RAPID: Computational Modeling of Contact Density and Outbreak Estimation for COVID-19 Using Large-scale Geolocation Data from Mobile Devices

Engineering - The outbreak of COVID-19 has highlighted both the growing global risk of emerging pandemics and the urgent need for enhanced data-driven tools to identify, contain, and mitigate their effects, particularly in dense urban areas. There has been increasing attention given to locational data from smartphones as a way to enhance epidemiological modeling and predict outbreak progression, transmission, and exposure risk. When combined with artificial intelligence or machine learning algorithms, these high resolution data have the potential to vastly improve the granularity and precision of infection and hospitalization estimates. However, the use of locational data raises serious social, ethical, and technical challenges. Trade-offs between the potential public health benefits and the impacts for privacy and civil liberties have started to be debated in earnest within the context of the current pandemic, especially in light of increasing use of these data by private companies to promote targeted advertisements, evaluate retail consumer behavior, and model travel demand, among other applications. Furthermore, the use of these data in the public interest is undermined by an incomplete understanding of the representativeness and bias embedded in these data, particularly in relation to under-represented and vulnerable communities. What is not yet known is the extent of this bias in locational data and how the public health benefits of using these data diminish with spatial and temporal aggregation, which could help to minimize privacy concerns in the collection and use of these data. To address these questions, this project will develop computational models derived from large-scale locational data to (1) estimate the exposure density across a range of temporal (hourly, daily, etc.) and spatial (census block, neighborhood, etc.) scales, which will enable officials and researchers to evaluate and predict transmission rates in a particular area; (2) measure and evaluate the extent and effectiveness of social (physical) distancing efforts over time and comparatively within and across counties and cities, as well as understand the disparate impacts on vulnerable communities and populations; and (3) measure the extent of disease spread based on movement and travel patterns between neighborhoods and communities, which will support predictions of the spatial-temporal patterns of disease outbreak and identify ?at-risk? locations based on the aggregated mobility trajectories for areas were infections have been identified or suspected.

The project team is particularly concerned with how shelter-in-place orders and exposure risk disproportionately impact low-income and minority communities, and the implications of potential bias in locational data in assessing socioeconomic variations. The project will assess how the usefulness of these models for epidemiologists and public health officials varies with spatial aggregation (e.g. is neighborhood level data superior to county level data) and temporal aggregation (e.g. is a near-real-time model superior to daily or weekly timescales) and provide quantitative performance assessments that can be used for collective decision-making on the trade-offs between health benefits and privacy risk. Project outputs will be made open-source and publicly available as appropriate.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.