In the Danger Zone: U-Net Driven Quantile Regression can Predict High-risk SARS-CoV-2 Regions via Pollutant Particulate Matter and Satellite Imagery

Abstract

Since the outbreak of COVID-19 policy makers have been relying upon non-pharmacological interventions to control the outbreak. With air pollution as a potential transmission vector there is need to include it in intervention strategies. We propose a U-net driven quantile regression model to predict P M 2.5 air pollution based on easily obtainable satellite imagery. We demonstrate that our approach can reconstruct P M 2.5 concentrations on ground-truth data and predict reasonable P M 2.5 values with their spatial distribution, even for locations where pollution data is unavailable. Such predictions of P M 2.5 characteristics could crucially advise public policy strategies geared to reduce the transmission of and lethality of COVID-19.