New spatially explicit approaches for estimating malaria parasite migration

The investigators consider how the spatial distribution of parasite genomic sampling locations impacts estimated migration patterns and determine whether the mapped migration paths are still meaningful even when there is an irregular distribution of sampling locations. The research provides insights on how to include locally known migration barriers as well as other relevant natural or built environmental features into mapped migration estimates. As public health officials work toward reducing and eventually eliminating malaria, local information about factors driving malaria risk is important for prioritizing resources and optimizing control and elimination strategies. The research will be undertaken using computational methods that generate estimated effective migration surfaces. These methods use Markov chain Monte Carlo simulations to estimate migration and create migration contours that can be mapped to show relative high or low areas of gene flow. This research will integrate local spatial features to improve gene flow estimates and account for possible barriers thereby reducing spatial uncertainty that is commonplace in current maps of parasite migration. A set of analyses will be undertaken to determine the robustness of parasite migration surface estimates to varying spatial distributions of genomic data sample locations. This research is expected to not only have a profound impact for local public health officials tasked with eliminating P. falciparum and P. vivax malaria but will transform the ability of a broader group of researchers to produce parasite migration estimates for other kinds of species at local scales and provide new computational tools for studying parasite migration.

Project details

Measurement of Transmission

Mar 2021 — Feb 2024

$405,515

United States