Understanding Spatiotemporal Human Mobility Patterns for Malaria Control Using a Multiagent Mobility Simulation Model.

BACKGROUND: More details about human movement patterns are needed to evaluate relationships between daily travel and malaria risk at finer scales. A multiagent mobility simulation model was built to simulate the movements of villagers between home and their workplaces in 2 townships in Myanmar. METHODS: 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 land cover, travel time, travel mode, occupation, malaria prevalence, and a detailed road network. Most visited network segments for different occupations and for malaria-positive cases were extracted and compared. Data from a separate survey were used to validate the simulation. RESULTS: 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. CONCLUSIONS: 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.

Temporal Dynamics of Subclinical Malaria in Different Transmission Zones of Myanmar.

Countries in the Greater Mekong Subregion have committed to eliminate Plasmodium falciparum malaria by 2025. Subclinical malaria infections that can be detected by highly sensitive polymerase chain reaction (PCR) testing in asymptomatic individuals represent a potential impediment to this goal, although the extent to which these low-density infections contribute to transmission is unclear. To understand the temporal dynamics of subclinical malaria in this setting, a cohort of 2,705 participants from three epidemiologically distinct regions of Myanmar was screened for subclinical P. falciparum and P. vivax infection using ultrasensitive PCR (usPCR). Standard rapid diagnostic tests (RDTs) for P. falciparum were also performed. Individuals who tested positive for malaria by usPCR were followed for up to 12 weeks. Regression analysis was performed to estimate whether the baseline prevalence of infection and the count of repeated positive tests were associated with demographic, behavioral, and clinical factors. At enrollment, the prevalence of subclinical malaria infection measured by usPCR was 7.7% (1.5% P. falciparum monoinfection, 0.3% mixed P. falciparum and P. vivax, and 6.0% P. vivax monoinfection), while P. falciparum prevalence measured by RDT was just 0.2%. Prevalence varied by geography and was higher among older people and in those with outdoor exposure and travel. No difference was observed in either the prevalence or count of subclinical infection by time of year, indicating that even in low-endemicity areas, a reservoir of subclinical infection persists year-round. If low-density infections are shown to represent a significant source of transmission, identification of high-risk groups and locations may aid elimination efforts.