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

TL;DR

This paper introduces a U-net driven quantile regression model that predicts ground-level PM2.5 pollution from satellite imagery, aiding COVID-19 intervention strategies by identifying high-risk regions.

Contribution

The novel approach combines U-net and quantile regression to accurately estimate PM2.5 levels from satellite data, including in areas lacking ground measurements.

Findings

Model accurately reconstructs ground-truth PM2.5 data.

Predicts PM2.5 distribution in unmonitored locations.

Potential to inform COVID-19 public health policies.

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 $PM_{2.5}$ air pollution based on easily obtainable satellite imagery. We demonstrate that our approach can reconstruct $PM_{2.5}$ concentrations on ground-truth data and predict reasonable $PM_{2.5}$ values with their spatial distribution, even for locations where pollution data is unavailable. Such predictions of $PM_{2.5}$ characteristics could crucially advise public policy strategies geared to reduce the transmission of and lethality of COVID-19.