Understanding the population risk from malaria using human mobility

Authors: Yao Li*, Center for Geospatial Information Science, Department of Geographical Sciences University of Maryland, College Park, MD 20742, Kathleen Stewart, Center for Geospatial Information Science, Department of Geographical Sciences University of Maryland, College Park, MD 20742
Topics: Medical and Health Geography, Geographic Information Science and Systems
Keywords: malaria,mobility
Session Type: Virtual Paper
Day: 4/10/2021
Start / End Time: 8:00 AM / 9:15 AM
Room: Virtual 8
Presentation File: No File Uploaded


Understanding patterns of mobility for local people can identify which population groups may be particularly vulnerable to infection as well as contribute to transmission. However, for areas where malaria still exists albeit at low levels of transmission, mass controls are not necessarily efficient. Here we present an mobility pattern analysis for the local people in Singu Township, located in central Myanmar. An agent-based model to simulate the movements of villagers in Singu Township. Travel history data was collected in 37 villages in Singu Township between 2018 and 2019. Patterns for different occupations were compared regarding their mobility pattern similarity with infected local people. This research will help us to understand the patterns of movements of these local people as a step towards understanding how mobility could be impacting their risk as well as the transmission of malaria in this area.

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