Determining the Impact of Human Mobility Patterns on Transmissions of SARS-CoV-2 (National Institutes of Health) 2022-2026

Under the unprecedented SARS-CoV-2 pandemic, a major mitigation effort has focused on non-pharmaceutical interventions, including masking requirements, shelter-in-place orders, border closures, school restrictions, and business interruptions. The effectiveness of these interventions for infection control depends on adherence as well as their specific impact: for instance, travel restrictions may reduce the geographic dispersion of infections but do not interrupt local transmission. Therefore, mobility-based analytics that link human movement patterns to spatiotemporal trends in SARS-CoV-2 are required to understand whether the interventions exhibit their intended effect. We aim to build a publicly accessible mobility data platform and to investigate the relationship between human movement patterns and SARS-CoV-2 transmission through three specific aims: (1) Fuse, integrate, and analyze passively collected mobile device locations and public health records for the two application areas: Nigeria and South Africa; (2) Design and reliably train an agent-based mobility model for multimodal and multi-dimensional travel behavior (i.e., across all modes, destinations, departure times, and routes) before and during the SARS-CoV-2 pandemic; and (3) Develop an epidemiological layer for the agent-based mobility model and empirically calibrate the model to spatiotemporal data of mobility and public health records.