A Location-Based Client-Server Framework for Assessing Personal Exposure to the Transmission Risks of Contagious Diseases

Abstract

Human mobility is an important risk factor affecting contagious disease transmission. Therefore, understanding spatial behaviors and interactions among individuals is a fundamental issue. Past studies using high-resolution human contacts data with sequential location data from global positioning systems (GPS) receivers have captured spatial-temporal heterogeneity and daily contact patterns among individuals. However, how to measure effectively personalized exposure to the risk of contagious disease transmission is still under development. The purpose of the study is to establish a location-based client-server framework for assessing personalized exposure to the transmission risk of contagious disease. The location-based framework consists of two major components: one is a client-side smartphone-based risk assessment module. We developed an Android application for collecting course-attending records and real-time location data for displaying personalized exposure scores. The other component is a server-side epidemic simulation model. The simulation model calculated the personalized exposure score based on GPS logs and individual mobility data from the client-side Android application. We used National Taiwan University (NTU) main campus as a pilot study to demonstrate the feasibility of the framework. The records of students attending courses and GPS logs were used for capturing mobility of students around the campus. We then generated a space-time mobility network based on individual mobility trajectories. Based on the epidemic simulation model with individual mobility network, each student who uses his/her smartphone as a personalized platform, can understand the epidemic progression and make better decisions, such as wearing a face mask or reducing the contact frequency, based on personalized exposure scores from the server-side computation. The proposed location-based framework presents complex interactions among personal risk reception, behavioral changes, and epidemic progression. More scenarios can be implemented in future studies for quantifying the effects of risk reception or behavioral changes with epidemic progression.