Gene Flow Inference Under Sparse Spatial Sampling
Accurately mapping malaria parasite migration guides elimination efforts by pinpointing hotspots and barriers. Popular tools such as EEMS and MAPS visualize gene-flow corridors from georeferenced genomic data, but sparse or uneven sampling can create misleading artefacts. We introduce a sample-location-aware (SLA) filter that measures local sampling density with topological skeletons and kernel-density estimates, removing contours lacking support. Tests on simulated landscapes and Cambodian Plasmodium falciparum data show that SLA pruning eliminates spurious barriers and yields more stable, precise migration maps than posterior-probability filtering alone. The approach can be paired with any spatial gene-flow method to boost reliability when sampling is patchy.
Malaria parasites migration simulation
Understanding the genetic structure of natural populations provides insight into the demographic and adaptive processes that have affected those populations. Such information, particularly when integrated with geospatial data, can have translational applications for a variety of fields, including public health. In this study, we developed a workflow to optimize the resolution of spatial grids used to generate EEMS migration maps and applied this optimized workflow to estimate migration of Plasmodium falciparum in Cambodia and bordering regions of Thailand and Vietnam.
