Toward Accurate Spatiotemporal COVID-19 Risk Scores Using High-Resolution Real-World Mobility Data

As countries look toward re-opening of economic activities amidst the ongoing COVID-19 pandemic, ensuring public health has been challenging. While contact tracing only aims to track past infected users, one path safe reopening is develop reliable spatiotemporal risk scores indicate propensity disease. Existing works which aim at developing either rely on compartmental model-based reproduction numbers (which assume uniform population mixing) or coarse-grain spatial based number (R0) and macro-level density-based mobility statistics. Instead, in this article, we a Hawkes process-based technique assign relatively fine-grain temporal by leveraging high-resolution data cell-phone originated location signals. also depend factors specific an individual, including demography existing medical conditions, primary mode disease transmission via physical proximity contact. Therefore, focus density behaviour. We demonstrate efficacy developed simulation real-world data. Our results show that can provide useful insights facilitate re-opening.