An integrated software platform to analyze the role of human mobility in vector-borne disease transmission

Human mobility plays a crucial part in the transmission of vector-borne diseases. The relationship between human mobility and disease transmission is a complex phenomenon since it involves the dynamics of human movement, disease transmission, and the interactions between humans and their environment. Several tools to support such complex analyses exist; however, they focus on one or a few aspects of mobility analysis and visualization, leading to tedious and potentially error-prone work when utilizing them across multiple platforms. To address this challenge, this study aimed to 1) develop the Mobility Analysis for Vector-borne Disease platform (MoVe), which integrates visualization, spatial analysis, and agent-based simulation functions, and 2) demonstrate the effectiveness of MoVe.
The MoVe platform consists of spatial analysis and agent-based simulation components. The spatial analysis component extracts essential information with regard to human mobility. The agent-based simulation function is designed to explore the effects of mobility patterns on the transmission of vector-borne diseases. It features a novel individual-level probabilistic risk estimation, which is easily interpretable, and also facilitates the examination of the effects of disease control strategies through what-if scenario analyses.
To demonstrate MoVe’s effectiveness, observational case studies are presented investigating the role of human mobility in malaria transmission in Tha Song Yang District, Tak Province, Thailand, where cross-border importation plays an important role. The participants in the observational case studies were randomly selected from the local population. MoVe was used to analyze the mobility data collected via a smartphone application installed on the participants' smartphones over a one-year period.
Findings from the case studies indicated that demographics and seasonal factors were crucial in determining patterns of cross-border mobility. During the dry season (August-December), it was common for farmers, laborers, and unemployed participants to cross the Thai-Myanmar border. However, only farmers crossed the border in the wet (May-July) season. This demonstrates the ability of MoVe to detect variations in mobility patterns.
Utilizing the extracted mobility patterns from the spatial analysis function, the simulation in MoVe investigated the role of cross-border mobility in malaria transmission in Thailand. It revealed that cross-border mobility among Thais, primarily farmers, significantly contributes to malaria infections in Thailand during the wet season. However, during the dry season, malaria infections in Thailand are largely driven by the cross-border mobility of infected short-term migrants from Myanmar. These findings indicate that the primary challenge hindering malaria elimination in Thailand, particularly at the Thai-Myanmar border, is the cross-border mobility of different populations.