Authors: Yao Li*, Center for Geospatial Information Science, Department of Geographical Sciences, University of Maryland, College Park, USA, Kathleen Stewart, Center for Geospatial Information Science, Department of Geographical Sciences, University of Maryland, College Park, USA, Shannon Takala-Harrison, Howard Hughes Medical Institute/Center for Vaccine Development, University of Maryland School of Medicine, Baltimore, Maryland, USA, Christopher V. Plowe, Institute for Global Health, University of Maryland School of Medicine, Baltimore, Maryland, USA, Timothy O’Connor, Institute for Genome Sciences and the Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
Topics: Medical and Health Geography, Spatial Analysis & Modeling, Geographic Information Science and Systems
Keywords: Markov Chain Monte Carlo, malaria, Cambodia
Session Type: Paper
Start / End Time: 4:40 PM / 6:20 PM
Room: Lafayette, Marriott, River Tower Elevators, 41st Floor
Presentation File: No File Uploaded
In response to emerging resistance to the artemisinins and their partner drugs, the World Health Organization has set a goal to eliminate Plasmodium falciparum malaria from the Greater Mekong Subregion where resistance first emerged and is highly prevalent. Estimating malaria parasite migration may inform elimination efforts by identifying regions of higher parasite out-migration (transmission sources) that can be targeted by elimination interventions. We present preliminary results from a research study on malaria parasite migration and genetic diversity using estimated effective migration surfaces, a Markov Chain Monte Carlo simulation-based approach to visualize a species’ migration and diversity based on georeferenced genomic data and provides visualizations of both migration and diversity for a study region. Malaria parasite genomic data collected in 27 districts in Cambodia as well as 8 districts in the bordering regions of Thailand, Vietnam, and Laos during 2008-2013 provides the basis for this analysis. We use a popular open source toolkit for estimating migration surfaces and discuss our efforts to generate more spatially explicit gene flow maps that reduce spatial uncertainties by accounting for the spatial distribution of genetic samples of Plasmodium falciparum. This work uses data generously shared by collaborators including the U.S. Armed Forces Research Institute of Medical Sciences, the Tracking Resistance to Artemisinins Collaboration, and the Artemisinin Resistance Confirmation, Characterization and Containment project.