Yao Li, Postdoctoral Associate in the Center for Geospatial Information Science, along with Professor Kathleen Stewart and Assistant Research Professor Dong Chen have published the article, "Understanding spatio-temporal human mobility patterns for malaria control using a multi-agent mobility simulation model" in the journal Clinical Infectious Diseases (Impact factor: 20.99). The research identified the relationship between occupation-related daily travel and malaria exposure in two townships in Myanmar.

Read the full article here.



More details about human movement patterns are needed to evaluate relationships between daily travel and malaria risk at finer scales. A multi-agent mobility simulation model was built to simulate the movements of villagers between home and their workplaces in two townships in Myanmar.

An agent-based model (ABM) was built to simulate daily travel to and from work based on responses to a travel survey. Key elements for the ABM were landcover, travel time, travel mode, occupation, malaria prevalence, and a detailed road network. Most visited network segments (MVS) for different occupations and for malaria-positive cases were extracted and compared. Data from a separate survey was used to validate the simulation.

Mobility characteristics for different occupation groups showed that while certain patterns were shared among some groups, there were also patterns that were unique to an occupation group. Forest workers were estimated to be the most mobile occupation group, and also had the highest potential malaria exposure associated with their daily travel in Ann Township. In Singu Township, forest workers were not the most mobile group; however, they were estimated to visit regions that had higher prevalence of malaria infection over other occupation groups.

Using an ABM to simulate daily travel generated mobility patterns for different occupation groups. These spatial patterns varied by occupation. Our simulation identified occupations at a higher risk of being exposed to malaria and where these exposures were more likely to occur.

Most visited network segments for forest workers, miners and farmers in Singu Township