Malaria hotspots explained from the perspective of ecological theory underlying insect foraging

Debebe, Yared; Hill, Sharon R.; Tekie, Habte; Dugassa, Sisay; Hopkins, Richard

doi:10.1038/s41598-020-78021-x

Cited by 11 publications

(18 citation statements)

References 47 publications

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“…These hotspots represent areas where malaria transmission intensity is significantly higher than the surrounding areas. They constitute significant reservoirs for residual malaria transmission, with higher malaria prevalence than the neighbouring areas (Debebe et al, 2020). This presents an opportunity for more targeted control interventions expected to be cost-effective and yield more results than blanket interventions in the State.…”

Section: Discussionmentioning

confidence: 99%

Point prevalence mapping of malaria infection in Rivers State, Nigeria

Egbom¹,

Nduka

Nzeako

2022

Tanzania J Hlth Res

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Background: Variations in the risk of malaria across locations exist but are poorly understood though identifying hotspots of malaria transmission will create opportunities for targeted interventions. Point prevalence of malaria in Rivers State was studied using Primary Healthcare Centres (PHCs) as survey points. Methods: The PHCs in Rivers State were geo-referenced using a handheld Global Positioning System (GPS) and 74 were selected across 21 local government areas using systematic grid point sampling. Blood samples were obtained from 2340 persons who consented and questionnaires were administered to obtain their demographic data. Malaria parasites in blood films were detected using the Giemsa staining technique. Data generated were analysed using SPSS 22.0 and presented using descriptive statistics. The level of relationship amongst the parameters was obtained using Chi-square. Co-ordinates of PHCs sampled and their prevalence data for malaria were entered into Microsoft Excel 2007 spreadsheet and transmitted to ArcGIS 10.8. This platform was then used to produce point prevalence infection maps of the State using geographic information systems (GIS). Survey points with malaria point prevalence values of 75% and above and cumulative prevalence of 1.97% and above were categorised as malaria transmission hot spots in the various LGAs. Results: The study recorded an overall prevalence of 56.3%, with P.falciparum as the only identified malaria parasite. Data revealed that Oyoro Model Primary Health Centre (MPHC), Arukwo Primary Health Centre, Ele Health Post (HP) and Emago HP recorded very high prevalence of 96.7%, 96%, 95.2% and 94.4% respectively, whereas MPHC Iriebe had the least prevalence. Twelve hotspots with point prevalence above 75% were identified and eight hotspots likewise with cumulative prevalence above 1.97%. Conclusion: Malaria infection remains endemic in Rivers State. This study provides malaria point prevalence maps of Rivers State which will serve as a reference to policymakers for strategic interventions in the State

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Section: Discussionmentioning

confidence: 99%

Point prevalence mapping of malaria infection in Rivers State, Nigeria

Egbom¹,

Nduka

Nzeako

2022

Tanzania J Hlth Res

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“…Second, we calculated the distance from each collection point to the edge of the village (defined as the convex hull polygon of each village—i.e. the minimum polygon that encompasses all the locations of the households), as it has been suggested elsewhere that living on the edge can increase biting rates [ 45 ].…”

Section: Methodsmentioning

confidence: 99%

Data-driven and interpretable machine-learning modeling to explore the fine-scale environmental determinants of malaria vectors biting rates in rural Burkina Faso

et al. 2021

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Background Improving the knowledge and understanding of the environmental determinants of malaria vector abundance at fine spatiotemporal scales is essential to design locally tailored vector control intervention. This work is aimed at exploring the environmental tenets of human-biting activity in the main malaria vectors (Anopheles gambiae s.s., Anopheles coluzzii and Anopheles funestus) in the health district of Diébougou, rural Burkina Faso. Methods Anopheles human-biting activity was monitored in 27 villages during 15 months (in 2017–2018), and environmental variables (meteorological and landscape) were extracted from high-resolution satellite imagery. A two-step data-driven modeling study was then carried out. Correlation coefficients between the biting rates of each vector species and the environmental variables taken at various temporal lags and spatial distances from the biting events were first calculated. Then, multivariate machine-learning models were generated and interpreted to (i) pinpoint primary and secondary environmental drivers of variation in the biting rates of each species and (ii) identify complex associations between the environmental conditions and the biting rates. Results Meteorological and landscape variables were often significantly correlated with the vectors’ biting rates. Many nonlinear associations and thresholds were unveiled by the multivariate models, for both meteorological and landscape variables. From these results, several aspects of the bio-ecology of the main malaria vectors were identified or hypothesized for the Diébougou area, including breeding site typologies, development and survival rates in relation to weather, flight ranges from breeding sites and dispersal related to landscape openness. Conclusions Using high-resolution data in an interpretable machine-learning modeling framework proved to be an efficient way to enhance the knowledge of the complex links between the environment and the malaria vectors at a local scale. More broadly, the emerging field of interpretable machine learning has significant potential to help improve our understanding of the complex processes leading to malaria transmission, and to aid in developing operational tools to support the fight against the disease (e.g. vector control intervention plans, seasonal maps of predicted biting rates, early warning systems). Graphical abstract

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“…The lower mosquito vector density in the fields compared to outdoors near shelters might be due to higher attraction to places where the host population was higher [ 28 , 29 ].…”

Section: Discussionmentioning

confidence: 99%

Malaria in migrant agricultural workers in western Ethiopia: entomological assessment of malaria transmission risk

Dugassa

Murphy

Chibsa

et al. 2021

Malar J

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Background Ethiopia has made great strides in malaria control over the last two decades. However, this progress has not been uniform and one concern has been reported high rates of malaria transmission in large agricultural development areas in western Ethiopia. Improved vector control is one way this transmission might be addressed, but little is known about malaria vectors in this part of the country. Methods To better understand the vector species involved in malaria transmission and their behaviour, human landing collections were conducted in Dangur woreda, Benishangul-Gumuz, between July and December 2017. This period encompasses the months with the highest rain and the peak mosquito population. Mosquitoes were identified to species and tested for the presence of Plasmodium sporozoites. Results The predominant species of the Anopheles collected was Anopheles arabiensis (1,733; i.e. 61.3 % of the entire Anopheles), which was also the only species identified with sporozoites (Plasmodium falciparum and Plasmodium vivax). Anopheles arabiensis was collected as early in the evening as 18:00 h-19:00 h, and host-seeking continued until 5:00 h-6:00 h. Nearly equal numbers were collected indoors and outdoors. The calculated entomological inoculation rate for An. arabiensis for the study period was 1.41 infectious bites per month. More An. arabiensis were collected inside and outside worker’s shelters than in fields where workers were working at night. Conclusions Anopheles arabiensis is likely to be the primary vector of malaria in the agricultural development areas studied. High rates of human biting took place inside and outdoor near workers’ residential housing. Improved and targeted vector control in this area might considerably reduce malaria transmission.

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Malaria hotspots explained from the perspective of ecological theory underlying insect foraging

Cited by 11 publications

References 47 publications

Point prevalence mapping of malaria infection in Rivers State, Nigeria

Point prevalence mapping of malaria infection in Rivers State, Nigeria

Data-driven and interpretable machine-learning modeling to explore the fine-scale environmental determinants of malaria vectors biting rates in rural Burkina Faso

Malaria in migrant agricultural workers in western Ethiopia: entomological assessment of malaria transmission risk

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