Impact of Environmental Modifications on the Ecology, Epidemiology, and Pathogenesis of Plasmodium falciparum and Plasmodium vivax Malaria in East Africa

Abstract

Food insecurity, recurrent famine, and poverty threaten the health of millions of African residents. Construction of dams and rural irrigation schemes is key to solving these problems. The sub-Saharan Africa International Center of Excellence for Malaria Research addresses major knowledge gaps and challenges in Plasmodium falciparum and Plasmodium vivax malaria control and elimination in malaria-endemic areas of Kenya and Ethiopia where major investments in water resource development are taking place. This article highlights progress of the International Center of Excellence for Malaria Research in malaria vector ecology and behavior, epidemiology, and pathogenesis since its inception in 2017. Studies conducted in four field sites in Kenya and Ethiopia show that dams and irrigation increased the abundance, stability, and productivity of larval habitats, resulting in increased malaria transmission and a greater disease burden. These field studies, together with hydrological and malaria transmission modeling, enhance the ability to predict the impact of water resource development projects on vector larval ecology and malaria risks, thereby facilitating the development of optimal water and environmental management practices in the context of malaria control efforts. Intersectoral collaborations and community engagement are crucial to develop and implement cost-effective malaria control strategies that meet food security needs while controlling malaria burden in local communities.

Figure 1.

International Center of Excellence for Malaria Research study sites in relation to malaria endemicity in Ethiopia and Kenya. Malaria endemicity is indicated by the population-adjusted estimate of Plasmodium falciparum infection rate in 2- to 10-year-old children (PfPR_2–10) for Kenya and the sum of P. falciparum plus Plasmodium vivax infections for Ethiopia. The map was adapted from the 2019 Malaria Atlas Project database.

Figure 2.

Integration of hydrological model with remote sensing and malaria transmission model to predict the impact of irrigation and water management on malaria vectors and disease risks within a sugarcane plantation in Arjo, Ethiopia. (A) Flow chart of modeling processes. (B) Predicted dry-season soil moisture saturation distribution in the absence of irrigation. (C) Dry-season soil moisture saturation distribution when the sugarcane plantation was irrigated. (D) Predicted impact of irrigation on daily entomological inoculation rate. (E) Impact of irrigation on malaria parasite prevalence. EMOD = Epidemiological MODeling software. Figure was adapted from Jiang et al.

Figure 3.

Spatial distribution of seropositivity for Plasmodium falciparum circumsporozoite protein and apical membrane antigen 1 among 1- to 10-year-old children in Homa Bay, Kenya, according to predicted malaria risk based on distance to irrigation area and elevation. (A) Circumsporozoite protein IgG antibody prevalence. (B) apical membrane antigen 1 IgG antibody prevalence.

Figure 4.

Impact of large dams in Africa on malaria burden. (A) Distribution of large dams in relation to malaria stability in 2015 in sub-Saharan Africa. (B) Number of annual malaria cases attributable to large dams in sub-Saharan Africa. (C) Relationship between malaria incidence and reservoir slope, showing a lower slope at the shoreline having greater impacts on malaria. Figure was adapted from Kibret et al.

Figure 5.

New outdoor resting and host-seeking malaria vector surveillance tools developed and evaluated at International Center of Excellence for Malaria Research study sites. (A) Sticky clay pot, with arrow showing the waterproof paper coated with Tangle-Trap sticky substance. (B) Human-odor baited CDC light trap, with arrow showing the polyvinyl chloride pipe that connects to a human sleeping room to move human odors from indoors to an outdoor mosquito-catching station via a fan. (C) Human-baited double net/CDC light trap combination, with arrow showing the CDC light trap. Figure was adapted from Degefa et al.^,