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.

中文翻译：

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在危险区域：U-Net驱动的分位数回归可以通过污染物颗粒物和卫星图像预测高风险的SARS-CoV-2地区

自COVID-19爆发以来，决策者一直依靠非药物干预来控制爆发。由于空气污染是潜在的传播媒介，因此需要将其纳入干预策略中。我们提出了一个基于U-net的分位数回归模型，该模型基于易于获取的卫星图像来预测$ PM_ {2.5} $的空气污染。我们证明了我们的方法可以重构地面数据上的$ PM_ {2.5} $浓度，并预测其合理的$ PM_ {2.5} $值及其空间分布，即使在没有污染数据的地方也是如此。对PM_ {2.5} $特性的这种预测可以为建议的公共政策策略提供关键建议，以减少COVID-19的传播和致死率。