  With the outbreak of the COVID-19 pandemic, various studies have focused on
predicting the trajectory and risk factors of the virus and its variants.
Building on previous work that addressed this problem using genetic and
epidemiological data, we introduce a method, Geo Score, that also incorporates
geographic, socioeconomic, and demographic data to estimate infection and
mortality risk by region and time. We employ gradient descent to find the
optimal weights of the factors' significance in determining risk. Such
spatiotemporal risk prediction is important for informed public health
decision-making so that individuals are aware of the risks of travel during an
epidemic or pandemic, and, perhaps more importantly, so that policymakers know
how to triage limited resources during a crisis. We apply our method to New
York City COVID-19 data from 2020, predicting ZIP code-level COVID-19 risk for
2021.
