Simulating Individual Infection Risk over Big Trajectory Data

In response to pandemics, transmission simulation models, contact tracing algorithms, and risk assessment techniques are attracting extensive research attention. They are helpful in predicting epidemic transmission trends and mitigating the spread of infectious diseases. In this light, we study a new problem of Individual Infection Risk Assessment (IIRA) on the basis of fine-grained trajectory data. The problem aims to quantify the infection risk through an integral model that considers contact distance, contact period, cross infection, and time-related risk decaying effects. To answer the IIRA problem, a brute-force solution is designed, namely Exact Distance-based Risk Assessment (EDS) method, which is straightforward but time-consuming. Therefore, we further develop a Group Distance-based Risk Assessment (GDS) method and an approximate solution with optimization strategies (APS). Extensive experiments demonstrate the effectiveness and efficiency of these methods.