Log In Sign Up

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

by   Jacquelyn Shelton, et al.

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.


page 3

page 4

page 5


Investigating the Relationship Between Air Quality and COVID-19 Transmission

It is hypothesized that short-term exposure to air pollution may influen...

Ground Control to Major Tom: the importance of field surveys in remotely sensed data analysis

In this project, we build a modular, scalable system that can collect, s...

Detecting Crop Burning in India using Satellite Data

Crop residue burning is a major source of air pollution in many parts of...

Cyclone preparedness strategies for regional power transmission systems in data-scarce coastal regions of India

As the frequency and intensity of tropical cyclones, and the degree of u...

Quantitative evaluation of regulatory policies for reducing deforestation using the bent-cable regression model

Reducing and redressing the effects of deforestation is a complex public...

How do mobility restrictions and social distancing during COVID-19 affect the crude oil price?

We develop an air mobility index and use the newly developed Apple's dri...