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 PM2.5 air pollution based on easily obtainable satellite imagery. We demonstrate that our approach can reconstruct PM2.5 concentrations on ground-truth data and predict reasonable PM2.5 values with their spatial distribution, even for locations where pollution data is unavailable. Such predictions of PM2.5 characteristics could crucially advise public policy strategies geared to reduce the transmission of and lethality of COVID-19.