Predictive models with a focus on different spatial-temporal scales benefit
governments and healthcare systems to combat the COVID-19 pandemic. Here we
present the conditional Long Short-Term Memory networks with Quantile output
(condLSTM-Q), a well-performing model for making quantile predictions on
COVID-19 death tolls at the county level with a two-week forecast window. This
fine geographical scale is a rare but useful feature in publicly available
predictive models, which would especially benefit state-level officials to
coordinate resources within the state. The quantile predictions from condLSTM-Q
inform people about the distribution of the predicted death tolls, allowing
better evaluation of possible trajectories of the severity. Given the
scalability and generalizability of neural network models, this model could
incorporate additional data sources with ease, and could be further developed
to generate other useful predictions such as new cases or hospitalizations
intuitively.

该研究提出了基于条件长短期记忆网络和分位数输出的预测模型 (condLSTM-Q)，该模型在不同空间 - 时间尺度下能够准确预测 COVID-19 疫情的死亡人数。该模型的精细地理尺度可以帮助州级政府协调资源，同时其可扩展性和通用性也保证了其广泛适用性。