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

• URL: http://arxiv.org/abs/2105.02406v1
• Date: Thu, 6 May 2021 02:50:54 GMT
• Title: In the Danger Zone: U-Net Driven Quantile Regression can Predict High-risk SARS-CoV-2 Regions via Pollutant Particulate Matter and Satellite Imagery
• Authors: Jacquelyn Shelton, Przemyslaw Polewski and Wei Yao
• Abstract summary: We propose a U-net driven quantile regression model to predict $PM_2.5$ air pollution based on easily obtainable satellite imagery. Such predictions could crucially advise public policy strategies geared to reduce the transmission of and lethality of COVID-19.
• Score: 0.5929956715430166
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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