area for farming, livestock-keeping, and fishing activities that exposed them to mosquito bites. This implies that the regional health bureau needs to give more focus and extend medical services to people who are engaged in farming, livestock keeping, and fishing.

The predominant Plasmodium species detected in this study was P. falciparum (66.1%), followed by P. vivax (29.5%). This was in agreement with other previous studies done in Argoba district, Northeast Ethiopia 12 , and Abergele district, Northern Ethiopia 15 . However, it disagreed with the national malaria parasite distribution pattern of Ethiopia, which showed that P. falciparum and P. vivax accounted for 60 and 40% of the malaria cases in the country, respectively 24 . This variation could be due to the fact that this study was limited to a small malaria-endemic setting in the country, which could have caused the species prevalence to vary. In addition, P. falciparum is a common species in the lowlands.

Malaria outbreaks are frequently complicated and multi-factorial, including both natural and man-made causes 25 . This case–control study verified the occurrence of a malaria outbreak in the Waghemra zone. Age, the availability of artificial water-holding bodies, nearby stagnant water, sleeping outside overnight, and a lack of knowledge about malaria transmission and prevention all contributed to the epidemic's existence. As a result, children under the age of five were nearly five times more likely than individuals over the age of 45 to contract malaria. This finding was congruent with research undertaken in the Bena Tsemay district of southern Ethiopia 18 . Malaria immunity develops slowly after multiple infections, and it takes at least five years for children to establish immunity 26 .

Furthermore, people who live near artificial water-holding bodies and stagnant water were more likely to be exposed to the malaria parasite than their counterparts. A similar conclusion was reached in research conducted in Simada district, Northwest Ethiopia, which found a link between staying near such water sources and contracting malaria 8 . Stagnant water created by heavy rains provides an ideal breeding environment for mosquitoes and contributes to malaria epidemics 8 , 16 . Similarly, people who stayed outside at night were approximately five times more likely to be infected with malaria than those who did not. This finding was supported by a report from the Ziquala, Armachiho, and Dembia districts of the Amhara region in Ethiopia 27 , 28 , 29 . This could be explained by the exophagic-exophilic biting behaviours of mosquitoes 30 . Moreover, a lack of knowledge regarding malaria transmission and control was a risk factor for disease development. Malaria education is crucial for minimizing exposure to the disease and its negative health consequences 8 , 31 , 32 .

During the investigation period, active case searching, treatment and management were carried out in accordance with national malaria treatment guidelines. Aside from that, environmental management activities such as filing, draining and clearing temporarily stagnant water were done with community involvement. At the time of data collection period, larvicide (abet chemical) was sprayed on Anopheles mosquito breeding sites. Moreover, the malaria surveillance team provided health education on disease transmission and prevention, and distributed over 3100 ITN to the community. However, due to a scarcity of chemicals, indoor residual spraying of houses in impacted kebeles is now being delayed. This outbreak scenario exemplified the critical role of long-term environmental and vector control intervention through well-organized malaria strategies and programs in preventing and controlling malaria infections. Malaria control and elimination require cross-sectoral collaboration as well as close monitoring and assessment of prevention and control initiatives.

Conclusion and recommendations

Following a year of internal conflict, a malaria outbreak was confirmed in Waghemra Zone. The predominant Plasmodium species identified was P. falciparum , and the outbreak was linked to being under five age, the existence of vector-breeding areas, people staying outdoors overnight, and a lack of knowledge about malaria transmission and control. The response to the outbreak included early diagnosis and treatment, environmental change, vector control, and awareness raising, which resulted in a reduction but not complete control of the outbreak. To prevent future malaria outbreaks in the study area, we recommended that the Waghemira Zone health office, Amhara regional health bureau, and other concerned sectors implement the following malaria prevention and control techniques: Those include raising community knowledge about malaria, mobilizing to disrupt mosquito breeding areas, scheduling indoor residual spraying activities, and monitoring malaria case trends on a weekly basis.

Ethical approval and consent to participate

Ethical clearance was obtained from the ethical review committee of College of Medicine and Health Sciences, Wollo University on the date 16/8/2022 with a protocol number of CMHS/201/2022. Permission was obtained from Waghemra Zone Health Office and each district health office where the study was conducted. This study was conducted in accordance with the Declaration of Helsinki. After briefly describing the significance of the study, the participants or children’s parents or guardians signed informed written consent. Confidentiality of the data was maintained. Finally, participants who were infected with the Plasmodium parasite received antimalarial treatment according to the national malaria treatment guidelines.

Data availability

All relevant data are included in the published article.

Abbreviations

Attack rate

Confidence interval

Indoor residual spray

Insecticide-treated nets

Plasmodium falciparum

Plasmodium vivax

Rapid diagnostic test

Statistical Package for Social Sciences

Total slide positivity rate

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Acknowledgements

The authors thank the study participants, data collectors, Waghemra Zone Health Office. The authors would like to also thank district health offices, kebele leaders, health extension workers, health facility administrative and medical laboratory staffs for their support and unreserved cooperation in making this study to be a fruitful work.

The research project was not funded by any organization.

Author information

Authors and affiliations.

Department of Medical Laboratory Sciences, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia

Habtu Debash, Habtye Bisetegn & Alemu Gedefie

Department of Internal Medicine, School of Medicine, Wollo University, Dessie, Ethiopia

Gebru Tesfaw

Department of Medical Laboratory Sciences, College of Health Sciences, Woldia University, Woldia, Ethiopia

Marye Nigatie

Department of Microbiology, Immunology and Parasitology, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia

Daniel Getacher Feleke

Waghemra Zone Health Department, Sekota, Ethiopia

Askale Amha

Department of Medical Laboratory Sciences, College of Medicine and Health Sciences, Bahirdar University, Bahirdar, Ethiopia

Megbaru Alemu Abate

The University of Queensland, School of Public Health, Brisbane, Australia

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Habtu Debash, Marye Nigatie, Habtye Bisetegn and Daniel Getacher Feleke conceived and designed the study, prepared the proposal, supervised data collection, analyzed, and interpreted the data. Habtu Debash, Gebru Tesfaw, Askale Amha, Megbaru Alemu, and Alemu Gedefie had participated in data collection, data analysis, and interpretation of the result, collecting scientific literature, critical appraisal of articles for inclusion, analysis, and interpretation of the findings. Habtu Debash drafted and prepared the manuscript for publication. Habtye Bisetegn, Marye Nigatie, Daniel Getacher Feleke and Alemu Gedefie critically reviewed the manuscript. All the authors have read and approved the final version of the manuscript and agreed to be accountable for all aspects of the work.

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Debash, H., Nigatie, M., Bisetegn, H. et al. Malaria surveillance, outbreak investigation, response and its determinant factors in Waghemra Zone, Northeast Ethiopia: unmatched case–control study. Sci Rep 13 , 9938 (2023). https://doi.org/10.1038/s41598-023-36918-3

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ICAP Quickly Responds to Malaria Outbreak in Ethiopia’s Newest Regional State

Feb 20, 2023 | News

On November 23, 2021, the South West Ethiopia Peoples’ Regional State was officially constituted as Ethiopia’s 11 th region. Just six months later, a malaria outbreak slammed the fledgling state following a bout of unseasonable rainfall, severely affecting nearly all of its 57  woredas —or districts—and making it the third largest malaria-reporting region in the country.

Malaria is one of the most fearsome threats to health in Ethiopia, where more than two-thirds of the population lives in high-risk areas and over 1.5 million cases are reported annually. South West Ethiopia, encompassing a population of 3.3 million, is particularly prone to malaria outbreaks, and represents over 15 percent of the country’s overall malaria case burden.

“South West Ethiopia is a newly constituted region characterized by weak physical infrastructure, recurrent and heavy floods, a fragile security situation, and poor public health awareness,” explained Megi Wechuro, who heads South West Ethiopia’s Public Health Institute. “These challenges, coupled with the existing weak health care system, have exacerbated the spread of malaria in the region.”

The new region’s government began to successfully address two of its other major public health challenges, malnutrition and louse-borne relapsing fever. The malaria problem, however, only worsened.

In May 2022, the South West Ethiopia Peoples’ Regional State government requested emergency help from ICAP, and it immediately responded. ICAP was already considered a trusted and reliable partner by health officials in Ethiopia, after nearly 14 years of actively working to improve malaria diagnosis across the country through the Malaria Diagnosis and Treatment Activity (MDTA). The MDTA is an initiative funded by the United States Agency for International Development (USAID)—via the U.S. President’s Malaria Initiative—and implemented in Ethiopia by ICAP under the leadership and guidance of Ethiopia’s Ministry of Health and the Ethiopian Public Health Institute.

Within a week, ICAP—in cooperation with the Ministry of Health—had deployed a clinical advisor to the region, along with a team of laboratory and monitoring and evaluation experts. The team began by assessing the malaria situation in Bench Sheko, West Omo, Keffa, and Dawro zones, an area that accounts for a third of the region’s population. Based on those findings, the team of experts provided emergency support to key hospitals, health centers, and community health posts that were struggling with a particularly high caseload of malaria patients. Within six weeks, malarial transmission rates had dropped by almost 75 percent.

“We trained and mentored hundreds of laboratory personnel working in the region in malarial microscopy and quality-assurance procedures,” explained Ayenachew Abebe, MD, ICAP’s team leader for the response. “We also mentored health extension workers and clinicians working in the health posts and health centers on how they can best apply rapid diagnostic tests for malaria diagnosis.”

In addition, the ICAP team provided on-the-job training to health practitioners and supplied health facilities with essential tools and materials such as fever case management and malaria treatment guidelines, malaria epidemic monitoring charts, malaria diagnosis registration books, and patient information documentation forms. Finally, it launched a Malaria Mass Test and Treat initiative in two districts that showed the highest burden of malaria cases.

Thanks to the quick response of ICAP and its partners, malaria cases were cut by almost half each week, and many lives were saved.

“We did the best we could to manage the situation and address at least the most critical and urgent of needs,” said Mekonnen Tadesse, MPH, MSc, ICAP’s deputy chief of party for MDTA.

“Our emergency response, which lasted for over six weeks in the region, has brought an enormous result in terms of reducing the rate of morbidity and mortality,” said Zenebe Melaku, MD, ICAP’s country director in Ethiopia. Going forward, “ICAP will work hard to expand its support to new locations that are susceptible to malaria transmission.”

Since 2008, ICAP has been working with Ethiopia’s Ministry of Health to improve the ability of Ethiopian health centers to detect, diagnose, and treat malaria, as part of the MDTA project, which aims to improve the quality of malaria diagnosis and case management services in Ethiopia.

A major global health organization that has been improving public health in countries around the world for two decades, ICAP works to transform the health of populations through innovation, science, and global collaboration. Based at Columbia Mailman School of Public Health, ICAP has projects in more than 40 countries, working side-by-side with ministries of health and local governmental, non-governmental, academic, and community partners to confront some of the world’s greatest health challenges. Through evidence-informed programs, meaningful research, tailored technical assistance, effective training and education programs, and rigorous surveillance to measure and evaluate the impact of public health interventions, ICAP aims to realize a global vision of healthy people, empowered communities, and thriving societies. Online at icap.columbia.edu

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Generating knowledge, catalyzing solutions

ICAP is at the forefront of research generating new knowledge to improve access to and quality of health services, strengthen delivery mechanisms, and evaluate public health interventions. Leveraging years of experience and a committed cadre of accomplished experts, ICAP designs, implements, monitors, and evaluates a range of innovative implementation science, epidemiological, and clinical research studies. To ensure sustainability of this pragmatic research, ICAP provides training and mentorship in the countries where it works.

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With a Clinic on Wheels, ICAP Moves Research on Opioids and HIV Into the Passing Lane

In summer 2021, a state-of-the-art mobile clinic began making rounds in the streets of Harlem and the Bronx, drawing attention with its bright graphics. But beneath the colorful exterior is a serious proposition – to address the intertwined public health crises of opioid addiction, HIV, and hepatitis C among people who inject drugs.

Drug overdose is the leading cause of accidental death in the United States, with nearly 108,000 fatalities in 2021, the highest number of overdose deaths recorded in any 12-month period. Factors such as lack of access to health care, poverty, mental health disorders, use of multiple illicit substances, stigma and discrimination combine to increase the risk of HIV transmission and acquisition and other health issues among people who inject drugs.

The mobile clinic is at the center of ICAP’s participation in the nationwide INTEGRA study (HPTN 094), which aims to determine whether using mobile health units to deliver integrated health services for people with opioid use disorder can improve addiction, HIV, hepatitis C and substance use outcomes compared to standard of care. At locations frequented by people who inject drugs, ICAP study team members engaged with individuals, provided them with information regarding the study, enrolled participants and followed up with them throughout their study participation.

Participants in the study are randomized to receive integrated care on the “van” – as the study team calls it – or to receive the services of a health care navigator who will assist the participant in finding care in the community.

“The integrated care model means they will be able to receive their buprenorphine [a medication to treat opioid use disorder] prescription from the van,” said Rashaunna Redd, NP, site clinician for ICAP’s Bronx Prevention Center, which conducts the study. “And they will also be tested for HIV, STIs, and hepatitis, and screened for routine primary care problems such as diabetes and blood pressure issues.”

After six months, all participants transition to care in the community. Follow-up after the study extends to 12 months.

“Our goal is to make it as close to one stop as a possible. Although we recognize that some people will have serious medical conditions that require them to see specialists – and we will help them with that,” said Ellen Morrison, MD, site lead at ICAP’s Bronx Prevention Center.

Since the study began, initial findings revealed a high prevalence of mental health disorders such as anxiety, depression, and post-traumatic stress disorder among participants.

“This finding is particularly important because recreational drug use may be used as a form of self-medication,” said Alan Padilla, BA, community educator at ICAP’s Bronx Prevention Center. “Our team is actively promoting the need to address these underlying factors to fully provide addiction services.”

As the van proclaims in bright lettering, ICAP is driving health forward . Mobile health units, along with this study, are providing the engine necessary to reach that mission.

Funder: U.S. National Institute of Allergy and Infectious Diseases (NIAID) with funding from the U.S. National Institute on Drug Abuse (NIDA)

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Charting LGBTQ+ Health Challenges in New York City During COVID-19

As COVID-19 swept the globe, its ruthless trajectory exacerbated the challenges and inequities already faced by the lesbian, gay, bisexual, transgender, and queer (LGBTQ+) community, including employment and housing discrimination, inequitable health care, and more. To gain insight into the burden and impact of COVID-19 on this community, and assess vaccine uptake, ICAP conducted a study that reached more than 1,000 LGBTQ+ New Yorkers aged 18 to 68 years.

While LGBTQ+ individuals in NYC reported a similar burden of COVID-19 and vaccine uptake compared to the general population of the city, the study revealed this community is more likely to experience increased financial and emotional challenges due to the pandemic, particularly among the most stigmatized, such as gender minorities and among those with multiple minority identities. For example, 81 percent of LGBTQ+ individuals reported experiencing financial hardship as a result of the pandemic. This important evidence base will inform strategies to reach unvaccinated individuals and assist policymakers in developing further programs to support those most negatively impacted by the pandemic.

Funder: Rockefeller Foundation

Project: Experiences of LGBTQ+ Populations in New York City During the COVID-19 Pandemic (The LEXICON Study)

COVID-19 and Older Adults in New York City: A Landmark Study

In New York City, older adults had seven times the mortality rate from COVID-19 compared to all other ages, but there was little known about the mental health and social ramifications of the pandemic on this population – especially those who were still living at home and not in nursing homes.

To gain a better understanding of the effects of the pandemic on this vulnerable group, ICAP launched the SARS-CoV-2 Impact on Lives and Views of Elderly Residents (SILVER) study, aimed at understanding the physical, emotional, and economic effects of the COVID-19 pandemic on older adults living at home. A total of 676 participants 70 years and older were enrolled – overall, 18 percent of older adults screened for depression and 17 percent for anxiety, with a greater percent of Latinx older adults reporting loneliness than other races and ethnicities. Almost one-third of older New Yorkers reported financial challenges and almost one in ten reported not having enough to eat.

With new funding, ICAP launched a second SILVER study seeking to learn more about the impact of the pandemic on participants’ ongoing health and wellbeing. The second round of data collection expanded topic areas, pursuing further details about participants’ access to resources such as telehealth, housing, internet, social support, and use of city services. Attitudes toward the COVID-19 vaccine, booster doses, and the influenza vaccine were also evaluated. In addition to following up with the first SILVER study participants, the second study included new participants, specifically Asian New Yorkers, to better represent the diversity of New York City. The ultimate goal of the study was to provide policymakers in New York City and other communities with more accurate information on how to best serve and assist older adults during times of crisis.

Funder: New York Community Trust

Project: SARS-CoV-2 Impact on Lives and Views of Elderly Residents (SILVER) Study

• Research article
• Open access
• Published: 02 May 2019

Risk factors associated with malaria outbreak in Laelay Adyabo district northern Ethiopia, 2017: case-control study design

• Afewerki Tesfahunegn   ORCID: orcid.org/0000-0002-1325-5796 1 ,
• Gebretsadik Berhe 1 &
• Equbay Gebregziabher 2  

BMC Public Health volume  19 , Article number:  484 ( 2019 ) Cite this article

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Globally in 2015 about 214 million malaria cases and 438,000 deaths were reported with 75% were from Sub-Saharan Africa. Malaria transmission in Ethiopia is unstable, and outbreaks are considered public health emergencies. Understanding the trigger for outbreaks in low-transmission areas can help facilitate malaria elimination. On July 8th malaria outbreak was reported from Laelay Adyabo district. The objective was to investigate the magnitude and associated factors with malaria outbreak.

We defined a case as confirmed malaria using microscopy or a rapid diagnostic test for Plasmodium parasites in a resident of Laelay-Adyabo District from July 9–28, 2017. We identified cases by reviewing health facility records and conducted a case-control study using randomly-selected cases from a line list, and two neighborhood controls per case. A pretested semi-structured questionnaire adapted from WHO malaria guidelines was used to collect data from case-patients and controls. We calculated crude (COR) and adjusted (AOR) odds ratios to identify factors associated with malaria.

A total of 145 confirmed malaria cases (57.9% males) were identified with village attack rate (AR) of 12.1/1000. The AR was higher among males than females (14.1 verses 10.1/1000), children aged 5–14 years (12.9/1000), and in Zelazle Kebelle (13.6/1000 population). Wearing protective clothing (AOR = 0.27, 95% CI 0.11–0.66), having good knowledge of malaria transmission (AOR = 0.25, 95% CI 0.08–0.75), having waste collection material at home (AOR = 0.25 95% CI 0.11–0.61), availability of mosquito breeding sites around home (AOR = 9.08, 95% CI 3.6–22.93), and staying outdoor overnight (AOR = 3.7, 95% CI 1.44–9.56) were independently associated with malaria.

The overall attack rate for malaria during this outbreak was high affecting > 1% of the population. Wearing protective clothing at night, knowing about malaria transmission, having mosquito breeding sites around the home, staying outdoors overnight, and having waste collection material in their house were predictors of the infection. Laelay Adyabo district health office should provide health education on malaria transmission and prevention measures and how to clear mosquito breeding sites.

Peer Review reports

Malaria is a parasitic infection caused by plasmodium species. Globally about 214 million malaria cases and 438,000 deaths occurred during 2015. Approximately 75% of cases and deaths were from Sub-Saharan Africa; Where approximately 60% of the population at risk [ 1 , 2 ].

In Ethiopia, malaria transmission is unstable; however, approximately 68% of the populations still live in malaria-endemic areas. Ethiopia is aggressively working towards malaria elimination by 2030 [ 3 , 4 , 5 , 6 , 7 ]. Known risk factors for malaria include low utilization of Insecticidal Treated bed Nets (ITNs), low utilization of Indoor Residual Spray (IRS), availability of multiple mosquito breeding sites or stagnant water sites near the home, and staying outdoors overnight [ 8 , 9 , 10 , 11 ]. Malaria outbreaks can occur among persons living near irrigation sites and dam areas, persons who keep livestock, live in near river, staying inside home at night, sleeping under ITNs, and spraying of IRS are protective [ 12 , 13 , 14 , 15 ].

Ethiopian ministry of health on 2015 targets to maintain zero malaria deaths and reduce malaria cases and admission by 67 and 48% respectively [ 16 , 17 ]. Ethiopia applied interventions like early diagnosis and treatment, and use of vector control methods (indoor residual spray with insecticide and ITNs) to control malaria in the last 25 years. These interventions are highly effective in reducing both the transmission and exposure to infectious mosquito bites and also the concomitant burden of malaria disease. However, ITN ownership and usage levels are still both below target levels. According to 2015 malaria indicator survey, about 64% of households in malaria-endemic areas owned at least one ITN [ 18 , 19 , 20 ].

Laelay Adyabo district received report of unexpectedly high number of malaria cases occurring since July 8th, 2017. This was unusual, as no malaria outbreak had happened in the last 10 years in the district [ 7 ]. Therefore we conducted a study.

Understanding the reasons for outbreak occurrence in low transmission areas enable to provide early case management, identify factors that maintain the disease, and design more effective prevention and control measures to facilitate malaria elimination strategy by 2030. Therefore, the objective of this study was to investigate the magnitude and risk factors associated with the malaria outbreak in this low-transmission area.

Study setting and period

This study was conducted in Laelay Adyabo district northwestern Tigray region, Ethiopia on July 9–28, 2017. The district has five health centers and 17 health posts. The district was 1924 m above sea level and they had activities like IRS, ITNs distribution to the community in the last 20 years [ 7 ]. The outbreak happened in two of the district Kebelles (Tsehayo and Zelazle Kebelle (total population: 12,000).

Study design

We did descriptive analysis and conducted a case-control study to identify risk factors associated with malaria.

Study participant and variables

Confirmed cases were defined as malaria confirmed by microscopy or Rapid Diagnostic Test (RDT) in an individual currently living in the study area. Suspected cases included any person with fever or fever with headache, back pain, chills, rigor, sweating, muscle pain, nausea and vomiting diagnosed clinically as malaria. Controls were healthy neighbors of cases who did not have malaria by RDT during the outbreak period.

We used a standardized questionnaire from literatures to collect data from cases and controls. The questionnaire was administered by trained interviewers to collect data on patient age, sex, residence, family size, insecticide-treated bed net (ITNs) usage, history of indoor residual spraying (IRS) at home, river within 2 km, overnight sleeping location (outdoor or indoor), presence of mosquito breeding sites at home, and knowledge of respondents about malaria transmission (Additional file 1 ). We also abstracted data from clinical patient records.

Sample size and sampling technique

Sample size was calculated by assuming an odds ratio of 0.3 for ‘sleeping under ITNs, control exposure of 87.4%, and case to control ratio of 1:2 for a total sample size of 170. With a 10% non-response rate, our sample size was 186, with 62 cases and 124 controls [ 11 ]. We selected cases randomly from our line list, and identified two neighborhood controls per case.

Data processing and analysis

Data were entered to EPI INFO 7.0 after cleaning and coding. Data were analyzed using SPSS 20.0. Descriptive analyses were conducted frequency with percentage for categorical variables and median and Inter Quartile Range (IQR) were computed for continues variables and normality test was checked graphically. Attack rate were computed based on age, sex, and residence of patients. Both bivariate and multivariable logistic regression was performed to identify factors associated with malaria outbreak.

We checked for multi-Collinearity using Variance Inflation Factor (VIF); values < 10 were included in the model. The model was fitted by Hosmer and Lemeshow’s goodness-of-fit. Data were presented using adjusted odds ratio (AOR) with the 95% confidence interval.

Operational definition

Malaria outbreak: increment of malaria cases in a specific week comparing to the doubling last year of the same weeks.

Wearing Protective cloth: peoples who wore long cloth to protect their leg and hand during night.

Waste collection material: peoples who have waste collection material in around their house.

Good knowledge: peoples who scored above mean of knowledge questions otherwise poor.

Mosquito breeding site: peoples who have stagnant water, availability of mosquito breeding material, and availability of dungs and tick grass in their dwelling house.

Outdoor overnight: peoples who stay outdoor more than 6 h during the night time.

Ethical consideration

Ethical clearance was obtained from Mekelle University College of Health Science institutional review board. Written informed consent and assent were obtained from participant/caregivers.

Descriptive result

A total of 145 confirmed cases of malaria were identified over the outbreak period during WHO weeks 27 to 30 from Tsehayo and Zelazle Kebelle (Fig. 1 ). The village Attack Rate (AR) during the outbreak was 12.1 cases per 1000.

Malaria outbreak line graph in Laelay Adyabo district northern Ethiopia, 2017

Among the 145 malaria patients, 84 (57.9%) were male. Sex-specific attack rates were 14.1 (males) and 10.1 (females) per 1000 population. Median age of patients was 16 years (IQR: 8–25 years) and approximately half were in the age group of 15–59 years (Table 1 ).

The date of onset for most of the cases 23 (15.9%) were on July 15, 2017 and the epidemic curve demonstrated a propagated outbreak (Fig. 2 ).

Malaria outbreak Epi curve in Laelay Adyabo district northern Ethiopia, 2017

Eighty-nine (61.4%) patients were from Zelazle Kebelle (AR = 13.6/1000) and the rest from Tsehayo (AR = 10.3/1000) (Fig. 3 ). Of the total, 126 (86.9%) infections were caused by Plasmodium falciparum and the rest by Plasmodium vivax .

Malaria outbreak affected Kebelles spot map in Laelay Adyabo district northern Ethiopia, 2017

Analytic results

Socio demographic characteristics.

In the case-control study, a total of 60 cases and 120 controls were interviewed with a response rate of 96.8%. Cases and controls differed by sex (71.7% vs 49.2% male, p  = 0.005) and age (68.3% vs 96.6% aged > 15 years, p  = 0.000 (Table 2 ). The most common symptoms among cases-patients were fever (93.3%), headache (73.3%), and anorexia (70.0%).

Fourteen (23.3%) case-patients were treated within 24 h of onset while the rest were treated at least 1 day after onset. Forty-eight (80%) case-patients were infected with Plasmodium falciparum and 12 (20%) by Plasmodium vivax and they were treated by coartem and chloroquine, respectively.

Risk factors for malaria

Multiple factors were associated with illness in bivariate analysis. Variables with p  < 0.2 were entered into multivariable analyses (Table 3 ). In multivariable analysis, cases had 73% lower odds (AOR = 0.27, 95% CI 0.11–0.66) of using protective clothing during the night compared with controls. Cases had 75% lower odds (AOR = 0.25 95% CI 0.11–0.61) of having waste collection material in their house than controls, and 75% lower odds (AOR = 0.25 95% CI 0.08–0.75) of knowing the mode of malaria transmission than controls. Cases also had nine-fold higher odds (AOR = 9.08 95% CI 3.6–22.93) of having mosquito breeding sites around their homes than controls, and nearly four times higher odds (AOR = 3.7 95% CI 1.44–9.56) of staying outdoors overnight than controls (Table 3 ).

Environmental observations

In all observed households, we noted unnecessary weeds around the house, artificial water holding containers, especially broken gutters, unused cans, unused old ties, and availability of un-cleaned dungs. Large traditional gold mining site was available separated from the community. There were also stagnant waters especially at Zelazle Kebelle .

Public health intervention

The affected Kebelles were assessed by health center rapid response teams and district public health emergency management focal personnel, and reviewed for risk factors. Most of the mosquito breeding sites were removed and indoor residual spray of Bendiocarb chemical was sprayed in all household in both Kebelles . Health education was given to the community of the district on comprehensive knowledge of malaria transmission and prevention modalities. Temporary sites for diagnosis and treatment were opened to interrupt further transmission by early treatment.

Malaria outbreak was investigated in Laelay Adyabo district northern Ethiopia. The overall attack rate was 12.1 cases per 1000 population, this finding was lower than a study done in Asgede Tsimbla district, Ethiopia (attack rate: 22.3) and India (attack rate: 15.1) [ 14 , 21 ] but greater than a study done in Pakistan (attack rate: 0.9) [ 22 ]. The low attack rate in this study might be attributed to area difference in the burden of malaria and duration of the illness. This area is known as low transmission area for malaria and the duration of illness was short compared to other studies [ 7 , 23 , 24 ].

Median age of malaria patients were 16 years, which is in line with outbreak investigation done in Ankesha and Asgede Tsimbla districts, Ethiopia [ 10 , 14 ], but younger than the study done in Guna Yala, Panama where the median age was 25 years [ 25 ]. This might be due to majority of the adolescents were spending more time outdoors in this area for agricultural and livestock keeping activities [ 6 ].

Most of the patients (72%) were males, this is also similar with the studies done in Ethiopia (Ziway and Amhara region pilot study), but higher than a study done in India (50.6%) and Ethiopia (Asgede Tsimbla (58%) and Bunga (69%)) [ 6 , 13 , 14 , 23 ]. This could be due to the reason that males were more likely engaged in agricultural, livestock keeping and traditional gold mining activities than females in our study area than the other studies. Most of male adolescent population in those Kebelles engaged on traditional gold mining, most of the summer period stays outdoor overnight and also during the study period there was interruption of rain fall. This implies that the regional health bureau needs to give more focus and extend medical services to people who are engaged in agriculture, livestock keeping and traditional gold mining.

In this study most of the cases were caused by Plasmodium falciparum (86.9%). This is similar with the previous year report of the district and by Tigray regional health bureau report. This result also similar with the study done in Asgede Tsimbla and Setit Humera, Ethiopia [ 3 , 7 , 13 , 14 ]. As the species of Plasmodium were identified by microscope and rapid diagnostic test the high amount of Plasmodium falciparum could be due to the areas which are in the same region.

Peoples who wore protective cloth at night, knowing about malaria mode of transmission, and availability of waste collection material in their house were less likely to be diseased than those who didn’t wore protective cloth at night, less knowledgeable on mode of transmission, and have waste collection material in their houses. This is consistent with the study done in Zimbabwe [ 11 ]. But it is different from the studies conducted in Ameya and Asgede Tsimbla districts of Ethiopia [ 9 , 14 ]. This may be due to the difference in study period and malaria intervention measures of the study areas which is in our area the health personnel’s were educated the community about malaria mode of transmission and prevention modalities [ 3 , 4 , 7 , 9 , 13 , 14 ]. These findings indicate that comprehensive awareness creation measures on prevention and transmission modalities of malaria are required to mitigate the outbreak effectively.

Those who had mosquito breeding site around their houses were more likely to be diseased by malaria than those who didn’t have mosquito breeding site. This is similar with study done in Ankesha, Ameya, Setit Humera, and Asgede Tsimbla districts of Ethiopia [ 9 , 10 , 13 , 14 ]. This could be due to the presence of mosquito breeding site that created suitable condition for reproduction of mosquito and infecting more people since most of the populations stayed out door during the night. The other reason might be due to availability of more stagnant waters and other suitable factors for mosquito breeding sites due to an interruption of rainfall during the study period. Also it could be due to the fact that most of the populations were engaged in outdoor activities especially during the study period. To control malaria outbreak, mosquito breeding sites need to be cleared by involvement of the local community.

In the study those who developed malaria were three times staying out door overnight than those who didn’t develop malaria. This is consistent with the study done in India, Setit Humera, and Asgede Tsimbla districts of Ethiopia [ 13 , 14 , 15 ]. This may be due to the fact that mosquitos were abundant during night and most of the peoples were not using any protective modalities during night and less use of ITNs outside their home. This indicated that the local health office should educate people staying out-door to use protective modalities.

Limitations of the study include that health facilities registration book didn’t record all relevant variables and that there were no malaria data during the past 5 years which was the best to set baseline levels of malaria cases; thus, the assessment of an outbreak was done based on doubling the previous year in similar weeks.

Conclusions and recommendation

The overall attack rate for malaria during this outbreak was high affecting > 1% of the population. This malaria outbreak was mostly affected for those male populations and residence in Zelazle Kebelle . Even though there are efforts to eliminate malaria in low transmission areas, the factors responsible to maintain malaria were not well controlled. Factors including presence of mosquito breeding sites around the home, staying outdoor overnight, wearing protective close during night, good knowledge on malaria mode of transmission, and presence of waste collection material at home are predictors of the infection.

Therefore, further creation of awareness to the community on malaria mode of transmission and prevention modalities and empowerment of community on their environmental cleanness may mitigate malaria outbreak. The local health workers and decision makers better to keep clean environment by empowering the community themselves and also better to create prevention modalities like preventive cloths and another insecticidal repellents to those outdoor at the night population and to people of special need.

Abbreviations

Adjusted Odds Ratio

Attack Rate

Confidence Interval

Crude Odds Ratio

Indoor Residual Spray

Insecticidal Treated Net

Rapid Diagnostic Test

Variance Inflation Factor

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Acknowledgements

We would like to thank you to Tigray regional health bureau and Ethiopian federal ministry of health. We have grateful thanks to Mrs. Alefech Addisu and Dr. Julie Harrison for unreserved advice during the investigation. Also we have a grateful thanks to Laelay Adyabo health office staffs and Tsehayo health center staffs. Lastly we would like to thank you to our data collectors and participants of the study.

Ethical approval and consent to participate

Ethical approval was obtained from Mekelle University College of Health Science. All participants were informed of the study’s objectives and confidentiality was assurance by anonymity and has signed an informed consent form.

The study was funded by Tigray regional health bureau for the activities of data collection and data analysis.

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Conception, design, acquisition, analysis, interpretation of data, drafted manuscript was done by AT; design, interpretation, revised the manuscript by GB; and supervise data collection and revise manuscript by EG. All authors read and approved the final manuscript. (AT-Afewerki Tesfahunegn, GB-Gebretsadik Berhe, EG-Equbay Gebrezgabher)

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Additional file 1:.

Questionnaire for risk factors of malaria outbreak in Laelay Adyabo district Northern Ethiopia, 2017. (DOCX 18 kb)

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Tesfahunegn, A., Berhe, G. & Gebregziabher, E. Risk factors associated with malaria outbreak in Laelay Adyabo district northern Ethiopia, 2017: case-control study design. BMC Public Health 19 , 484 (2019). https://doi.org/10.1186/s12889-019-6798-x

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Malaria outbreak investigation in a rural area south of Zimbabwe: a case–control study

Paddington t. mundagowa.

1 The Clinical Research Center, Africa University, 132 H. Chitepo Street, Mutare, Zimbabwe

Pugie T. Chimberengwa

2 Ministry of Health and Child Care, P.O Box 441, Bulawayo, Matabeleland North Province Zimbabwe

Associated Data

The datasets supporting the conclusions of this article are included within the article (and its additional files)

Ninety percent of the global annual malaria mortality cases emanate from the African region. About 80–90% of malaria transmissions in sub-Saharan Africa occur indoors during the night. In Zimbabwe, 79% of the population are at risk of contracting the disease. Although the country has made significant progress towards malaria elimination, isolated seasonal outbreaks persistently resurface. In 2017, Beitbridge District was experiencing a second malaria outbreak within 12 months prompting the need for investigating the outbreak.

An unmatched 1:1 case–control study was conducted to establish the risk factors associated with contracting malaria in Ward 6 of Beitbridge District from week 36 to week 44 of 2017. The sample size constituted of 75 randomly selected cases and 75 purposively selected controls. Data were collected using an interviewer-administered questionnaire and Epi Info version 7.2.1.0 was used to conduct descriptive, bivariate and multivariate analyses of the factors associated with contracting malaria.

Fifty-two percent of the cases were females and the mean age of cases was 29 ± 13 years. Cases were diagnosed using rapid diagnostic tests. Sleeping in a house with open eaves (OR: 2.97; 95% CI 1.44–6.16; p < 0.01), spending the evenings outdoors (OR: 2.24; 95% CI 1.04–4.85; p = 0.037) and sleeping in a poorly constructed house (OR: 4.33; 95% CI 1.97–9.51; p < 0.01) were significantly associated with contracting malaria while closing eaves was protective (OR: 0.45; 95% CI 0.20–1.02; p = 0.055). After using backward stepwise logistic regression, sleeping in a poorly constructed house was associated with five-fold odds of getting sick from malaria (AOR: 8.40; 95% CI 1.69–41.66; p = 0.009). Those who had mosquito nets did not use them consistently. The district health team and the rural health centre were well prepared to response despite having limited human resources.

Health promotion messages should emphasize the importance of closing the entry points of the malaria vector, and the construction of better houses in the future. Residents had to be educated in the importance of consistent use of mosquito nets. The district had to improve malaria preventive measures like distribution of mosquito nets and lobby for more human resources to assist with malaria surveillance thus, curbing the recurrence of malaria outbreaks.

Nearly half the global population is exposed to malaria [ 1 ] and in 2018, an estimated 228 million malaria cases occurred globally (95% Confidence interval (CI) 206–258 million) [ 2 ], up from 212 million in 2015 [ 3 ]. 405,000 malaria deaths were recorded in 2018 with the sub-Saharan region and India carrying 85% of the global malaria burden [ 2 ]. Malaria disease burden in Africa is very high and 90% of the global annual malaria mortality cases come from the African region [ 3 ]. In sub-Saharan Africa, 80–90% of malaria transmissions primarily occurs indoors during the night [ 4 ].

In Zimbabwe, malaria is a major public health problem affecting all age-groups and according to the World Health Organization (WHO) malaria report of 2016, 79% of the population are at risk of contracting the disease. Malaria incidence in the country was 139 per 1000 population at risk in 2013 [ 5 ]. The Ministry of Health and Child Care reported a surge in the malaria case fatality rate from 6.1% in 2012 to 13.8% in 2014 [ 6 ] and the malaria mortality cases were being reported throughout the year although transmission of the disease usually happens in seasonal epidemics largely occurring during the summer months of November to April [ 7 ].

The Zimbabwe National Malaria Control Programme and Roll Back Malaria Programme have achieved major successes in combating the diseases over the last 13 years, and this led to the reorientation of focus from malaria control to elimination [ 8 ]. These two programmes whose leadership is centrally stationed in the capital city, Harare, are informed by provincial and disease control officers who gets information from the district health teams which oversees malaria case management, surveillance and outbreak response at a local level [ 9 ]. The progress in malaria elimination was tenaciously challenged by vector and parasite resistance to insecticides and anti-malarial medicines, outdoor malaria transmission, dynamic vector behaviour and invasion of new areas by vectors. Climate change, erratic funding, local economic and political disturbances as well as increased cross border population movements have also contributed to the regression of malaria control programmes [ 8 ]. These factors have contributed to sporadic malaria outbreaks throughout Zimbabwe.

In 2017, the Beitbridge District was experiencing a second malaria outbreak in a space of less than 12 months. The first malaria outbreak in the district started in late 2016 and was declared over in week 22 of 2017. This earlier malaria outbreak recorded over 800 cases resulted in five fatalities, three children and two adults [ 10 ]. The recent malaria outbreak in Beitbridge District had resulted in one death in Beitbridge town during week 42 of 2017 [ 11 ]. Situated in Ward 6 of Beitbridge District, the Mtetengwe Clinic started to record a rapid increase in malaria cases during week 36, 2017 and subsequent weeks. Malaria threshold and alert levels for the clinic could not be calculated because the health facility was established in 2016, however malaria threshold levels for Makakabule Clinic which is in Ward 7 were used. Makakabule Clinic used to serve most of the population in Ward 6 before Mtetengwe Clinic was constructed.

This study aimed to investigate the malaria outbreak and determine the factors associated with contracting malaria in Ward 6 of Beitbridge. Understanding the risk factors associated with the occurrence of a malaria outbreak enables early and effective initiation of prevention interventions and control measures towards elimination of the disease in the affected area. The study also sought to investigate the malaria outbreak preparedness and response in the district. District health teams are responsible for detecting and responding to outbreaks at a local level. The assessment of the measures taken by the district health team to respond to a rise in malaria cases disease was imperative since the district was experiencing a second malaria outbreak; thus, this assessment could identify gaps in the malaria response system processes and make recommendations directed towards efficient and effective local disease outbreak response.

An unmatched 1:1 case–control study was conducted in October 2017. This study employed the stages used in the investigation of an outbreak guidelines recommended by the Centre for Disease Control and Prevention (CDC) [ 12 ]. This is a guide to real time stepwise investigation of acute public health events at a local, national, or multinational level. The systematic approach was used in preparing for field work after establishing the existence of an outbreak, verifying a diagnosis to finding and recording cases before epidemiologically evaluating the study hypotheses. The results from the investigation were communicated to the authorities were they were used to implement control and preventive measures.

Study setting

Beitbridge District is located 583 km south of the Zimbabwean capital, Harare, bordering Zimbabwe and South Africa. Mtetengwe Clinic is a rural health centre under the administration of Beitbridge Rural District Council, Matabeleland South Province, Zimbabwe. The clinic was opened in 2016 and it provides health services to Ward 6 population estimated at 3548. Mtetengwe Clinic covers villages like Mtetengwe, Mzingwane, Mapani, Tshinabazwimi, Malala and Bishopstone, BK Cawood, Beitbridge Juicing farming estates among others. Villagers and farm workers in this area live in house made of mud and wood or bricks roofed using thatch, asbestos or corrugated sheets.

The Ward 6 of Beitbridge District is primarily rural and it is located in an area of semi-arid land with erratic rainfalls and very hot climate. Due to poor annual rainfalls, the population depends on buying and selling of commodities purchased from South Africa. Furthermore, the district is also known for its thriving goat, cattle and game ranching activities. Beitbridge District offices are in Beitbridge town 22 km east of Ward 6. According to the Matabeleland South Generic Report of June 2017, the district had an estimated population of 128,454.

Entomological surveys carried out in Beitbridge District identified the main malaria vector in the area as Anopheles gambiae sensu lato ( s.l.) and Anopheles arabiensis. However, the surveys also noted Anopheles quadriannulatus as a potential vector. This prompted the district to schedule indoor residual spraying (IRS) between the month of October and December every year depending on the availability of resources.

Study population

The study population consisted of all adults and children residing in Ward 6 of Beitbridge District between week 36 and week 44 of 2017. The healthcare workers in the district were also included as key informants in the study. A case was a patient who resided within Ward 6, of Beitbridge District who presented with signs and symptoms of malaria and tested positive on rapid diagnostic test (RDT) for malaria from week 36 to week 44 of 2017. Symptoms of malaria referred to one or a combination of fever, vomiting, headache, general body malaise and rigors/chills. Controls who served as the comparison group were individuals who resided with or near a case and did not contract malaria during the period under study.

Using Stat-Cal embedded in Epi Info version 7.2.1.0 (CDC, USA) and assuming that living within a 3 km radius of a river or swamp was a significant risk factor for contracting malaria with an odds ratio (OR) of 2.7 and 43% of controls having been exposed [ 13 ], using a power of 80% and a 95% confidence interval (CI) gave the minimum required sample size of 66 cases and 66 controls. With expected 20% attrition rate the sample size was approximately equal to 83 cases and 83 controls.

The Mtetengwe Clinic line list which was used as the sampling frame for this study had 109 malaria cases and Ward 6 were purposively selected as study site in this investigation. Simple random sampling of cases was done by allocating numbers to all the 109 cases on the Mtetengwe Clinic line list and putting the numbered cards in a hat. After mixing, cards with numbers were blindly picked by a nurse from the clinic without replacement until the calculated sample size was reached. Controls were purposively selected from individuals residing with or within the neighbourhood of cases. To ensure that the control sample was representative enough, they were age-matched with cases.

For the assessment of the health workers used as key informants in the malaria outbreak response and preparedness in the district, maximum variation sampling was used. Different health workers with different roles in malaria epidemic investigation were interviewed at the district, facility and community level were utilized and this introduced a sample high heterogeneity while capturing maximum diversity of experiences [ 14 ]. The sample of key informants included purposively recruited district medical officer, district nursing officer, laboratory scientist, environmental health technician, and district pharmacist at the district level. The two nurses were interviewed at the facility level and three VHW were interviewed at the community level. All the key informants consented to participate for a 30 to 40 min interview.

Data collection and analysis

Data collection was conducted over a period of 2 weeks using structured and pretested interviewer-administered questionnaires (Additional file 1 ). These were translated from English to the local language Shona and back translated to English to ensure comprehension of the questions. Treatment records, clinic registers as well as healthcare workers were part of the study population. The participant questionnaire aimed to determine the demographic characteristics, the knowledge levels on transmission of malaria and practices used for protection against the disease. Checklists were also used to assess for environmental health risk factors and the availability of resources essential to mount an outbreak response. Data collection tools were pretested using ten participants from a village in Gwanda District. The researchers carried out the malaria epidemic investigation from the beginning of week 44 to the end of week 46 of 2017. Local VHWs were used to assist in locating selected cases using the clinic line list during community visits which were conducted during week days from 8am to 4 pm. Appointments for interviewing key informants were made in advance to minimize inconveniences.

Data from hand-written copies were transferred into Microsoft Excel (Additional file 2 ) before it was exported into Epi Info version 7.2.1.0 (CDC, USA) for data cleaning, coding and analysis. The statistical software was used to calculate frequencies, means, proportions, odds ratios and p-values at 95% CI. Backward stepwise multivariate logistic regression analysis was done to control for any confounding variables.

Permission to conduct the study was sought from Provincial Medical Director of the Matabeleland South Province, and District Medical Officer Beitbridge District. Ethical review and approval were granted by the Africa University Research Ethics Committee and the approval number was 266/17. Written informed consent was obtained from the study participants before data was collected. Participation in this study was voluntary.

Operational definitions

Well-constructed house a house made from bricks walls and a roof made from asbestos or corrugated roof [ 15 ].

Poorly constructed house a house made using mud and pole while the roof was either thatched with grass or non-thatched, that is made of asbestos or corrugated sheets. A house made of bricks and thatched with grass was classified under poorly constructed houses.

Eaves gaps between the top of the wall and the overhanging roof [ 15 ].

Evening outdoor activities habitually spending significant evening time outside the house from sunset to about 10 pm before going to bed.

Description of the malaria outbreak by person

A total of 109 cases of malaria were reported from week 36 to week 44 of 2017 and no deaths occurred in the study area during this period. Four of the sampled cases could not be located since they had travelled outside Ward 6 and four refused to be interviewed thus, a total of 75 cases and 75 controls were interviewed of which 52% (n = 39) of the cases were females and the mean age of the malaria cases was 29 ± 13 years while most (35%) cases were in the age-group 20–9 years.

Study participants who had long lasting insecticide-treated nets (LLINs) (n = 85) reported that the LLINs were donated in 2015 and researchers noted that 45% of these had since developed holes which were potential entry points for the malaria vector. Of those who had LLINs, 26% (n = 22) did not use them because the treated nets were perceived to cause suffocation (n = 11), itchiness (n = 4), or individuals were not interested in using the nets (n = 7).

Although the community knowledge of malaria transmission was not significantly associated with contracting malaria, the majority (97%) managed to recall the use of LLINs while 35% mentioned IRS as a way of protecting self from malaria. 83% of participants agreed that one can protect self from getting malaria and all the participants reported that malaria disease is curable and they would visit the clinic upon suspecting any signs and symptoms of malaria. 93% of participants correctly identified that malaria is more common during the hot and wet season. 12% of the cases had travelled outside Ward 6 approximately 4 weeks before they were diagnosed of malaria while none of the controls had travelled outside Ward 6. The mean number of days taken from onset of malaria symptoms to visiting the clinic by the malaria cases was 3. Table  1 shows the sociodemographic characteristics of the study participants.

Table 1

Study participants’ socio-demographic information

Description of the malaria outbreak by place

The residents of Ward 6 of Beitbridge District mainly lived in rural villages and farm compounds where the walls of the houses were made from bricks or mud and pole while roofing material was mainly thatch, asbestos or corrugated zinc sheets. 36%, 32%, 29% and 3% of the participants resided in houses made from brick and asbestos/corrugated sheets, mud and thatch, brick and thatch, and mud and asbestos/corrugated sheets, respectively.

Although 45% of the participants slept in houses with conventional windows, some of the windows did not have panes or the panes were broken exposing the inhabitants to mosquitoes. The majority of malaria cases from the clinic line list for week 36 to week 44 were from Bishopstone farm compound (18%; n = 20) and Mzingwane village (17%; n = 19). Figure  1 shows the spot map of Ward 6 of Beitbridge District showing the distribution of the 109 cases for week 36 to week 44 of 2017 according to the Mtetengwe Clinic line list. 33% (n = 49) of the participants had IRS done more than 8 months earlier and 47% (n = 23) of these were malaria cases.

The spot map of Ward 6 of Beitbridge District showing the distribution of the 109 cases for week 36 to week 44 of 2017 according to the Mtetengwe Clinic line list

Description of the malaria outbreak by time

Figure  2 displays an epidemiological curve reporting the gradual increase in number of individuals who presented with clinical symptoms of malaria and tested positive for the disease. The curve shows a common source outbreak with intermittent exposure. The irregular peaks represents the timing and extent of exposure to the malaria parasite. Malaria positive cases started to increase gradually from the 9th of September 2017, peaked on the 3rd of October 2017 and steadily began to decline thereafter. Although malaria cases identification continued after the outbreak period, the frequencies ranged below the threshold level.

An epidemiological curve for malaria outbreak in Ward 6 of Beitbridge District, week 36 to week 44, 2017

Factors associated with malaria transmission

Table  2 shows the bivariate analysis of factors associated with contracting malaria in Ward 6, Beitbridge District between week 36 and week 44 of 2017.

Table 2

Bivariate analysis for factors associated with contracting malaria in Ward 6 of Beitbridge District for week 36 to Week 44, 2017

LLTNs: Long-lasting insecticide-treated nets; IRS: Indoor residual spraying; Mzi/Bishopstone: Mzingwane village or Bishopstone Farm

a Sleeping in a house made from bricks walls and a roof made from asbestos or corrugated roof

*Statistically significant p-value

Multivariate analysis

Backward stepwise regression analysis was conducted to estimate the variables associated with contracting malaria while controlling for confounding factors. Only variables with p < 0.1 in bivariate analysis were used in constructing a mathematical model to describe the association between exposure and disease and other variables that may confound the effect of the exposure. The results of the logistic regression are presented in Table  3 . While controlling for the presence of open eaves and having an LLIN, sleeping in a poorly constructed house remained statistically significant. Thus, individuals who slept in a poorly constructed houses were three times more likely to contract malaria than individuals sleeping in a well-constructed house. To control for multicollinearity while reducing omitted variable bias, the variable ‘house had visible eaves was excluded from the regression analysis because it had high partial correlations which inflated standard errors for ‘closing eaves at sunset’ and ‘sleeping in a poorly constructed house’.

Table 3

Multivariate analysis of the factors associated with contracting malaria in Ward 6, Beitbridge District for week 36 to week 44, 2017

*p > 0.05, result is statistically significant

Malaria outbreak preparedness and response

Before the outbreak, malaria cases were being reported to the district office on a weekly basis. The clinic staff notified the District Health Team (DHT) of the sudden increase in malaria cases on the 9th of September, 2017. The Emergency Preparedness Response (EPR) team which used to converge every Wednesday, met immediately and recommended mobilization of resources in preparation for a potential malaria outbreak. An Environmental Health Technician (EHT) from Makakabule Clinic and 2 Laboratory technicians from the district office were deployed by the DHT to establish the possibility of an outbreak in Ward 6 within 48 h since there was no EHT at Mtetengwe Clinic. The clinic staff collected blood specimens from cases identified by RDT and the blood specimens were collected daily from the clinic to the district laboratory.

Mtetengwe Clinic had a staff complement of two primary care nurses and one community-hired nurse aide. All the nurses at the clinic were trained in malaria surveillance and response as well as malaria case management. Utilizing the epidemic preparedness and rapid response guidelines, the nurses at Mtetengwe Rural Health Centre monitored malaria trends and drafted malaria threshold graphs. Mtetengwe Clinic did not experience stock outs of supplies and anti-malarial medications during the period of the outbreak.

The EHT and local VHWs, conducted active case finding by visiting the surrounding communities, identifying risk factors and giving information on malaria. Health education and promotion on use of LLINs and early seeking of treatment was ongoing. Some malaria positive cases were accompanied by the Village Health Worker to the clinic. Farm owners from distant farms such as BK Cawood and Bishopstone offered free transport for ill workers and their relatives. Indoor residual spraying was conducted in the area during the last 2 weeks of November 2017. The district team was in constant contact with the clinic staff throughout the outbreak period and the outbreak was declared over on the 20th of November 2017.

Malaria case management

Due to the unavailability of microscopy tests and microscopists in the rural areas, health workers relied on malaria rapid diagnostic test (RDT) to confirm diagnosis and this test can be conducted by nurses and village health workers (VHWs). The VHWs were equipped with RDT kits for rapid testing as well as artemisinin-based combination therapy (ACT) for uncomplicated cases. Uncomplicated cases who visited the clinic were also given ACT with the first dose being directly observed by a healthcare worker before they were discharged home. All cases reported to have completed the full course of anti-malarial medication. The complicated cases were referred to Beitbridge District Hospital for further management. The two cases who were referred during this study period, recovered well. At Mtetengwe Clinic, malaria treatment and diagnostic testing is free for all age groups.

This study sought to investigate a malaria outbreak, determine the factors associated with contracting malaria and to investigate the malaria epidemic preparedness and response in Ward 6 of Beitbridge District. The majority of the malaria cases were between the ages of 20–30 years which is contrary to the findings of the study by Drakeley et al. in which the disease was more prevalent in the age group 5–19 years [ 16 ]. This may be attributed to the young population in the study area as evidenced by the proportion of cases decreasing with age. Many people come to Ward 6 for economic opportunities particularly seeking employment in the farming estates and selling goods from South Africa. Most of the farmworkers and traders are between the ages of 20 to 30 years.

This study revealed that sleeping in a poorly constructed house were significantly associated with contracting malaria while closing eaves was protective. A similar study in Gambia reported that closing eaves halved the prevalence of malaria caused malaria in children [ 17 ]. A systematic study of five case–control studies and two cohort studies showed that both living in houses made of brick walls and closing eaves reduced the odds of contracting malaria infection by a quarter [ 18 ]. Well-constructed houses protect inhabitants from malaria by preventing entry of the malaria vectors. Modern brick walls and roofs also limit resting places for mosquitoes and reduce the attractiveness of the internal environment to the vectors when compared to the traditional mud/pole and thatch houses [ 18 ]. Although the use of LLINs and IRS are equally important, housing improvements have significantly contributed to a decline in malaria cases and elimination of the disease in many countries [ 19 , 20 ]. Two studies on malaria outbreak in Zimbabwe also cited that the presence of open eaves increased the likelihood of contracting malaria [ 13 , 21 ]. The majority of the remote Ward 6 residents lived in poorly constructed houses and similar settings of poverty and remoteness were found to be risky with regards to contracting malaria in Amazon Villages [ 22 ].

Spending time outdoors after sunset was significantly associated with contracting malaria and this finding was similar to another study in which overnight hunting and spending night time outdoors were significant malaria transmission risk factors in French Guiana [ 23 ]. Zimbabwean studies in Chipinge and Mberengwa also cited that routine outdoor activities such as bathing and night fishing were significantly associated with contracting malaria [ 13 , 24 ]. Mothers and children in Ward 6 were known to spend evening time outdoors sitting around a fire, cooking and exposing themselves to mosquito bites thus increasing their chances of contracting malaria. Adult males particularly those who were employed at the farms were also exposed to mosquito bites since they dismissed well after sunset.

The community had fairly good knowledge of malaria disease since the majority of participants managed to identify infected mosquitoes as the vector for the disease although a few still believed that eating infected fruits cause malaria. Most of the cases and controls could identify the signs and symptoms of malaria and all cases reported to have completed the treatment course. A similar study done also recorded high knowledge of the signs and symptoms as well as prevention of the disease [ 13 ]. This finding was also consistent with the malaria survey which also cited reasonably good knowledge about malaria causes, signs and symptoms and prevention among the rural community of Zimbabwe [ 25 ].

Participants reported that malaria disease was curable and they would visit a healthcare centre upon suspecting that they had the disease. Health-seeking behaviour in Ward 6 was good, a finding that was contrary to what Onwjekwe and colleagues, found out in Nigeria were populations consulted herbalists, used spiritual/ritual water or just pray ignoring the malaria symptoms [ 26 ]. This difference in results can be attributed to increased sensitization on the disease in Beitbridge District since this was a second malaria outbreak within the same year.

Having an LLIN does not directly translates to its consistent use in malaria prevention; this was noted in 56% of LLIN owners being malaria cases. A study by Msellemu et al. in Tanzania also made the same conclusion after finding out that LLINs were often reserved for children and visitors exposing other family members to the vector [ 27 ]. The study participants who had LLINs in Ward 6 reported that the LLINs were donated in 2015 and on observation some nets had developed holes. Such damages to the preventive barrier can still expose individuals to mosquito even though they used the nets. Some LLIN owners mentioned that the nets were suffocating or caused itchiness as barriers to LLIN use. This resulted in inconsistencies in the use of nets as malaria preventive measures. The fact that some of the controls who had LLINs did not use them reveals the idea that mosquito-proof housing provides some form of protection that does not depend on human compliance.

The use of EPR team to mount a response against the outbreak was an effective way of combating the malaria outbreak. Unlike in 2015 when the Beitbridge Emergency Response team did not consistently conduct EPR meetings [ 21 ], the team was sitting every Wednesday for the past 6 months and there were meeting minutes to support this. All the nurses at Mtetengwe Clinic were trained in integrated disease surveillance and response, a finding that was contrary to a similar study done in Shamva, Zimbabwe [ 9 ]. Repeated malaria outbreak attacks had prepared the Beitbridge District team to be on high alert for a possible increase in cases of the disease. This encouraged prompt outbreak notification and response to prevent further proliferation of the disease in Ward 6. Overall, both the healthcare centre and district were well prepared to respond to this malaria outbreak because most essential stocks were available and there were no stock outs during the course of the outbreak.

Zimbabwe faces a critical shortage of human resource for health and the shortage of nurses, EHTs and microscopists can have a negative impact on the control of malaria in Zimbabwe [ 9 ]. The EHT deployed during the current outbreak was from another clinic while Mtetengwe Clinic had a vacant EHT post due to the recruitment freeze on health posts [ 28 ]. The cadre was evidently overwhelmed with responsibility and rarely had time to cover Mtetengwe Clinic. This may have resulted in the insidious increase of cases in the study area.

Study limitations

All cases used in this study were diagnosed using RDT and this may have resulted in recruitment of participants infected outside the duration of the outbreak particularly during the early stages of the outbreak. Self-reported data used in this study might have introduced recall bias on exposure as well as social desirability when responding to interview questions. The sample size for the study was fairly small which could affect the power to identify significant changes in some exposure variables. This study did not include those patients who had a positive malaria test, received ACT from local VHW and recovered before visiting the clinic. The study also focused on a single infection assessment done once per person without repeat screening which limits the inference of findings to the actual malaria infections.

Health promotion messages should emphasize the importance of closing eaves and other entry points of the malaria vector, and constructing better houses in the future. Resident should also be educated on the importance of consistent use of LLINs. The district had to improve on preventive measures like distribution of LLINs and lobby for more human resources to assist with malaria surveillance thus, curbing the recurrence of malaria outbreaks in the future. To counter the possibility of another malaria outbreak, these measures had to be instituted promptly.

Supplementary information

Acknowledgements.

We would like to express our sincere gratitude to the study participants, VHWs and Mtetengwe Clinic nurses who made this study a success. We are also thankful to the Matabeleland South Medical Directorate and the Beitbridge District Medical Officer for the permission to carry out the study.

Abbreviations

Authors’ contributions

PTM conceived the study, wrote the protocol, collected the data and drafted the manuscript. PTC supervised the study and critically reviewed the manuscript. Both authors read and approved the final manuscript.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Availability of data and materials

Ethics approval and consent to participate.

The permission to carry out this study was sought from Matabeleland South Medical Director and ethical clearance was obtained from Africa University Research Ethics Committee. Approval number 266/17. All participants voluntarily signed written consent before taking part in the study.

Consent to publish

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information accompanies this paper at 10.1186/s12936-020-03270-0.

• Open access
• Published: 13 January 2024

Evaluation of malaria outbreak detection methods, Uganda, 2022

• Marie Gorreti Zalwango 1 ,
• Jane F. Zalwango 1 ,
• Daniel Kadobera 1 ,
• Lilian Bulage 1 ,
• Carol Nanziri 1 ,
• Richard Migisha 1 ,
• Bosco B. Agaba 2 ,
• Benon Kwesiga 1 ,
• Jimmy Opigo 2 ,
• Alex Riolexus Ario 1 &
• Julie R. Harris 3  

Malaria Journal volume  23 , Article number:  18 ( 2024 ) Cite this article

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Malaria outbreaks are detected by applying the World Health Organization (WHO)-recommended thresholds (the less sensitive 75th percentile or mean + 2 standard deviations [2SD] for medium-to high-transmission areas, and the more sensitive cumulative sum [C-SUM] method for low and very low-transmission areas). During 2022, > 50% of districts in Uganda were in an epidemic mode according to the 75th percentile method used, resulting in a need to restrict national response to districts with the highest rates of complicated malaria. The three threshold approaches were evaluated to compare their outbreak-signaling outputs and help identify prioritization approaches and method appropriateness across Uganda.

The three methods were applied as well as adjusted approaches (85th percentile and C-SUM + 2SD) for all weeks in 2022 for 16 districts with good reporting rates ( ≥ 80%). Districts were selected from regions originally categorized as very low, low, medium, and high transmission; district thresholds were calculated based on 2017–2021 data and re-categorized them for this analysis.

Using district-level data to categorize transmission levels resulted in re-categorization of 8/16 districts from their original transmission level categories. In all districts, more outbreak weeks were detected by the 75th percentile than the mean + 2SD method (p < 0.001). For all 9 very low or low-transmission districts, the number of outbreak weeks detected by C-SUM were similar to those detected by the 75th percentile. On adjustment of the 75th percentile method to the 85th percentile, there was no significant difference in the number of outbreak weeks detected for medium and low transmission districts. The number of outbreak weeks detected by C-SUM + 2SD was similar to those detected by the mean + 2SD method for all districts across all transmission intensities.

District data may be more appropriate than regional data to categorize malaria transmission and choose epidemic threshold approaches. The 75th percentile method, meant for medium- to high-transmission areas, was as sensitive as C-SUM for low- and very low-transmission areas. For medium and high-transmission areas, more outbreak weeks were detected with the 75th percentile than the mean + 2SD method. Using the 75th percentile method for outbreak detection in all areas and the mean + 2SD for prioritization of medium- and high-transmission areas in response may be helpful.

The global technical strategy for malaria 2016–2030 of the World Health Organization (WHO) recommends strengthening malaria surveillance as a fundamental activity to inform programme planning and implementation for improved outbreak detection in malaria-endemic countries [ 1 ]. According to the World Malaria Report of 2022, Uganda is ranked as the third-highest contributor to malaria burden globally, with 95% of the country being highly endemic and 5% prone to malaria epidemics [ 2 , 3 ].

A malaria outbreak is characterized as an increase in case counts above the threshold for the normal seasonal pattern of malaria in an area. This threshold is usually calculated based on historical routine data at the district level for a minimum of 5 years [ 4 , 5 ]. The WHO recommends various methods to calculate thresholds, including the 75th percentile, mean ± 2 standard deviations (SD), cumulative sum (C-SUM), and constant case counts [ 4 ]. The 75th percentile method considers the threshold as the 75th percentile of the average number of cases for a specific epidemiological week in that district over the past 5 years. The mean + 2SD method takes the mean number of cases for that week over the last 5 years and adds 2SD to establish the threshold. The C-SUM method involves a running average of cases for the current epi week, the previous week, and the following week over the past 5 years [ 4 ]. To accommodate seasonal malaria peaks that are not necessarily epidemics, modifications to these methods have been proposed, including raising the 75th percentile to the 85th percentile, and increasing the C-SUM method threshold by adding two standard deviations (C-SUM + 2SD) [ 4 ]. These adaptations are meant to improve the ability to distinguish between true outbreaks and regular seasonal variations.

The threshold calculation method that is recommended depends on the extent of malaria transmission in a given area. The WHO defines high transmission as an annual parasite index (API) > 450/1000, medium transmission as 251–450/1000 API, low transmission as 101–250/1000 API, and very low transmission as ≤ 100/1000 API [ 4 ]. The C-SUM method is recommended for areas with very low to low transmission; however, it is considered too sensitive for outbreak detection in medium- to high-transmission areas [ 4 ]. In the medium- to high-transmission areas, the 75th percentile method and mean + 2SD methods are both recommended by the WHO; however, they are considered too insensitive to accurately detect outbreaks in low-transmission areas [ 4 ]. For any method used, a malaria epidemic is declared when the malaria cases are above the threshold for > 2 weeks consecutively. Uganda’s malaria epidemic preparedness and response plan for 2019 suggests using the 75th percentile method at the national level and for all districts [ 6 ]. However, some districts use the mean + 2SD and others use the 75th percentile methods, based on the WHO recommendation for similar settings.

From 2019 to 2022, Uganda’s health information system reported a rise in confirmed malaria cases [ 7 ]. During the first half of 2022, more than half of the districts in Uganda were in outbreak mode for at least 10 weeks, according to the 75th percentile method used [ 8 ]. While every outbreak should be investigated and responded to by the national rapid response team, limited resources for logistics and human resources forced the national malaria control programme to restrict its response to only a few districts, using the number of complicated malaria presentations and malaria deaths as the prioritization measure. With the rate of progress slowing in terms of malaria control, not only in Uganda but also in other sub-Saharan African countries [ 9 , 10 ], there will be a need to ensure that appropriate methods are being used to identify malaria outbreaks and that prioritization methods are available when sufficient resources are not. The three threshold approaches were evaluated to compare their outbreak-signaling outputs in Uganda for improved malaria epidemic detection and response.

Study setting

Uganda comprises 15 health regions, of which 2 (West Nile and Acholi Regions) are considered areas with high annual malaria transmission rates. Five (Lango, Karamoja, Teso, Bukedi, and Busoga Regions) are considered medium malaria transmission areas and seven (South Central, North Central, Kampala, Ankole, Tooro, Bugisu and Bunyoro Regions) are considered low malaria transmission areas. Kigezi Region is considered to have very low malaria transmission and is targeted for malaria elimination in the Uganda National Malaria Strategic Plan 2025 [ 4 , 7 , 11 ].

Data source

Historic weekly malaria surveillance data from the District Health Information System version 2 (DHIS2) during 2017–2021 was used for the calculation of thresholds. The health facility malaria data are routinely generated at health facilities in outpatient registers. The data are aggregated weekly into health facility weekly surveillance reports, which are submitted to the DHIS2 using a short message system (SMS). This captures information for all health facilities in the districts. The weekly reporting rates for the districts can also be calculated based on data from this system using submitted reports (numerator) divided by expected reports (denominator). Districts with reporting rates of < 80% are considered to have incomplete data submitted.

Study variables, data abstraction, and analysis

Pivot tables were used to filter secondary data on weekly confirmed malaria cases by both rapid diagnostic test (RDT) and microscopy from the health information management system weekly disease surveillance reports (HMIS 033b report) from 2017 to 2022 available in the DHIS2. Additionally, data on weekly reporting rates for all districts was extracted. Data were extracted for each year for each district. The Ministry of Health (MoH) considers a reporting rate of ≥ 80% as the minimal level for usable data. Sixteen out of 146 districts were selected for the evaluation based on having reporting rates ≥ 80% over the 5-year period and based on their stated regional malaria transmission intensity (four each in the high, medium, low, and very low transmission regions). District API was calculated using malaria cases (numerator) and the total population (denominator) obtained from Uganda Bureau of Statistics census data for the selected districts. Malaria transmission levels by district were re-calculated using district data to enable us evaluate the accuracy of regional-level assignment of transmission levels and evaluate the different threshold approaches accurately.

Using 2022 as the year of review, thresholds were calculated using historic data from 2017 to 2021 for the selected districts. Thresholds were calculated using the three recommended approaches: Mean + 2SD, 75th percentile, and C-SUM to establish their outbreak detection sensitivity, using the highly sensitive C-SUM method as the reference. Case counts were not considered since Uganda is highly endemic for malaria and they are not recommended for such settings [ 4 ]. Malaria cases for 2022 were plotted together with the thresholds and displayed using line graphs.

The 85th percentile and C-SUM + 2SD adjusted approaches were also evaluated to see how outbreak week detection changed from the original approaches. The difference in malaria outbreak weeks detected by the various methods were compared for significance using chi-square in STATA software version 14. Finally, the number of outbreak weeks detected by the method used during 2022 and the recommended threshold method were compared, based on the district transmission level. The level of significance was considered at p < 0.05. For graphical presentation in this report, one district was picked randomly from each transmission level category (Fig.  1 ).

Average regional malaria transmission rates, Uganda, 2017–2021

Characteristics of the study data

Varying malaria incidence levels were identified for districts in the same malaria transmission region (Table  1 ). Overall, 8 of the 16 districts were recategorized based on the use of district data rather than regional data. These included one district (Nwoya) reassigned from ‘high’ to ‘medium’, two districts (Butambala and Bundibugyo) re-categorized from ‘low’ to ‘medium’, 1 district (Kanungu) recategorized from ‘very low’ to ‘low’, two districts (Alebtong and Kibuku) recategorized from ‘medium’ to ‘low’, two districts (Ntoroko and Bukwo) re-categorized from ‘’low’ to ‘very low’. Due to this identified granularity in actual transmission levels, districts were re-categorized by transmission level using district-level data and these assignments were used in the rest of the analysis (Table  1 ).

Outbreak weeks detected per threshold approach and the difference in weeks detected for specific threshold approaches

The number ‘outbreak weeks’ varied by method used across the different transmission levels. For all transmission levels, the difference in malaria outbreak weeks detected by the 75th percentile method and the mean + 2SD was statistically significant, with the 75th percentile method detecting ~ 1.5 to 30 times the number of outbreak weeks as the mean + 2SD method (p < 0.001). In low- and very low-transmission areas, the more sensitive C-SUM method usually detected similar numbers of malaria outbreak weeks as the 75th percentile method. As transmission levels increased, there was a tendency for greater differences between the C-SUM method and the 75th percentile method, with the C-SUM method detecting more outbreak weeks (Table  2 ). On adjustment of the 75th percentile method to the 85th percentile, there was no difference in the number of outbreak weeks detected for low and medium transmission levels. The adjustment of C-SUM to C-SUM + 2SD reduced its sensitivity to make it equivalent to the mean + 2SD method (Table  2 ).

Graphical presentation of malaria outbreak detection in a high-transmission district

The 75th percentile and mean + 2SD methods are both meant to be used for medium- to high-transmission districts. Using Yumbe District (high-transmission district) data, malaria cases using the 75th percentile method exceeded the threshold in 31 weeks compared to 2 (non-sequential) weeks detected by the mean + 2SD method (p-value < 0.001). Since a malaria outbreak is declared with 2 or more sequential outbreak weeks, with mean + 2SD, no malaria outbreak would be detected for Yumbe District. The 75th percentile method classified epidemics from weeks 1–15 and weeks 21–24 (Fig.  2 ).

Weekly malaria cases and thresholds on the currently used 75th percentile and mean + 2SD for the year 2022 for the high transmission Yumbe District in West Nile Region, Northern Uganda

Graphical presentation of malaria outbreak detection in a medium transmission district

Bundibugyo District, a medium-transmission district, showed 36 weeks exceeding the threshold using the 75th percentile method and 26 weeks using the mean + 2SD method. This would have resulted in the district having a malaria outbreak requiring epidemiologic investigation from weeks 5 to 25 using the mean + 2SD method, and weeks 4–25, 29–36, and 41–43 using the 75th percentile method (Fig.  3 ).

Weekly malaria cases on the currently used 75th percentile and mean + 2SD for the year 2022 for the medium-transmission Bundibugyo District in Tooro Region, Western Uganda

Graphical presentation of malaria outbreak detection in a low malaria transmission district

Alebtong District, a low-transmission district, showed 50 weeks exceeding the threshold using the 75th percentile method and 52 weeks using the C-SUM method. The district would have had a malaria outbreak requiring epidemic investigation for 49 weeks in 2022 using the 75th percentile method, and 52 epidemic weeks using the C-SUM method (Fig.  4 ).

Weekly malaria cases on the currently used 75th percentile and C-SUM for the year 2022 for the low transmission Alebtong District in Lango Region, Northern Uganda

Graphical presentation of malaria outbreak detection in a very-low malaria transmission district

For Kisoro District, Kigezi Region, an area of very low transmission also targeted for malaria elimination in the 2020–2025 Malaria Strategic Plan, the 75th percentile method detected 34 weeks above the threshold while the recommended C-SUM detected 26 weeks. This would have resulted in the district having a malaria outbreak requiring epidemic investigation from weeks 3–6 and 21–33 in 2022 using the C-SUM method, and weeks 3–6, 21–22, and 26–43 using the 75th percentile method (Fig.  5 ).

Weekly malaria cases on the currently used 75th percentile and C-SUM for the year 2022 for the low transmission Kisoro District in Southwestern Region, Uganda. This image shows clearly how the C-SUM method smooths out outliers in the data

Identifying the appropriate situations to respond to an apparent increase in cases of a disease in an endemic setting is challenging. The use of transmission intensity-specific thresholds, based on historical data, is meant to facilitate the identification of malaria outbreaks and distinguish true increases from seasonal upsurges in endemic areas. Using real examples from Uganda, major differences between threshold calculation approaches in terms of the number of weeks above the threshold detected as well as the number of outbreaks that would require epidemic response were identified. Specifically, two approaches that are both meant to be acceptable for outbreak detection in medium-to-high transmission areas (mean + 2SD and 75th percentile) yielded large differences in the number of outbreak weeks detected across all levels of transmission. The 75th percentile method yielded outbreak weeks more similar to those identified by the very sensitive C-SUM method across all transmission levels. In addition, the true transmission levels in districts were often not reflective of the region to which they were assigned.

Both the 75th percentile and mean + 2SD methods have been recommended for malaria outbreak detection in medium- to high-transmission areas, suggesting their comparability and possible interchangeability. However, significant differences in the number of weeks exceeding the outbreak threshold between these two methods were identified, with the mean + 2SD method identifying significantly fewer outbreak weeks. A Kenyan study in three different regions similarly found that the 75th percentile method identified approximately 3 times as many months as being ‘epidemic’ as the mean + 2SD method [ 12 ]. Clear guidance on the application of these methods for specific transmission areas is required for improved malaria outbreak surveillance and detection.

While only the C-SUM method is recommended for low- or very low-transmission areas, no significant difference in the number of weeks above the threshold detected by the 75th percentile and C-SUM methods in these districts was observed. Existing guidance discourages the use of the 75th percentile method in low- and very low-transmission areas due to the potential for missing outbreaks [ 4 , 13 , 14 ]. In this evaluation, outbreaks were not missed. However, in medium- and high-transmission areas, the C-SUM method detected significantly more outbreak weeks than the 75th percentile method. This supports not using the C-SUM method in medium- and high-transmission areas to avoid false alarms, as it does not account for seasonal peaks [ 4 ]. Studies conducted in Sudan and Ethiopia for early malaria epidemic detection have suggested the use of both the 75th percentile and C-SUM methods as pre-malaria-outbreak warnings in areas with medium to high malaria transmission [ 15 , 16 ].

The comparable sensitivity of the 75th percentile method and the C-SUM method in very low- and low-transmission areas and the significant differences observed in medium to high transmission areas suggests that the 75th percentile method could be applicable across all transmission levels. Since one objective of surveillance is the timely detection of outbreaks, the sensitivity of the 75th percentile method would provide timely detection of malaria epidemics, especially in medium- and high-malaria transmission areas. However, the use of this approach yielded more outbreaks than were feasible to respond to in Uganda during 2022. Thus, it may be useful to consider whether an alternate, less sensitive approach, such as the mean + SD method, could be applied for epidemic response prioritization when the 75th percentile yields more outbreak districts than can be adequately addressed with existing resources.

On adjustment of the 75th percentile to the 85th percentile, no statistically significant difference was observed in the number of outbreak weeks for low and medium transmission areas. Other studies have proposed adjusting the 75th percentile to the 90th percentile instead of the 85th to better accommodate malaria seasonal peaks and improve outbreak detection [ 4 , 17 , 18 , 19 ]. However, the small differences in outbreak weeks detected between the 75th percentile and the 85Th percentile might not suffice to recommend this adjustment for better accommodation of seasonal peaks. It may be useful to consider other modified approaches, such as modifying the 75th percentile to the 90th percentile to better accommodate seasonal peaks in some situations.

On adjustment of the C-SUM method to the C-SUM + 2SD method, there was a significant decrease in the number of outbreak weeks detected, but no difference from the number of outbreak weeks detected by the mean + 2SD method. This similarity can be attributed to both methods using averages, with the main difference lying in their respective methodologies (the mean + 2SD method takes the mean number of cases for that week over the last five years and adds 2SD to establish the threshold. The C-SUM + 2SD method takes the running average of cases for the current epi week, the previous week, and the week after over the past 5 years and adds 2SD to establish a threshold). Similar findings were observed in Madagascar in a study analysing trends and forecasting malaria epidemics using a sentinel surveillance network which indicated improved specificity when the 2SD is added to the C-SUM [ 17 ]. A consideration of C-SUM + 2SD for epidemic detection in medium to high malaria transmission districts could provide an alternative method for malaria epidemic detection to the mean + 2SD method.

In Uganda, transmission levels, on which threshold approaches are meant to be based, are assessed using regional (larger; n = 15 in Uganda) data rather than the district (smaller; n = 146 in Uganda) data. Granularity in the actual malaria transmission levels, different from the regional transmission levels for the districts evaluated was identified. The study revealed notable differences in the malaria transmission of the evaluated districts and their nationally allocated regional malaria transmission levels. Districts in high-transmission regions were found to have medium- or low-transmission levels, while some districts in low-or very low-transmission regions had medium-transmission levels. These findings highlight the need for stratification of the malaria burden at district level rather than regional level. Stratification at district level could be helpful for instances when prioritization for epidemic response is required as it only applies to medium and high transmission areas. This could also support appropriate allocation of resources for improved malaria epidemic surveillance and response at district level.

Limitations

The study's limitations include the absence of a definitive gold standard approach for identifying outbreaks; however, this is inherent to a highly endemic setting for any disease. Additionally, methods were evaluated in only 16 out of 146 districts in Uganda due to under-reporting by most districts. However, the selected districts were distributed around the country and across all transmission levels, which may enhance the generalizability of the study findings.

Our study demonstrated notable differences in district malaria transmission levels from the assigned regional malaria transmission levels. Among the districts evaluated, the 75th percentile approach proved most applicable for all transmission areas. However, the number of epidemic weeks detected for medium- and high-transmission areas was significantly higher than the mean + 2SD method. This would challenge response in resource-limited settings which is the majority of Africa where the malaria burden is high. We recommend use of the 75th percentile method for epidemic detection in all malaria transmission areas and the use of mean + 2SD for prioritization of districts for response in situations of low resources. Furthermore, the stratification of areas to the smallest geographical unit possible would ensure detection of localized malaria outbreaks. Additionally, re-calculation of malaria transmission levels at district level and re-categorization of districts rather than regions would ensure appropriate malaria outbreak surveillance and detection for appropriate response.

Availability of data and materials

The datasets upon which our findings are based belong to the Uganda Ministry of Health. However, the datasets can be availed upon reasonable request from the corresponding author and with permission from the Uganda Public Health Fellowship Program.

Abbreviations

Annual parasite incidence

District Health Information System (DHIS2)

Health facility

Standard deviation

Cumulative sum

Health Management Information System

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Acknowledgements

We appreciate the National Malaria Control Division and other national malaria stakeholders for raising the questions that initiated this analysis.

This project was supported by the President’s Emergency Plan for AIDS Relief (PEPFAR) through the US Centers for Disease Control and Prevention Cooperative Agreement number GH001353 through Makerere University School of Public Health to the Uganda Public Health Fellowship Program, Uganda National Institute of Public Health. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the US Centers for Disease Control and Prevention, the Department of Health and Human Services, Makerere University School of Public Health, or the MoH. The staff of the funding body provided technical guidance in the design of the study, ethical clearance and collection, analysis, and interpretation of data, and in writing the manuscript.

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Marie Gorreti Zalwango, Jane F. Zalwango, Daniel Kadobera, Lilian Bulage, Carol Nanziri, Richard Migisha, Benon Kwesiga & Alex Riolexus Ario

National Malaria Control Division, Ministry of Health, Kampala, Uganda

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GMZ conducted data extraction, analysis, and interpretation of the data under the technical guidance and supervision of JRH, ARA, DK, RM, BK and LB. GMZ drafted the manuscript. GMZ, JFZ, LB, RM, BBA, MKM, DK, BK, JO, ARA, and JRH, critically reviewed the manuscript for intellectual content. All co-authors read and approved the final manuscript. GMZ is the guarantor of the paper.

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Correspondence to Marie Gorreti Zalwango .

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We used routinely collected malaria surveillance data in the national health information management system DHIS2 that is publicly available for analysis and use to inform public health intervention. The data is aggregated with no individual identifiers. This activity was reviewed by the CDC and was conducted consistent with applicable federal law and CDC policy.§ §See e.g., 45 C.F.R. part 46, 21 C.F.R. part 56; 42 U.S.C. §241(d); 5 U.S.C. §552a; 44 U.S.C. §3501 et seq. This determination was made because the project aimed to address a public health problem and had the primary intent of public health practice. Additional clearance was obtained from the National Malaria Control division (NMCD) and the Division of Health Information (DHI).

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Zalwango, M.G., Zalwango, J.F., Kadobera, D. et al. Evaluation of malaria outbreak detection methods, Uganda, 2022. Malar J 23 , 18 (2024). https://doi.org/10.1186/s12936-024-04838-w

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Received : 22 November 2023

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DOI : https://doi.org/10.1186/s12936-024-04838-w

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Malaria Journal

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• Published: 04 October 2019

Malaria outbreak investigation in Tanquae Abergelle district, Tigray region of Ethiopia: a case–control study

• Kissanet Tesfay 1 ,
• Belete Assefa 2 &
• Alefech Addisu 1  

BMC Research Notes volume  12 , Article number:  645 ( 2019 ) Cite this article

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We investigated this outbreak to describe the magnitude and associated risk factors due to the malaria outbreak in Tanquae Abergelle district, Tigray, Ethiopia, in 2017.

Case fatality rate of this study was zero. Among the 62 cases and 124 controls, the presence of mosquito breeding sites [OR = 6.56 CI (2.09–20.58) P value = 0.001], sleeping outside a home [OR = 5.06 CI (1.75–14.61) P-value = 0.003] and having unscreened window [OR = 14.89 CI (1.87–118.25) P-value = 0.011] were associated with illness in multivariate analysis.

Introduction

In 2017, globally malaria caused an estimated number of 219 million cases in 87 countries, and 435,000 deaths [ 1 ]. Around 90% of malaria deaths in 2016 took place in the WHO African Region [ 2 ].

Malaria is widely distributed in tropical and subtropical countries like Sub-Saharan Africa region because of consistent high humidity and high temperature. In 2015, globally Sub-Saharan African region accounts for 76% cases and 75% of malaria deaths [ 3 ]. In Ethiopia, around 60% of the populations live in malaria-prone areas, and 68% of the country’s landmass is favorable for malaria transmission. Malaria in Ethiopia has an unusual transmission pattern and large scale epidemics happen every 5–8 years. Malaria is the leading cause of morbidity and mortality in Ethiopia [ 4 ].

In the year 2014/2015 Ethiopia had a total number of 2,174,707 malaria cases. About 85.9% were confirmed either by microscopy or rapid diagnostic test (RDT). Of those, 63.7% were positive for Plasmodium falciparum [ 4 ].

In 2014/15 all malaria cases diagnosed in the Tigray region were 302,136. Among that, 86.8% were confirmed either by RDT or using microscopy. Of those, 70.2% were caused by Plasmodium falciparum [ 5 ].

Investigation of malaria outbreak is used to determine the specific cause or causes of the outbreak at the earliest time. And it is used to take appropriate measures and prevent future occurrence of malaria outbreaks. This would contribute to the national malaria elimination strategy by 2030. Therefore, the aim of this study is to describe the magnitude and risk factors associated with malaria outbreak in Tanquae Abergelle district, Tigray, Ethiopia, in 2017.

Tanquea Abergelle is one of the districts found in the central zone of Tigray region, Ethiopia. It is bounded on south and west by Amhara Region, on west Tekeze river separating it with Amhara region, on north Kola Temben district, on east Deguea Temben district and in the southeast Southeastern zone of Tigray region.

Study period

The study was conducted from September 8 to October 18, 2017.

Study design

Descriptive epidemiology: During this outbreak, confirmed malaria case was an acute febrile illness with blood smear positive for malaria. Due to the incompleteness of the 2011–2016 malaria data, 2016s weekly malaria cases report was used to set the malaria epidemic threshold level. By doubling weekly data of 2016 and comparing it with the similar week of the year 2017 [ 6 ]. Data on malaria cases and deaths were obtained from Yechella primary hospital. This outbreak was described by age, sex, kebele, health facility, week, month and year. Similarly, slide positivity rate, attack rate, and the case fatality rate was calculated.

Analytical epidemiology: we conducted an unmatched case–control study in 1:2 ratio basis to identify risk factors associated with the disease from September 29 to October 18, 2017. Controls were selected from the community. Controls were defined as having no malaria signs and symptoms for the last 3 months and who did not have malaria by RDT during the outbreak period. During this investigation, a structured questionnaire was developed and adapted from different kinds of literature to assess risk factors for malaria. This includes data on patient age, sex, residence, family size, sleeping and staying area during the night, use of insecticide bed net, indoor residual spray, and presence of stagnant water or any other mosquito breeding area. The significance of risk factors for the outbreak was determined through multivariate analysis by calculating Odds Ratio (OR) and 95% confidence interval (CI).

Inclusion criteria

All laboratory-confirmed malaria cases of residents of Tanquae Abergelle attending Yechella primary hospital during the study period were included.

Exclusion criteria

Those severely ill and who were unconscious during the study period.

Sample size determination

We calculated the sample size using the statistical software calculation of Epi-info taking the power of 80%, odds ratio of 0.3 for ‘sleeping under ITNs, percentage of exposed controls of 87.4%, and case to control the ratio of 1:2. The total sample size yields 170. With a 10% of non-response rate, our sample size was 186, with 62 cases and 124 controls [ 7 ].

Laboratory method

Thick and thin smears with a 100 × oil immersion microscopy was conducted by laboratory technicians of Yechella primary hospital.

Environmental assessment

In addition to interviews, environmental assessment of the presence of mosquito breeding sites of cases and controls in the radius of 500 meters near to their home was conducted. These include unprotected surface water, open deep well, solid and liquid waste collection and disposal facility. Similarly, observation of these potential mosquito breeding sites and the presence of Anopheles larvae in stagnant water were conducted.

Data collection

Data were collected using interviewer-administered a structured questionnaire.

Data processing and analysis

Data were entered and analyzed using SPSS software version 22.

Data quality control

Three days of training was given to data collectors on the data collection questionnaire. We used line list for describing malaria cases in terms of time, place and person. Data completeness was checked before analysis.

Case definitions

Suspected: Patient with fever or history of fever in the last 48 h and lives in malaria-endemic areas or has a history of travel within the past 30 days to malaria-endemic areas.

Probable: Any person with fever and one or more of major sign such as headache, rigor, back pain, chills, sweats, myalgia, nausea, and vomiting diagnosed clinically as malaria [ 6 ].

Description of malaria by the person

From a total of 1300 suspected malaria cases, 694 (53.4%) were females. The median age of suspected malaria cases were 27 with interquartile range (IQR) of (19.40). The proportion of malaria cases was higher in females than in males.

The overall positivity of this outbreak was 876 (67.4%). Females had malaria positivity rate (PR) of 462 (66.5%). The age group of 5–14 had highest positivity 166 (76.8%), followed by 15–44 years of age 420 (67.3%) (Table  1 ).

The malaria attack rate of the catchment population was 33.1 per 1000 population. The case fatality rate of the catchment population was zero. The highest attack rate was among the age of 0–4 accounting 55.1 per 1000 population. Females had an attack rate of 34.2 per 1000 population (Table 2 ).

Description of malaria by place

Among the total of 1300 malaria suspected cases, 1024 (78.8%) were from Mearey kebelle.

Description of malaria by time

Tanquae Abergelle malaria outbreak had started weeks back before the outbreak was detected by field epidemiology residents in week 37. Malaria has an increasing trend during the study time. It had passed the threshold in all WHO epidemiologic week 28 to week 42.

The investigation team departed to investigate this outbreak on 29.09.2017. Interventions were held starting from the time of the investigation (Fig.  1 ).

Epi curve showing of malaria outbreak in Tanquae Abergelle district, Tigray, Ethiopia, 2017

Risk factors analysis

Of the 62 case-patient 29 (46.8%), and 69 (55.6%) of the 124 controls were males with a response rate of 100%. The median ages of cases were 19 with IQR of (10.28) and controls were 30 with IQR of (22.44) (Additional file 1 : Table S1).

In multivariate analysis the risk factors of sleeping outside home [AOR = 5.06 (CI 1.75–14.61) P-value of 0.003], the presence of mosquito breeding site around home [AOR = 6.56 (CI 2.09–20.58) P-value of 0.001], and having unscreened window [AOR = 14.89 (CI 1.87–118.25) P-value of 0.011] were associated with malaria.

Environmental observations

In our observation, there were multiple stagnant waters which could be potential mosquito breeding sites. There were also visible larvas in the stagnant waters especially in the Gerea kebelle of Tanquae Abergelle.

In the district, there were multiple mosquito breeding sites identified which might be a source of the outbreak. Moreover, indoor residual spraying of houses in affected kebeles with deltamethrin was not performed timely. Spraying in some rural kebelles had started after the outbreak had begun but didn’t involve the district town Yechella.

According to this case–control study, malaria was prevalent in females than males, an identified risk factor for malaria was the presence of mosquito breeding sites around home or vicinity, sleeping outside the home and having an unscreened window.

This study shows that malaria was more prevalent in females than in males. It was different from the finding of studies done in Oromia, and Gedio zone of Ethiopia [ 8 , 9 ]. This might be due to improper use of ITNs, not giving priority to women’s to use ITN at home and spending more time outdoors during evening performing household chores.

Our study finding of sleeping outside the home was significantly associated with malaria. This was consistent with studies done in west Begal, India, and Swaziland [ 10 , 11 ]. This might be due to the hot weather. During dry season people prefer to sleep outside in order to get fresh air and reduce heat. And this could make it difficult to use ITN’s while sleeping outside.

In our study, the finding of the presence of a mosquito breeding site around home or vicinity was significantly associated with malaria. This was in agreement with studies done in the Laelay Adeyabo, and Gedeo zone of Ethiopia, Tanzania, and Zimbabwe [ 9 , 12 , 13 , 14 ]. However study done in Hadya zone, Ethiopia has no significant association between mosquito breeding site and malaria [ 15 ]. The difference with the study done in Hadya could be due to the far distance of the mosquito breeding site from the households.

The finding of having an unscreened window as a risk for malaria was in agreement with a study done in Equatorial Guinea [ 16 ].

In Tanquae Abergelle district 15–44 years of age group and females were more affected by malaria. Malaria transmission had an increasing trend. There was a significant association of malaria with the presence of mosquito breeding sites around home or vicinity, sleeping outside a home and having an unscreened window. We would like to recommend the district health office to mobilize residents to avoid potential places of mosquito breeding site and to conduct regular indoor residual spray.

Limitations

The selected disease-free controls might be in the incubation period for developing malaria.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on request.

Abbreviations

Adjusted Odds Ratio

confidence interval

case fatality rate

Crude Odds Ratio

Health Extension Workers

interquartile range

insecticide treated bed nets

Plasmodium falciparum

positivity rate

Plasmodium vivax

Rapid Diagnostic Test

Tigray Regional Health Bureau

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Acknowledgements

Our deepest gratitude goes to Tanquae Abergelle district health office, Yechella primary hospital, Tigray regional health bureau and Mekelle University College of Medical and Health Science. We would also want to thanks Mr. Belyu Hagos for his continuous support.

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Department of Epidemiology, Mekelle University College of Health Science School of Public Health, Mekelle, Ethiopia

Kissanet Tesfay & Alefech Addisu

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Belete Assefa

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Contributions

KTW contributed to the conception of the idea, draft writing, and statistical analysis. BAA and AAG contributed in editing the manuscript, reviewing and statistical analysis. All authors read and approved the final manuscript.

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Correspondence to Kissanet Tesfay .

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Ethics approval and consent to participate.

Ethical clearance was obtained from an ethical review board of Mekelle University, College of Health Sciences and Department of Public Health. Letter of the permission was written from the field base Tigray Regional Health bureau to Tanquae Aberegelle district health office. The outbreak investigation was done after permission was obtained from Tanquae Aberegelle district health office. We obtained written informed consent from the study participants. The confidentiality of information regarding patients involved in this study was maintained by avoiding identifying study participants by name.

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Supplementary information

Additional file 1: table s1..

Bi-variate analysis related to malaria outbreak in Tanquae Abergelle district, Tigray, Ethiopia, 2017.

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Tesfay, K., Assefa, B. & Addisu, A. Malaria outbreak investigation in Tanquae Abergelle district, Tigray region of Ethiopia: a case–control study. BMC Res Notes 12 , 645 (2019). https://doi.org/10.1186/s13104-019-4680-7

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DOI : https://doi.org/10.1186/s13104-019-4680-7

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Locally Acquired Malaria Cases Identified in the United States

Distributed via the CDC Health Alert Network June 26, 2023, 5:00 PM ET CDCHAN-00494

Summary The Centers for Disease Control and Prevention (CDC) is issuing this Health Alert Network (HAN) Health Advisory to share information and notify clinicians, public health authorities, and the public about—

• Identification of locally acquired malaria cases ( P. vivax ) in two U.S. states ( Florida [4] and Texas [1]) within the last 2 months,
• Concern for a potential rise in imported malaria cases associated with increased international travel in summer 2023, and
• Need to plan for rapid access to IV artesunate, which is the first-line treatment for severe malaria in the United States.

Background CDC is collaborating with two U.S. state health departments with ongoing investigations of locally acquired mosquito-transmitted Plasmodium vivax malaria cases. There is no evidence to suggest the cases in the two states (Florida and Texas) are related. In Florida , four cases within close geographic proximity have been identified, and active surveillance for additional cases is ongoing. Mosquito surveillance and control measures have been implemented in the affected area. In Texas , one case has been identified, and surveillance for additional cases, as well as mosquito surveillance and control, are ongoing. All patients have received treatment and are improving. Locally acquired mosquito-borne malaria has not occurred in the United States since 2003 when eight cases of locally acquired P. vivax malaria were identified in Palm Beach County, FL (1). Despite these cases, the risk of locally acquired malaria remains extremely low in the United States. However, Anopheles mosquito vectors, found throughout many regions of the country, are capable of transmitting malaria if they feed on a malaria-infected person (2). The risk is higher in areas where local climatic conditions allow the Anopheles mosquito to survive during most of or the entire year and where travelers from malaria-endemic areas are found. In addition to routinely considering malaria as a cause of febrile illness among patients with a history of international travel to areas where malaria is transmitted , clinicians should consider a malaria diagnosis in any person with a fever of unknown origin regardless of their travel history. Clinicians practicing in areas of the United States where locally acquired malaria cases have occurred should follow guidance from their state and local health departments. Prompt diagnosis and treatment of people with malaria can prevent progression to severe disease or death and limit ongoing transmission to local Anopheles mosquitos. Individuals can take steps to prevent mosquito bites and control mosquitos at home to prevent malaria and other mosquito-borne illnesses.

Malaria is a serious and potentially fatal disease transmitted through the bite of an infective female anopheline mosquito. Though rare, malaria can also be transmitted congenitally from mother to fetus or to the neonate at birth, through blood transfusion or organ transplantation, or through unsafe needle-sharing practices. Malaria is caused by any of five species of protozoan parasite of the genus Plasmodium : P. falciparum , P. vivax , P. malariae , P. ovale, and P. knowlesi . Worldwide, more than 240 million cases of malaria occur each year (95% in Africa). Almost all cases of malaria in the United States are imported and occur in people traveling from countries with malaria transmission , many from sub-Saharan Africa and South Asia. Before the COVID-19 pandemic, approximately 2,000 cases of mostly travel-related malaria were diagnosed in the United States each year; approximately 300 people experienced severe disease (most P. falciparum ), and 5 to 10 people with malaria died yearly (3). Most imported cases of malaria in the United States are diagnosed during summer and early fall. In 2023, CDC expects summer international travel among U.S. residents will be increasing to pre-COVID-19 pandemic levels (4).

Clinical manifestations of malaria are non-specific and include fever, chills, headache, myalgias, and fatigue. Nausea, vomiting, and diarrhea may also occur. For most people, symptoms begin 10 days to 4 weeks after infection, although a person may feel ill as early as 7 days or as late as 1 year after infection. If not treated promptly, malaria may progress to severe disease, a life-threatening stage, in which mental status changes, seizures, renal failure, acute respiratory distress syndrome, and coma may occur. Malaria in pregnant people is associated with high risks of both maternal and perinatal morbidity and mortality. P. falciparum and P. knowlesi infections can cause rapidly progressive severe illness or death, while the other species, including P. vivax , are less likely to cause severe disease. Laboratory abnormalities can include anemia, thrombocytopenia, hyperbilirubinemia, and elevated transaminases, varying from normal or mildly altered in uncomplicated disease to very abnormal in severe disease. P. vivax and P. ovale canremaindormant in the liver and such infections require additional treatment; failure to treat the dormant hepatic stages may result in chronic infection, causing relapsing episodes. Relapses may occur after months or even years without symptoms.

Malaria is a medical emergency and should be treated accordingly . Patients suspected of having malaria should be urgently evaluated in a facility that is able to provide rapid diagnosis and treatment, within 24 hours of presentation . Order microscopic examination of thin and thick blood smears, and a rapid diagnostic test (RDT) if available, to diagnose malaria as soon as possible. Artemether-lumefantrine (Coartem®) is the preferred option, if readily available, for the initial treatment of uncomplicated P. falciparum or unknown species of malaria acquired in areas of chloroquine resistance. Atovaquone-proguanil (Malarone®) is another recommended option. P. vivax infections acquired from regions other than Papua New Guinea or Indonesia should initially be treated with chloroquine (or hydroxychloroquine). IV artesunate is the only drug available for treating severe malaria in the United States. Artesunate for Injection TM , manufactured by Amivas, is approved by the U.S. Food and Drug Administration (FDA) and is commercially available. Hospitals should have a plan for rapidly diagnosing  and treating malaria within 24 hours of presentation. Additional information on diagnosing and treating malaria, including details of treating the dormant liver stages, is available on the CDC website .

Recommendations for Clinicians

• Consider the diagnosis of malaria in any person with a fever of unknown origin, regardless of international travel history, particularly if they have been to the areas with recent locally acquired malaria.
• Routinely obtain a travel history and consider malaria in a symptomatic person who traveled to an area with malaria in the weeks to months preceding symptom onset.
• Malaria is a medical emergency. If not diagnosed and treated promptly, illness may progress to severe disease, a life-threatening stage, where mental status changes, seizures, renal failure, acute respiratory distress syndrome, and coma may occur. An algorithm for diagnosis and treatment of malaria is available here .
• Patients suspected of having malaria should be urgently evaluated in a facility, such as an emergency department, able to provide rapid diagnosis and treatment, within 24 hours of presentation.
• “BinaxNOW™,” a malaria RDT, is approved for use in the United States. RDTs are less sensitive than microscopy and cannot confirm each specific species of the malaria parasite or the parasite density.
• Therefore, microscopy should also be obtained in conjunction with an RDT as soon as possible.
• If blood smears or RDT are positive and species determination is not available, antimalarial treatment effective against chloroquine-resistant P. falciparum must be initiated immediately.
• Artemether-lumefantrine (Coartem®) is the preferred option, if readily available, for the initial treatment of uncomplicated P. falciparum or unknown species of malaria acquired in areas of chloroquine resistance. Atovaquone-proguanil (Malarone®) is another recommended option. P. vivax infections acquired from regions other than Papua New Guinea or Indonesia should initially be treated with chloroquine (or hydroxychloroquine).
• IV artesunate is the first-line drug for treatment of severe malaria in the United States. Artesunate for Injection TM is approved by the FDA for treating severe malaria and is commercially available. More information on how to acquire IV artesunate in the United States can be found here .
• Species determination is important because P. vivax and P. ovale canremaindormant in the liver and require additional antirelapse treatment; failure to treat the dormant hepatic parasites may result in chronic infection with relapsing episodes. Relapses may occur after months or even years without symptoms.
• After an urgent infectious disease consultation, if there are still questions about diagnosing and treating malaria, CDC malaria clinicians are on call 24/7 to provide advice to healthcare providers, further information can be found here .
• Suspected or confirmed locally acquired malaria is a public health emergency and should be reported immediately to your state, territorial, local, or tribal health department . Imported (or travel-associated malaria) is also reportable in all states through routine reporting methods.
• Discuss travel plans with patients; prescribe a CDC-recommended malaria chemoprophylaxis regimen and discuss mosquito bite prevention for those traveling to an international area with malaria; encourage patients to adhere to the regimen before, during, and after travel. Malaria chemoprophylaxis is not needed domestically at this time.

Recommendations for Hospitals and Laboratories

• If malaria blood smear or RDT results are not readily available, patients in whom malaria is suspected should be referred to a higher level of care for prompt evaluation for malaria.
• Bench aids for blood smear preparation, staining, diagnosis, and calculating the percent parasitemia are available here .
• More information on how to acquire IV artesunate in the United States can be found here .
• Stock artemether-lumefantrine (Coartem®), the first-line drug in the United States for most cases of uncomplicated P. falciparum or unknown malaria species.  Atovaquone-proguanil (Malarone®) is another recommended option.

Recommendations for Public Health Officials

• Public health officials who are concerned about potential cases of locally acquired malaria should contact CDC’s Malaria Branch ( [email protected] ; 770-488-7788) during regular business hours or CDC’s Emergency Operations Center ([email protected]; 770-488-7100) outside of regular business hours for assistance with recommendations and testing.
• How you can support clinicians to identify hospitals that can rapidly diagnose and treat malaria.
• Outreach to communities to provide education on the importance of precautions for malaria and other diseases before traveling internationally to an area where malaria occurs .
• Provide education to communities to prevent mosquito borne illness including breeding site reduction strategies.
• Assessing capacity of hospitals and laboratories to rapidly diagnose and treat malaria. This should include the ability to rapidly acquire and provide treatment (See Recommendations for Hospitals and Laboratories.)
• Coordination with mosquito control programs to enhance mosquito surveillance.

Recommendations for the Public

• Take steps to prevent mosquito bites and control mosquitos at home to protect yourself from any mosquito-borne illness.
• Before you travel, learn about the health risks and precautions for malaria and other diseases for your destination.
• If you are traveling internationally to an area where malaria occurs , talk to your healthcare provider about medicines to prevent you from getting malaria.
• If you have traveled to an area where malaria occurs and develop fever, chills, headache, body aches, and fatigue, seek medical care and tell your healthcare provider that you have traveled.

For More Information Malaria Prevention, Diagnosis, and Treatment

• CDC Treatment of Malaria: Guidelines for Clinicians (United States)
• CDC – DPDx – Diagnostic Procedures
• Malaria | CDC Yellow Book 2024
• CDC Malaria Information and Prophylaxis, by Country
• CDC – Parasites – Continuing Education – Malaria 101 for the Healthcare Provider
• CDC – Malaria – Travelers – Risk Assessment

Mosquito-Borne Disease Prevention

• Prevent Mosquito Bites | Mosquitoes | CDC
• CDC. Local Transmission of Plasmodium vivax Malaria — Palm Beach County, Florida, 2003 . MMWR. 2003 Sep 26; 52(38):908-911.
• Dye-Braumuller KC, Kanyangarara M. Malaria in the USA: How Vulnerable Are We to Future Outbreaks? Curr Trop Med Rep. 2021; 8(1):43-51.
• Mace KE, Lucchi NW, Tan KR. Malaria Surveillance — United States, 2018 . MMWR Surveill Summ 2022 Sep 2; 71(No. SS-8):1–29.
• Schultz JS, Mace KE, Tan KR. Return to Travel in the Coronavirus Disease 2019 Pandemic Recovery Period and Implications for Imported Malaria: Reinforcing Prevention, Early Diagnosis, and Appropriate Treatment of Malaria. Clin Infect Dis. 2023 Apr 1; 76(7):1161-1163 .

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The increase in hydro dams in the Mekong River amidst the prevalence of multidrug-resistant malaria in Cambodia has raised concerns about global public health. Political conflicts during Covid-19 pandemic led cross-border movements of malaria cases from Myanmar and caused health care burden in Thailand. While previous publications used climatic indicators for predicting mosquito-borne diseases, this research used globally recognizable World Bank indicators to find the most impactful indicators related with malaria and shed light on the predictability of mosquito-borne diseases. The World Bank datasets of the World Development Indicators and Climate Change Knowledge Portal contain 1494 time series indicators. They were stepwise screened by Pearson and Distance correlation. The sets of five and four contain respectively 19 and 149 indicators highly correlated with malaria incidence which were found similarly among five and four GMS countries. Living areas, ages, career, income, technology accessibility, infrastructural facilities, unclean fuel use, tobacco smoking, and health care deficiency have affected malaria incidence. Tonle Sap Lake, the largest freshwater lake in Southeast Asia, could contribute to the larval habitat. Seven groups of indicator topics containing 92 indicators with not-null datapoints were analyzed by regression models, including Multiple Linear, Ridge, Lasso, and Elastic Net models to choose 7 crucial features for malaria prediction via Long Short Time Memory network. The indicator of people using at least basic sanitation services and people practicing open defecation were health factors had most impacts on regression models. Malaria incidence could be predicted by one indicator to reach the optimal mean absolute error which was lower than 10 malaria cases (per 1,000 population at risk) in the Long Short Time Memory model. However, public health crises caused by political problems should be analyzed by political indexes for more precise predictions.

Citation: Nguyen PHN (2024) Data-driven nexus between malaria incidence and World Bank indicators in the Mekong River during 2000–2022. PLOS Glob Public Health 4(9): e0003764. https://doi.org/10.1371/journal.pgph.0003764

Editor: Meghnath Dhimal, Nepal Health Research Council, NEPAL

Received: January 30, 2024; Accepted: August 29, 2024; Published: September 23, 2024

Copyright: © 2024 Phuong Hoang Ngoc Nguyen. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data were downloaded free of charge on Worldbank.org . All relevant data are within the manuscript and its Supporting Information files.

Funding: The author received no specific funding for this work.

Competing interests: The author has declared that no competing interests exist.

Introduction

According to a report from the WHO, vector-borne diseases killed more than 700,000 people yearly and accounted for 17% of the deaths caused by all infectious diseases in 2020 [ 1 ]. Among mosquito-borne diseases, malaria, a parasitic infection, caused estimated 249 million cases and 608,000 deaths globally in 2022 [ 2 ]. Southeast Asia is among the regions impacted most by malaria after Sub-Saharan Africa. Recently, reported cases of multidrug-resistant malaria in Cambodia could spread worldwide rapidly, increasing the risk to public health worldwide [ 3 ].

Malaria is caused by the blood parasites Plasmodium ( P . falciparum , P . knowlesi , P . malariae , P . ovale , and P . vivax ) through the infected female Anopheles mosquitoes, which can cause symptoms such as fever and shaking. Without treatment, patients can have severe health problems, such as seizures, brain damage, trouble breathing, organ failure, and death [ 4 ].

Affected by climate change, approximately one billion people may be exposed to mosquito-borne diseases for the first time by 2080 during extreme global warming [ 5 ]. Globally, the number of dengue cases has increased 30-fold in the past 50 years [ 6 ]. Some Southeast Asian countries, such as Vietnam, the Philippines, and Malaysia, have been seriously affected by severe dengue epidemics [ 7 ]. Malaria situations in GMS were divergent in the past two decades. Myanmar contributed 92.5% of total malaria cases while China was certified as malaria free in 2021 [ 2 ].

Furthermore, more than 200 large dams planned, completed, or under construction on the Mekong mainstream and its tributaries have raised concerns about increased mosquito habitats and water-related vector-borne diseases [ 8 , 9 ]. In addition, international travel and globalization could increase the spread of infectious diseases worldwide [ 10 , 11 ]. Several severe viruses, such as the Zika virus [ 12 ], MERS-CoV, and SARS-CoV-2, have spread far beyond their origin by air travel, causing the COVID-19 pandemic. In Lower Mekong (LM) countries, where the growth rate of tourism was the fastest among Asia-Pacific regions, nearly 60 million tourists visited these regions in 2017 [ 13 ]. Multidrug-resistant infectious diseases in that region could be dispersed globally through international tourism and transport [ 3 ].

Controlling the spread of infectious diseases requires effective coordination between the health and informational sectors of transnational governments in the Greater Mekong Subregion (GMS). However, several barriers in LM countries, such as information systems based on paper reports, a low level of computerization, and a shortage of health workers with data science training in the field of preventive health care, have been common challenges in addition to their ecological distribution conflicts [ 14 ]. These factors have slowed down data sharing for disease surveillance and effective policymaking.

Published studies in the region have shown correlations between mosquito-borne diseases with many sociodemographic and environmental factors. A multivariate analysis of dengue-like diseases in suburban communities in Laos and Thailand revealed that age, education, and occupation were associated with infection rates in suburban Laos and rural Thailand [ 15 ]. Many studies in Mekong countries indicate a strong relationship between dengue incidence with temperature and humidity [ 16 , 17 ]. A study in rubber forests confirmed that industrial rubber plantations provided shelter for mosquitoes and increased the incidence of mosquito-borne diseases [ 18 ]. In the Mekong Delta, a waterborne disease index was used to map dengue for a remote sensing study in Vietnam, which revealed clear seasonal variation in dengue fever according to changes in climatic factors [ 19 ]. In general, previous studies about factors affecting mosquito-borne diseases in LM were scattered and inadequate because inconsistent data and information have not been shared between countries. In addition, those studies in the region have not employed global open data portals or widely recognizable indicators, e.g., the World Bank indicators, the Air Quality Index, or the Human Development Index. These global indicators are being used in reports by many international organizations and have become references for information exchange worldwide.

Several climatic factors, such as temperature, rainfall, and humidity, have been widely used for time series forecasting via generalized linear models (GLMs), autoregressive integrated moving averages (ARIs), seasonal autoregressive integrated moving averages (SARIMAs), and Holt-Winters models [ 16 ]. The GLM assumes a linear relationship between targets and features, while the ARIMA model assumes a linear relationship between past values and current values. This finding is inconsistent with the nonlinear seasonal climatic features in the real world. SARIMA and Holt-Winters can deal with seasonal data better than the former. However, they assume that the data are stationery and deal with stable short-term data better than long-term data. ARIMA, SARIMA, and Holt-Winters are used for univariate data; hence, they lack explanatory power, which provides insights into the underlying factors affecting the models.

Long Short Time Memory (LSTM) is an advanced Recurrent neural network (RNN) that can be used for multivariate sequential data and addressing the vanishing or exploding gradient problem in traditional RNNs [ 20 ]. They have been applied for the classification, processing, and prediction of sequential data such as time series, handwriting, and voice data. In GMS, LSTM has been used for predicting malaria in China [ 21 ] or dengue in Vietnam [ 22 ] by climatic factors as the features.

Using too many features in prediction can cause overfitting and computational inefficiency. Ridge is a regularization technique used for preventing overfitting in linear regression models [ 23 , 24 ]. It uses L2 regularization, which is controlled by the tuning parameter alpha, to shrink the coefficients toward zero and prevent overfitting, hence resulting in a more robust and accurate model. Similarly, Lasso [ 25 ] is another regularization technique that uses L1 regularization to select features, but this approach could cause information loss due to the limited recognizability of collinearity between important indicators and their collinear counterparts. Elastic Net combines both Lasso and Ridge by learning from their shortcomings to improve the regularization.

This research aims to find critically impactful World Development Indicators (WDIs) correlated with malaria incidence in the GMS and used multiple linear regression combined with Ridge, Lasso, and Elastic Net to select features for time series forecasting by LSTM.

The methodology used in this work is described in Fig 1 , which includes the following steps: data preprocessing, correlational analysis, regression, and time series forecasting.

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The World Bank datasets included 1494 time series indicators from 2000–2022 from six countries in the Greater Mekong Subregion. Among the set of indicators strongly correlated with malaria incidence in most countries, seven were selected as the features for Multiple Linear Regression models combined with Ridge, Lasso, and Elastic Net techniques. One indicator critically affecting the linear models was chosen for time series forecasting by Long Short Time Memory Network. The vectors of map were downloaded from public domain https://www.naturalearthdata.com/ . The clipart icons were downloaded from public domain https://www.flaticon.com/ including:

https://www.flaticon.com/free-icon/data-collection_2103533

https://www.flaticon.com/free-icon/analysis_1239623

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The WDI dataset of the updated version (May 30, 2024) for GMS, which included Cambodia, the People’s Republic of China, the Lao People’s Democratic Republic (Lao PDR), Myanmar, Thailand, and Vietnam, was downloaded from the World Bank Group [ 26 ]. The dataset contained 1492 time series indicators, including the indicator of malaria incidence per 1000 population at risk (WDI code: SH.MLR.INCD.P3), from 2000 to 2022. In addition, another dataset of climatic indicators containing annual mean average temperature and precipitation in the GMS from 2000 to 2022 was downloaded from the Climate Change Knowledge Portal (CCKP) [ 27 ]. The WDI and CCKP datasets were combined into the final dataset containing 1494 indicators from 2000 to 2022 for six countries of the Mekong River. The datasets are provided in S1 Table .

Correlation

Distance correlation was calculated by the equation in Eq ( 2 ), in which D(x i , x j ) is the Euclidean distance between two sets of indicators, with x for malaria and y for the other indicators [ 29 ]. The Distance correlation ranges from 0 to 1, where 0 implies independence between two indicators and 1 implies a linear relationship.

The indicators with high correlation coefficients, > = 0.8 or < = -0.8 for Pearson and > = 0.8 for Distance correlation, which were found similarly between GMS countries, were put into the sets from 1 to 6.

The features in each topic or topic group for the regression models were selected from the indicators containing > = 20 datapoints that were strongly correlated with malaria and found similarly in most countries. The MLR models which were established in Eq ( 3 ) with malaria (y), the number of other independent variables (k), the k th feature (x k ), the regression coefficient (weight) of the k th feature (β k ), and the intercept (β 0 ). The datapoint values in each topic or topic group were normalized between 0 and 1. Ridge, Lasso, and Elastic Net algorithms were applied with an alpha range from 10 −5 to 10 2 for feature selection in time series forecasting.

Time series forecasting

The many-to-one architecture of LSTM consists of the following four layers. The 1 st LSTM layer takes mini batches in the sliding window from the time series input and returns the whole sequence. The number of neurons in the 1 st LSTM layer equals the number of features multiplied by the size of the sliding window. The 2 nd LSTM layer with the same number of neurons receives the sequence from the 1 st LSTM layer but only returns the same number of features. The next dense layer with the number of neurons is the same as the size of the features. The last dense layer outputs the predicted value. The LSTM setting parameters for the sequential model include normalization between 0 and 1, a training ratio from 0.6 to 0.8, 10 to 30 epochs, a batch size from 10 to 30, and a sliding window size (window size) from 1 to 9.

Performance metrics for evaluation

The performance metrics for model evaluation were calculated by Eqs ( 4 )–( 7 ). The absolute error (MAE), mean absolute percentage error (MAPE), mean square error (MSE), and coefficient of determination (R2) were used for evaluating the MLR and LSTM models.

The calculation and visualization were implemented in Python 3.9.16 with the following main packages: Cartopy 0.22.0, Geopandas 0.9.0, Geoplot 0.5.1, Keras 2.12.0, Matplotlib 3.7.1, Numpy 1.23.5, Pandas 1.5.3, Scikit-Learn 1.2.1, and TensorFlow 2.12.0.

Trends in malaria incidence

In the GMS, Myanmar, Cambodia, and Laos were among the top countries with the most cases of malaria during 2000–2022 ( Fig 2 ). The spread of a multidrug-resistant co-lineage of P . falciparum malaria, named KEL1/PLA1, across Cambodia could lead to a peak time of malaria during 2008–2013 [ 30 ]. Similar peaks repeated later in Myanmar, Laos, and Thailand. Myanmar contributed 92.4% indigenous malaria cases and 95.0% indigenous P . falciparum cases in 2021–2022 [ 2 ]. The increase of malaria in Myanmar from 78 000 cases in 2019 to 584 000 cases in 2022 amid political instability, which was shown at the end of line plot of Fig 2A , led cross-border movements of individuals to seek health care in Thailand. Nevertheless, the other GMS countries are aiming for certification of malaria elimination like China, which was successfully certified malaria free in 2021.

(A) The trends in malaria incidence in the Greater Mekong Subregion during 2000–2022. (B) The Greater Mekong Subregion in 2022. The vectors of map were downloaded from public domain https://www.naturalearthdata.com/ .

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Among a total of 1494 combined indicators of WDI and CCKP, 994 and 394 unique indicators, respectively, were strongly correlated with malaria incidence in the GMS countries according to Pearson and Distance correlation ( Fig 3 ).

(A) The number of indicators strongly correlated with malaria incidence according to Pearson correlation analysis; the results were similar for one to six countries, and the values are shown in parentheses. (B) The number of indicators strongly correlated with malaria incidence according to the Distance correlation coefficient, which was similar for one to six countries, with the corresponding indicators in parentheses. (C) The set of 4, containing 149 indicators highly correlated with malaria incidence, was found similarly for four countries with 109 indicators were identified by Pearson correlation, and 88 indicators were identified by Distance correlation, with 48 intersecting indicators. (D) The set of 5, containing 19 indicators highly correlated with malaria incidence, was found similarly for five countries with 19 indicators were identified by Pearson correlation, and 0 indicators were identified by Distance correlation.

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Table 1 summarizes the number of indicators in the set of 4 and 5 which were counted by topic groups, number of datapoints, urbanization, and gender. Fig 4 and Table 2 present the Pearson correlation coefficients in the set of 5. The coefficients in the set of 4 are provided in S2 Table .

The linkage for hierarchical clustering uses the complete method and Euclidean metric. Zero (0.00) values contain null values.

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In the set of 5 containing 19 indicators which were found similarly for five GMS countries, most of them (10/19) belong the topic group of Environment ( Table 1 ). The other topic groups are Health (7/19) and Poverty (2/19). Among 149 indicators in the set of 4, half of them (71/149) belong to the topic group of Health. The second and third leading topic groups are Economic Policy & Debt (23/149) and Social Protection & Labor (17/149). The set of 4 contains 92 indicators with > = 20 datapoints during 2000–2022. However, most of indicators in the set of 5 contain less than 20 datapoints, from two to seven datapoints per country, except the only indicator of people practicing open defecation in urban (SH.STA.ODFC.UR.ZS). The number of datapoints in each indicator is provided in S3 Table .

Environment & demographics

In the topic group of Environment, the rural land area (AG.LND.TOTL.RU.K2, Fig 4 ) was perfectly positively correlated with malaria incidence, as opposed to the urban land area (AG.LND.TOTL.UR.K2, Fig 4 ) in all countries. Similarly, the rural land area where the elevation is less than 5 meters (AG.LND.EL5M.RU._, Fig 4 ) was perfectly positively correlated with malaria, in contrast to the urban land area where the elevation is less than 5 meters (AG.LND.EL5M.UR._, Fig 4 ) in all four countries except Cambodia.

Moreover, the population living in areas where the elevation is less than 5 meters (EN.POP.EL5M._, Fig 4 ) was completely positively correlated with malaria cases in Cambodia and Vietnam as opposed to Myanmar and Thailand. Meanwhile, the population density (EN.POP.DNST, S2 Table ), population in largest city (EN.URB.LCTY, S2 Table ), and the population in urban agglomerations of more than one million (EN.URB.MCTY.TL.ZS, S2 Table ) were strongly negatively correlated with malaria incidence in most countries.

On the other hand, the population ages under 4 (SP.POP.0004., S2 Table ), population ages 0–14 (SP.POP.0014., S2 Table ), and population ages 10–14 (SP.POP.1004., S2 Table ) were correlated strongly positively with malaria incidence, in contrast to the population ages 55–59 (SP.POP.5559.MA.5Y, S2 Table ) and population ages above 80 (SP.POP.80UP.MA.5Y, S2 Table ).

Income per capita (SI.SPR._, Fig 4 ), GDP per capita (NY.GDP.PCAP._, S2 Table ), wage and salaried workers (SL.EMP.WORK._, S2 Table ), and coverage of social protection & labor programs (per_allsp.cov_pop_tot, S2 Table ) were strongly negatively correlated with malaria incidence. In contrast, employment in agriculture (SL.AGR.EMPL._, S2 Table ) was strongly positively correlated with malaria incidence, which was like self-employed (SL.EMP.SELF._, S2 Table ), and vulnerable employment (SL.EMP.VULN._, S2 Table ).

The indicators of environment and demographics suggest living areas, ages, careers, income, social securities have affected malaria incidence.

Manufacturing (NV.IND.MANF.CN, S2 Table ), industry (NV.IND.TOTL._, S2 Table ), service (NV.SRV.TOTL._, S2 Table ), merchandise imports from low- and middle-income economies (TM.VAL.MRCH._, S2 Table ), researchers in R&D (SP.POP.SCIE.RD.P6, S2 Table ) and statistical performance indicators (IQ.SPI.PIL1, S2 Table ) were strongly negatively correlated with malaria incidence.

Similarly, individuals using the Internet (IT.NET.USER.ZS, S2 Table ), account ownership at a financial institution or with a mobile-money-service provider (FX.OWN.TOTL._, S2 Table ), accessibility to clean fuels and technologies for cooking (EG.CFT.ACCS._, S2 Table ), and average time to clear exports through customs (IC.CUS.DURS.EX, S2 Table ) were strongly negatively correlated with malaria cases, in contrast to power outages in firms in a typical month (IC.ELC.OUTG, S2 Table ).

The indicators of economics suggest economic improvement, technology accessibility, infrastructural facilities of information, transportation, and energy help to decrease malaria incidence.

In the topic groups of health, the cause of death by communicable diseases and maternal, prenatal and nutrition conditions (SH.DTH.COMM.ZS, Fig 4 ) was strongly positively with malaria incidence, which was similar to infant deaths (SH.DTH.IMRT, S2 Table ), neonatal deaths (SH.DYN.NMRT, S2 Table ), under-five deaths (.SH.DYN.MORT._., S2 Table ), and incidence of HIV (SH.HIV.INCD._, S2 Table ). Besides, prevalence of current tobacco use (SH.PRV.SMOK._, Fig 4 ) was also strongly positively correlated with malaria incidence, which was like people practicing open defecation (SH.STA.ODFC._, Fig 4 ).

In contrast, universal health coverage (UHC) index (SH.UHC.SRVS.CV.XD, Fig 4 ) was strongly negatively correlated with malaria incidence, which was similar to people using at least basic drinking water services (SH.H2O.BASW._, S2 Table ), people using at least basic sanitation services (SH.STA.BASS._, S2 Table ), people with basic handwashing facilities including soap and water (SH.STA.HYGN.ZS, S2 Table ).

The indicators of health suggest vulnerable dependent population, health safety and habits have affected malaria incidence.

In the suspected indicators which were previously reported about the correlation with malaria ( Table 3 ), forest area (AG.LND.FRST._) was associated with malaria incidence in some countries. Climatic indicators of precipitation (PR.) and temperature (TAS.) were uncorrelated with malaria incidence in all countries, which was like air transport (IS.AIR._) in both freight and passenger. Meanwhile, the renewable internal freshwater resources per capita (ER.H2O.INTR.PC) were moderately positively correlated with malaria incidence, especially strong in Cambodia and Vietnam.

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Multiple linear regression model

Although most of the 19 indicators in the set of 5 show a perfect correlation with malaria incidence, they contain very few observations for building dependable models except the indicator of people practicing open defecation in urban (SH.STA.ODFC.UR.ZS). Therefore, 92 indicators containing > = 20 datapoints were considered as the features for regression models from the set of 4. There was high collinearity between these indicators via Pearson correlational analysis as shown in S4 Table . S5 Table provided MLR coefficients when using all indicators in each topic group for building MLR models to predict malaria incidence.

When using 50 indicators from the group topic of Health for building MLR models ( Fig 5 and S5 Table ), each country, each locality (urban and rural regions), and each gender reveals different patterns. The MLR coefficient of people practicing open defecation in urban (SH.STA.ODFC.UR.ZS) in Lao PDR is negative while they were positive in the other countries, especially high in China. However, the MLR coefficient of people practicing open defecation in rural is negative in China while they were positive in the others. In Myanmar, two out-standing indicators with negative coefficients including people using safely managed sanitation services in urban (SH.STA.SMSS.UR.ZS) and population ages 65 and above in female (SP.POP.65UP.FE.IN) while population ages 65–69 in male (SP.POP.6569.MA.5Y) had an out-standing positive coefficient. Nevertheless, the coefficients of population ages 80 and above (SP.POP.80UP._) are noticeably positively high in Vietnam.

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Using 50 features such as the indicators from topic group of Health at the same time for MLR regression could cause overfitting models but using too few features such as one or two indicators from the topic group of Infrastructure or Private Sector & Trade could cause under fitting models. Therefore, 92 indicators were divided into 7 groups based on their topics or topic groups for feature selection by MLR, Ridge, Lasso, and Elastic Net regressions. The MLR, Ridge, Lasso, and Elastic Net coefficients for each group are provided in S6 Table . The more the alpha increases in the paths of the regularized coefficients, the coefficients of the more affecting indicators in the models tended more slowly to zero.

Table 4 presents the most impacting indicator per each group in each country. The indicator SH.STA.ODFC.RU.ZS was the most impacting feature of the group 1 of Health in most countries, except Myanmar. In Myanmar, SH.STA.SMSS.UR.ZS was the most impacting feature in the group 1 of Health ( Fig 6 ). Moreover, it was also the most impacting feature among seven representative indicators selected from 7 groups in Myanmar ( Fig 7 ).

(A) Ridge. (B) Lasso. (C) Elastic Net.

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The number of features, training ratio, and window size played significant roles in tuning the optimal models ( Fig 8 ). The MAE values range from 2–32 (mean = 11, median = 10). The lower MAE values indicate the better models. The training ratio 0.6 releases MAE values larger than the models with training ratio 0.7 and 0.8. The window sizes which were lower than 3 or higher than 4 at big epochs and batches also result the higher MAE values. There was no significant difference between the one-feature model ( Fig 9A & 9B ) and three-feature model ( Fig 9C & 9D ). Both feature sets were better than the 7 feature sets ( Fig 9E & 9F ). In general, the predictability of malaria incidence could be predicted by World Bank indicators via machine learning and artificial intelligence at national or subregion levels yearly.

The parameters of the LSTM model included the number of features, window size, training ratio, epochs, and batch size. The evaluation data of the LSTM network are provided in S7 Table . One-feature models used each of the indicators ’SH.STA.SMSS.UR.ZS’, ’SP.DYN.IMRT.IN’, and ’SP.POP.TOTL.MA.ZS’. Three-feature model used all of three indicators ’SH.STA.SMSS.UR.ZS’, ’SP.DYN.IMRT.IN’, and ’SP.POP.TOTL.MA.ZS’. Seven-feature model used all of seven indicators ’SH.STA.SMSS.UR.ZS’, ’SP.DYN.IMRT.IN’, ’SP.POP.TOTL.MA.ZS’, ’NY.GDP.PCAP.KD’, ’NY.GNP.PCAP.PP.CD’, ’SL.AGR.EMPL.FE.ZS’, and ’SP.RUR.TOTL.ZS’.

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(A & B) One-feature model used the indicators ’SH.STA.SMSS.UR.ZS’. (C & D) Three-feature model used all of three indicators ’SH.STA.SMSS.UR.ZS’, ’SP.DYN.IMRT.IN’, and ’SP.POP.TOTL.MA.ZS’. (E & F) Seven-feature model used all of seven indicators ’SH.STA.SMSS.UR.ZS’, ’SP.DYN.IMRT.IN’, ’SP.POP.TOTL.MA.ZS’, ’NY.GDP.PCAP.KD’, ’NY.GNP.PCAP.PP.CD’, ’SL.AGR.EMPL.FE.ZS’, and ’SP.RUR.TOTL.ZS’.

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Many previous publications have shown that climatic factors such as temperature, moisture, and precipitation are strongly correlated with mosquito-borne diseases [ 31 – 33 ]. However, this study showed that climatic factors were uncorrelated with malaria incidence in the GMS during 2000–2022. The reason could be that the input data in previous studies were collected locally where latitude, longitude, elevation, and weather changed in limited regions compared to a very wide range of climatic patterns across the different regions of the GMS in this research. This provides hope for local communities in hot and humid regions to combat against not only malaria but also mosquito-borne diseases by considering and affecting other key factors.

Other reports have indicated that many sociodemographic factors, such as urbanization, education, and occupation are strongly correlated with mosquito-borne diseases [ 15 , 34 ]. This research also reinforces these findings and provides other noticeable indicators. Here, people using at least basic sanitation services and people practicing open defecation are among the crucial indicators affecting regression models. Many previous studies have shown that poor sanitation, open defecation, and improper wastewater management are ideal breeding conditions for mosquitoes [ 35 – 38 ]. Although rapid urbanization in developing countries could cause urban health problems such as air pollution, garbage, heat, the urban island effect, and water containers for larval habitats [ 39 ], the results here show that rural land area and rural population living in area where elevation is below 5 meters were correlated positively strongly with malaria incidence, as opposed to urban ones in most GMS countries. However, the limited number of datapoints in land area and population should be considered to conclude urbanization or ruralization facilitates malaria. This pattern was reversed in Cambodia. Tonle Sap Lake, the largest freshwater lake in Southeast Asia [ 40 ], could contribute greatly to the larval habitat in Cambodia. This research also revealed that the renewable internal freshwater resources per capita were correlated positively strongly with malaria incidence in most of the GMS, especially in Cambodia and Vietnam. The increase in hydro dams in the Mekong River to satisfy the unstoppable demands of economic development will be one of the main roots for spreading mosquito borne diseases in future.

Furthermore, behaviors at the household level, for example, improving housing quality and removing larval habitats, provided evidences for preventing mosquito-borne diseases [ 41 – 43 ]. A prior research on socioeconomic and household risk factors with malaria showed using wood and dung cakes as cooking fuel were significantly more at risk to have malaria cases [ 44 ]. Others reported carbon dioxide as a mosquito attractant on Aedes [ 45 , 46 ], Culex [ 47 ] and Anopheles [ 48 , 49 ]. Here, the indicators of accessibility of clean fuels and technologies for cooking is correlated negatively strongly with malaria incidence in most GMS countries. Smartphone geospatial apps and other mobile technology-tools have been used for disease surveillance in community [ 50 ]. This research also shows that individuals using the Internet is correlated negatively strongly with malaria incidence.

In addition, nutritional conditions, and healthy habits are also among the important key factors for reducing mosquito-borne diseases according to this study. Mosquitoes are attracted by several specific chemicals, such as carbon dioxide, lactic acid, and oct-3-enol, that are emitted by tobacco smokers [ 45 , 46 , 51 – 55 ]. Here, the prevalence of current tobacco use was also strongly correlated with the incidence of malaria in all GMS countries. In addition to tobacco users, pregnant women who exhale more carbon dioxide also attract more mosquitoes [ 56 ]. According to this research, female individuals aged 15–19 years are among most affecting features in regression models. Some odorous compounds produced by skin bacteria and emitted strongly by young adults that might attract mosquitoes [ 57 , 58 ]. Beside young adults, dependent population such as elderly individuals and infants who usually have weak immune systems and limited mobility were critically affected by malaria.

While most previous reports about predicting malaria and other mosquito-borne diseases by regression and time series forecasting models used climatic factors as features [ 13 , 14 , 18 , 19 , 41 ], the results here provide another approach using globally referenceable World Bank indicators. Some indicators, such as land area, have changed little for a long time, so it is difficult and expensive to collect data yearly. Although the yearly datasets in specified countries cause a challenge in building high resolution models for certain geographic locality, the recommended features used for building predicting models are among the indicators that are strongly correlated with malaria and are found similarly in most Mekong countries. Those results will deliver stakeholders and policymakers important references to make national and transnational decisions. However, political upheaval and humanitarian crisis which were main reasons of malaria increasing in Myanmar since 2021 have not been estimated by WDI.

Despite many steady efforts towards malaria elimination in GM, antimalarial drug resistance is still a concern in GMS. Malaria is among the most common fatal vector-borne diseases, especially in low- and middle-income countries. LM has been thriving in tourism, a green industry, owing to its diversified original culture and nature, which are less impacted by humans even though this also increases the risk of the spread of communicable diseases. To pursue sustainable development goals, transnational governments in GMS need to communicate effectively by sharing electronic data and information about communicable diseases, including malaria.

Malaria is still one of the most severe public health problem midst the multidrug-resistant malaria in Cambodia and the increase in hydro dams in the Mekong River. From 1494 indicators from WDI and CCKP, this research provided the sets of 4 and 5 containing respectively 19 and 149 indicators highly correlated with malaria incidence which was found respectively similarly for five and four GMS countries by Pearson and Distance correlation. They indicate malaria incidence are correlated with the living areas, ages, careers, health habits, economic status, technology accessibility, infrastructural facilities of information, transportation, and energy. From the set of 4, 92 indicators containing > = 20 datapoints were analyzed by MLR, Ridge, Lasso, and Elastic Net regressions. Seven most impacting features from seven topic groups were chosen for LSTM model. WDI can be used for predicting malaria incidence by LSTM model with one feature. While WDI could be referred for transnational or national level decisions, certain geographic areas still need high resolution time series indicators such as climatic indicators for disease surveillance. However, public health crises in GMS caused by political instability should be analyzed by political indexes for more precise prediction.

Supporting information

S1 table. dataset of world development indicator..

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S2 Table. Peason and Distance correlation coefficients.

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S3 Table. Number of datapoints in the set of 4 and 5.

https://doi.org/10.1371/journal.pgph.0003764.s003

S4 Table. Collinearity via Pearson correlation between the indicators of the set of 4.

https://doi.org/10.1371/journal.pgph.0003764.s004

S5 Table. MLR coefficients of total of not-null indicators in the set of 4.

https://doi.org/10.1371/journal.pgph.0003764.s005

S6 Table. MLR, Ridge, Lasso, and Elastic Net coefficients of the indicators in each group of topics.

https://doi.org/10.1371/journal.pgph.0003764.s006

S7 Table. The evaluation and coefficients of long short time memory network for Myanmar.

https://doi.org/10.1371/journal.pgph.0003764.s007

Acknowledgments

The author would like to express the gratitude to her family and communities, who support her unconditionally. The author would like to acknowledge the Mekong-U.S. Partnership Young Scientist Program in the second year known as “Lower Mekong Initiative 2019 (LMI)—Public Health & Bioinformatics: Using Information Technologies to Address Public Health Challenges”. The program was sponsored by the U.S. State Department and implemented by Arizona State University (ASU) and the National University of Laos (NUOL).

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FACT SHEET: The United States Commitment to Address the Global Mpox Outbreak

“Now we face the mpox outbreak in Central and Eastern Africa. Mpox is different from COVID-19. But we will act quickly – and bring partners with us. We are prepared to commit at least $500 million – to support African countries to prevent and respond to mpox and donate up to one million doses of mpox vaccines. We call on governments, charities, and businesses to match our pledge – and make this a $1 billion commitment to the people of Africa.” —President Biden, September 24, 2024

The United States has led global efforts to combat infectious diseases, including mpox, for decades. Most recently in 2022, the Biden-Harris Administration mounted a robust response to the spread of clade IIb mpox by making vaccines available to those at risk, making testing more convenient, and providing treatments to those who needed them both in the United States and worldwide. In response to the ongoing mpox outbreak in Eastern and Central Africa, with several cases outside the region, the United States is acting quickly and decisively to support the response, and to prepare for potential cases domestically. On September 16, the White House welcomed key partners and community stakeholders working on mpox in the United States and around the world to a roundtable with U.S. Government leadership to exchange ideas, feedback and recommendations to inform the U.S. response to this global crisis.

This week, President Biden announced that the United States is committed to providingat least $500 million dollars, as well as one million mpox vaccine doses, to support African countries to prevent and respond to the current mpox outbreak. These investments will be delivered both bilaterally, through existing relationships with partner countries, as well as through multilateral institutions. United States investments in mpox preparedness and response will address a range of needs outlined in the Mpox Continental Preparedness and Response Plan jointly issued by the Africa Centers for Disease Control and Prevention (Africa CDC) and the World Health Organization (WHO), including training frontline health workers, disease surveillance, laboratory diagnostic supplies and testing, clinical case management, risk communication and community engagement, infection prevention and control, and research. In addition to financial support and vaccines, the U.S. Government has surged dozens of staff, including epidemiologists, laboratorians, and risk communication experts to offer support to the mpox response in DRC and each of the countries surrounding DRC.

BUILDING STRONGER, RESILIENT HEALTH SYSTEMS

Investments in building stronger health systems are essential to a rapid and effective emergency response. Longstanding United States support, including through the President’s Emergency Plan for AIDS Relief (PEPFAR), helped to strengthen the systems that are now supporting the mpox response.

• Ongoing global health and health security investments. Since the start of the Biden-Harris Administration, the United States has provided more than $50 billion to support global health and health security. The United States is the largest health donor in the Africa region, allocating more than $2.65 billion in bilateral health funding to countries in Central and Eastern Africa in FY 2023 alone.
• Global health security partnerships. In April 2024, the United States announced formal global health security partnerships with 50 countries, including Burundi, DRC, Kenya, and Uganda. Global health security investments make it possible for the United States to address country-identified gaps in their capacity to prevent, detect, respond to, and recover from health security threats. U.S. assistance to the government of DRC, which began in 2015, has bolstered the DRC’s efforts to contain five Ebola outbreaks since 2020, develop an antimicrobial stewardship work plan, and develop a community feedback system to address infectious disease threats.
• President’s Emergency Plan for AIDS Relief (PEPFAR). For over 20 years, PEPFAR has supported more than 55 countries worldwide, saved more than 25 million lives, enabled 5.5 million babies to be born HIV-free, and prevented millions of new HIV infections. Longstanding PEPFAR investments in creating sustainable HIV care platforms have been leveraged for quick and effective response to cholera, COVID-19, Ebola, H1N1 influenza, tuberculosis, and other health threats. Given the increased risk of severe morbidity and mortality from mpox among people living with HIV, PEPFAR is ensuring program continuity to protect people living with HIV through the use of existing PEPFAR platforms through risk communication, laboratory and surveillance capacity, referral to care, HIV testing, and vaccination delivery to help prevent and respond to mpox.

SUPPORTING MPOX TESTING, VACCINATION, TREATMENT AND CARE

• Mpox vaccine research and development. Since 2007, the United States, through the Department of Health and Human Services (HHS), has invested more than $2 billion in the JYNNEOS vaccine as part of smallpox preparedness. Additionally, U.S. Government research institutions led the development of the JYNNEOS vaccine through preclinical evaluation, clinical trials, and advanced clinical evaluation platforms. These investments directly led to product licensure for both smallpox and mpox. On September 13, WHO announced pre-qualification of the JYNNEOS vaccine for global use, including in the Africa region in response to ongoing mpox outbreaks.
• Mpox vaccine donation. This week President Biden pledged that the United States will donate up to one million doses of the mpox vaccine. The first U.S.-donated vaccine doses arrived in Nigeria in August (10,000 doses), and in DRC in September (50,000 doses). The next installment of the U.S. commitment, 300,000 vaccine doses, will be available immediately for disbursement in coordination with Gavi, the Vaccine Alliance and the WHO Access and
• Allocation Mechanism. Additional mpox vaccine doses will be delivered in tranches (totaling up to one million) pending country progress in administering the vaccines, in coordination with Gavi.
• Clinical care and protecting health workers. I n DRC, the U.S. Government has procured and delivered medical kits containing antibiotics, oral hydration, and wound care supplies to support government facilities to offer mpox patients relief from their symptoms free of charge, which bolsters community trust and connection with the health care system. The U.S. Government is expanding health care worker capacity to treat mpox and offer psychosocial support to patients, while simultaneously training the workers to protect themselves through use of infection prevention and control best practices.
• Diagnostic tests and training. The U.S. Government is also supporting mpox-affected countries with laboratory expertise and diagnostic supplies. This includes: providing over 40,000 individual test assays and reagents that ensured that countries in the region had the capacity to detect clade I mpox when it crossed their borders; training dozens of laboratory personnel on the use of mpox test kits and procedures to enhance laboratory safety, hygiene, and waste management; strengthening the reach and availability of rapid diagnostic testing capacity; expanding specimen transportation routes; and establishing platforms for laboratory data management.
• Development and testing of effective therapeutics. The United States Government is leading the ongoing “Study of Tecovirimat for Human Mpox Virus” clinical trial for mpox treatment in the United States and other countries affected by clade II mpox.
• Identifying mpox research priorities. To help prioritize mpox research, the United States released an update on mpox research priorities, focusing on four objectives: (1) increasing knowledge about the biology of all clades, including how the virus is transmitted and how people’s immune systems respond to it; (2) evaluating dosing regimens of current mpox vaccines to stretch the vaccine supply and developing novel vaccine concepts; (3) advancing existing and novel treatments, including antivirals and monoclonal antibodies; and (4) supporting strategies for detecting the virus to facilitate clinical care and epidemiological surveillance.

LEVERAGING STRONG MULTILATERAL PARTNERSHIPS

As with investments in health systems, building stronger and more effective multilateral institutions between emergencies is essential to ensuring the world is prepared to respond effectively in times of crisis. The United States supports the critical roles of WHO and Africa CDC in leading the mpox response, and we call on those institutions to utilize the strong partnerships that are already in place, including with other multilateral institutions, to protect the health and wellbeing of people living in the affected countries.

• World Health Organization. Among his first acts in office, President Biden declared the United States would reengage with WHO, highlighting our nation’s commitment to advancing multilateral cooperation in a global health crisis. Beyond health emergencies, the United States is collaborating with WHO on a wide range of global health issues such as childhood immunization, nutrition, polio eradication, and strengthening the global health workforce to achieve universal health coverage. Since the beginning of the Biden-Harris Administration, the United States has provided nearly $1.9 billion of support to WHO. In addition, since March 2024, the United States has already provided more than $7.7 million to WHO to support mpox response activities, and $450,000 for building sustainable capacity for mpox elimination in DRC, Burundi, Central African Republic, Republic of Congo, Rwanda, and Uganda.
• Africa CDC. The United States welcomes and supports the role of Africa CDC as a continent-wide public health institution, established in 2016. In 2022, the U.S. Government signed a Memorandum of Cooperation to Promote Public Health Partnership with the African Union, accompanied by a U.S.-Africa CDC Joint Action Plan outlining shared global health priorities and areas for collaboration. In addition to substantial U.S. bilateral and multilateral support aligned with Africa CDC’s five-year strategic plan and Agenda 2063, the United States provided more than $3 million in direct support to the Africa CDC in the form of in-kind assistance last year alone.
• Gavi, the Vaccine Alliance. Gavi holds essential expertise in effective vaccine procurement, distribution, and administration, which should be leveraged immediately in the mpox response. Since its inception in 2000, the United States Government has invested or announced: 1) over $3.6 billion to improve equitable access to new and underutilized vaccines in low- and middle-income countries; 2) a $4 billion dollar contribution to Gavi’s COVAX Advance Market Commitment; 3) an annual contribution to Gavi’s core budget, including $300 million in 2024 ; 4) and pledged at least $1.58 billion towards USG’s first-ever five-year pledge to Gavi’s next replenishment cycle, subject to Congressional approval. U.S. funding is included in Gavi’s $500 million First Response Fund, which is supporting procurement, delivery, and deployment of 500,000 JYNNEOS doses in response to the mpox outbreak. Finally, affected countries, WHO, Africa CDC, and Gavi recently established the Access and Allocation Mechanism (AAM) as a platform to increase equitable access to mpox response resources and contributions.
• The Quad. The Quad partnership was established in 2020 between the United States, India, Japan and Australia as a global force for good, including working together to help partners address pandemics and disease. During a September 21 Quad Summit, leaders agreed to coordinate efforts to promote equitable access to safe, effective, quality-assured mpox vaccines, including where appropriate expanding vaccine manufacturing in low and middle-income countries.

In addition to ongoing bilateral and multilateral support to build stronger health systems, respond to ongoing health challenges, and pivot to address the current mpox crisis, the United States supports expanded sources of financing for response to health emergencies. Many of these have been developed and launched since the COVID-19 pandemic to address gaps identified through that response.

• The Pandemic Fund. As the only multilateral fund fully focused on prevention and preparedness, the Pandemic Fund has a critical role to play in building capacity to end the current outbreak and prevent the next one. The Pandemic Fund has taken quick action to support mpox preparedness efforts, approving $129 million to support 10 countries impacted by the disease to strengthen laboratory, surveillance, and human resources capacities. The selected projects meet needs articulated in the joint WHO-Africa CDC Mpox Continental Preparedness and Response Plan for Africa. The awards will be implemented over multiple years enabling an effective transition from crisis to long term preparedness. To continue its critical work, the Pandemic Fund is engaged in a concurrent resource mobilization round, with the goal of raising at least $2 billion in new funding through 2026. The United States has committed to provide up to $667 million, subject to Congressional appropriations and the availability of funds.
• Gavi’s Day Zero Financing Facility. The United States has supported Gavi, the Vaccine Alliance in establishing the Day Zero Financing Facility, a suite of tools that will mobilize, for example, up to $2 billion in risk-tolerant surge and contingent capital to enable Gavi to quickly meet the demand for vaccines in a pandemic.
• U.S. Development Finance Corporation (DFC) Health Emergency Financing: The DFC finances private-sector led solutions to health services, supply chain, and technology challenges in low- and middle-income countries. These solutions improve health system resilience and pandemic preparedness through: 1) a $1 billion-dollar rapid financing facility applicable to a full spectrum of vaccines (COVID-19, childhood vaccine-preventable diseases, and future outbreaks); 2) investments in regional, Africa-based vaccine manufacturing, including Aspen Pharmacare (South Africa) and Institute Pasteur de Dakar (Senegal); and 3) a G7 Surge Financing Initiative for Medical Countermeasures that supports Gavi and regional vaccine manufacturers.
• Multilateral development bank (MDB) evolution. MDBs have a key role to play in helping countries address global challenges, such as climate change, pandemics, and fragility and conflict. The United States is working with other shareholders to evolve the visions, incentive structures, operational approaches, and financial capacity of the MDBs to equip these institutions to respond to global challenges with sufficient speed and scale. The United States is pleased to see the close coordination between the World Bank, IMF, and regional development banks with WHO and affected countries on how to best utilize or reprogram resources to aid the mpox response.

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Malaria outbreak investigation and contracting factors in Simada District, Northwest Ethiopia: a case-control study

Affiliations.

• 1 Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
• 2 Department of Human Nutrition, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia. [email protected].
• 3 Department of Human Nutrition, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
• PMID: 31101068
• PMCID: PMC6525450
• DOI: 10.1186/s13104-019-4315-z

Objective: The aim of this study was to assess the occurrence of malaria outbreak and investigate contracting factors of malaria in Simada District, Northwest Ethiopia. A single observation original research.

Results: Among the total 54 cases, 44 (81.5%) of them were confirmed malaria cases. The average attack rate was 20 per 100 and slide positivity rate was 81.5%. People in the age group of 5-14 years were most affected with an attack rate of 37%. Presence of water bodies for mosquito breeding inside less than 1 km radius (AOR = 3.32, 95% CI 1.18-9.34), no knowledge on transmission, prevention and control mechanisms of malaria (AOR = 4.36, 95% CI 1.64, 12.23), not using Insecticide Treated Bed Net (AOR = 5.85, 95% CI 1.94, 17.54) and absence of environmental control (AOR = 10.01, 95% CI 2.94, 33.33) were factors associated with malaria outbreak.

Keywords: Case control; Contracting factors; District; Ethiopia; Investigation; Malaria; Outbreak; Simada.

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Conflict of interest statement

The authors declare that they have no competing interests.

Map of investigation area, Workaye…

Map of investigation area, Workaye Kebele, Simada District, Amhara Region, Ethiopia 2016 (source:…

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