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

arXiv: https://arxiv.org/abs/2105.02406 · HTML: https://mlsanity.com/paper/2105.02406/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 6 May 2021.

## Builds on (most cited first, 2 of 2)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks](https://mlsanity.com/paper/1705.03820/) (2017)

## Abstract (arXiv)

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.