The impact of antimalarials and insecticide resistance on malaria transmission in Uganda

Investigator: Melissa Conrad, PhD
Sponsor: NIH John F. Fogarty International Center

Location(s): Uganda


Our goal is to use our well established clinical and entomological infrastructure in Uganda to perform experiments in which we feed mosquito vectors malaria-infected blood from Ugandan patients using membrane feeding assays. We will measure parasite and mosquito factors that we hypothesize impact on transmission, and use modeling techniques to determine how they influence the number of mosquitoes that become infected and the number of parasites that infect each mosquito. These data and the models that fit them will be useful for helping policy makers decide which interventions to prioritize to reduce the malaria burden in this resource limited settings.

 Eliminating malaria in high transmission settings where asymptomatic infections are prevalent will require improved interventions to treat malaria, control vectors, and also decrease transmission to mosquitoes. However, our understanding of what factors govern the efficiency of malaria transmission is incomplete, limiting our ability to accurately predict the impacts of transmission-reducing interventions. We hypothesize that parasite and mosquito factors are associated with the likelihood of malaria transmission to mosquitoes. To test this hypothesis, we will utilize our well-established clinical and entomology infrastructure in Tororo, Uganda to infect field-collected and colony anopheline mosquitoes with blood from P. falciparum-infected Ugandans using membrane feeding assays. We will then analyze the prevalence and intensity of malaria infection in mosquitoes in relation to measured parasite and vector characteristics. Among the characteristics we will investigate are gametocyte density, multiplicity of infection and sex ratio, parasite drug resistance and genotypes, and mosquito insecticide resistance and genotypes. Using Bayesian Markov Chain Monte Carlo techniques, we will fit models to our data, allowing explicit estimation of parameters related to infectiousness needed to reproduce the observed data, allowing us to test our hypotheses regarding variation in infectiousness, measure the magnitude of these effects, and identify putative sources of the variation, information that will be essential to inform policy decisions that will facilitate the control and eventual elimination of malaria. Specifically, our results will help us to prioritize control measures directed toward parasites (drugs) and mosquitoes (insecticides) in Uganda.