Spatial panorama of malaria prevalence in Africa under climate change and interventions scenarios. International

Abstract

Background: Malaria is highly sensitive to climatic variables and is strongly infuenced by the presence of vectors in a region that further contribute to parasite development and sustained disease transmission. Mathematical analysis of malaria transmission through the use and application of the value of the basic reproduction number (R0) threshold is an important and useful tool for the understanding of disease patterns. Methods: Temperature dependence aspect of R0 obtained from dynamical mathematical network model was used to derive the spatial distribution maps for malaria transmission under diferent climatic and intervention scenarios. Model validation was conducted using MARA map and the Annual Plasmodium falciparum Entomological Inoculation Rates for Africa. Results: The inclusion of the coupling between patches in dynamical model seems to have no efects on the estimate of the optimal temperature (about 25 °C) for malaria transmission. In patches environment, we were able to establish a threshold value (about α = 5) representing the ratio between the migration rates from one patch to another that has no efect on the magnitude of R0. Such fndings allow us to limit the production of the spatial distribution map of R0 to a single patch model. Future projections using temperature changes indicated a shift in malaria transmission areas towards the southern and northern areas of Africa and the application of the interventions scenario yielded a considerable reduction in transmission within malaria endemic areas of the continent. Conclusions: The approach employed here is a sole study that defned the limits of contemporary malaria transmission, using R0 derived from a dynamical mathematical model. It has ofered a unique prospect for measuring the impacts of interventions through simple manipulation of model parameters. Projections at scale provide options to visualize and query the results, when linked to the human population could potentially deliver adequate highlight on the number of individuals at risk of malaria infection across Africa. The fndings provide a reasonable basis for understanding the fundamental efects of malaria control and could contribute towards disease elimination, which is considered as a challenge especially in the context of climate change