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Predictive models with a focus on different spatial-temporal scales benefitgovernments and healthcare systems to combat the COVID-19 pandemic. Here wepresent the conditional Long Short-Term Memory networks with Quantile output(condLSTM-Q), a well-performing model for making quantile predictions onCOVID-19 death tolls at the county level with a two-week forecast window. Thisfine geographical scale is a rare but useful feature in publicly availablepredictive models, which would especially benefit state-level officials tocoordinate resources within the state. The quantile predictions from condLSTM-Qinform people about the distribution of the predicted death tolls, allowingbetter evaluation of possible trajectories of the severity. Given thescalability and generalizability of neural network models, this model couldincorporate additional data sources with ease, and could be further developedto generate other useful predictions such as new cases or hospitalizationsintuitively.

condLSTM-Q: A novel deep learning model for predicting Covid-19 mortality in fine geographical Scale