Transmitted by the bite of Anopheles mosquitoes, Plasmodium parasites are responsible for hundreds of millions of clinical malaria cases and more than a million deaths every year. The Institute for Disease Modeling is committed to supporting data-driven malaria control and elimination efforts through our research. Whether through statistical analyses on surveillance data or the application of our individual-based stochastic model of malaria transmission, our team is focused on understanding what combination of tools is necessary and sufficient to interrupt transmission, what are the dynamics of the human infectious reservoir after transmission is interrupted, and what are the impacts of introducing new interventions.
IDM’s malaria team is an active member of the Malaria Modeling Consortium.
Currently in development, there are vaccines that protect humans from acquiring infections, prevent transmission back to mosquitoes, and reduce the severity of disease manifestations. Our research explores the role of present and future vaccine candidates in reducing the burden of malaria morbidity and mortality, and as part of multi-intervention campaigns, increasing the probability of elimination in different settings. Recent work has focused on understanding the efficacy and waning profile of the RTS,S/AS01 vaccine candidate towards estimating the most cost-effective settings for deployment.
Antimalarial Drug Campaigns
Artemisinin-based combination therapies can reduce transmission when widely distributed in a campaign setting. Modeling mass antimalarial campaigns can elucidate how to most effectively deploy drug interventions and quantitatively compare the effects of cure, prophylaxis, and transmission-blocking elements in suppressing parasite prevalence. Recent work has applied age- and weight-dependent drug pharmacokinetics in the EMOD model to address the importance of diagnostic threshold, campaign timing, drug compliance, and population coverage.
The intensity of malaria transmission is often quite focal, with exposure to infectious bites varying by an order of magnitude over just kilometers. Because the task of eliminating malaria from a region requires not just the interruption of local transmission but also the prevention of reimportation, it is critical to understand the spatial patterns of heterogeneous exposure, population mobility, and accessibility. Together with our partners at PATH MACEPA and the Zambian National Malaria Control Program, we continue to analyze the operational effectiveness of ongoing large-scale antimalarial drug campaigns in Southern Zambia. Using a spatial model of the region, we have projected the impact of switching to alternative drugs and distribution modes.
The genetic signature of malaria parasites encodes the transmission history of the population from which they are sampled -- whether it is emerging drug resistance, a recent population bottleneck, or distinguishing imported cases from local transmission. We have combined the EMOD transmission model with the genetic dynamics of parasite strain generation through sexual recombination of distinct gametocytes in the mosquito midgut. Recent work has compared the predictions of our epidemiological modeling to the population-genetics signatures observed in Senegal during a period of falling-then-rebounding transmission.