The early detection of the coronavirus disease 2019 (COVID-19) outbreak is important to save people’s lives and restart the economy quickly and safely. People’s social behavior, reflected in their mobility data, plays a major role in spreading the disease. Therefore, we used the daily mobility data aggregated at the county level beside COVID-19 statistics and demographic information for short-term forecasting of COVID-19 outbreaks in the United States. The daily data are fed to a deep learning model based on Long Short-Term Memory (LSTM) to predict the accumulated number of COVID-19 cases in the next two weeks. A significant average correlation was achieved (r=0.83 (p = 0.005)) between the model predicted and actual accumulated cases in the interval from August 1, 2020 until January 22, 2021. The model predictions had r > 0.7 for 87% of the counties across the United States. A lower correlation was reported for the counties with total cases of <1000 during the test interval. The average mean absolute error (MAE) was 605.4 and decreased with a decrease in the total number of cases during the testing interval. The model was able to capture the effect of government responses on COVID-19 cases. Also, it was able to capture the effect of age demographics on the COVID-19 spread. It showed that the average daily cases decreased with a decrease in the retiree percentage and increased with an increase in the young percentage. Lessons learned from this study not only can help with managing the COVID-19 pandemic but also can help with early and effective management of possible future pandemics. The code used for this study was made publicly available on https://github.com/Murtadha44/covid-19-spread-risk.

及早发现 2019 年冠状病毒病 (COVID-19) 爆发对于挽救人们的生命并快速安全地重启经济非常重要。人们的移动数据所反映的社会行为在疾病传播中发挥着重要作用。因此，除了 COVID-19 统计数据和人口统计信息之外，我们还使用县级汇总的每日流动数据来对美国的 COVID-19 疫情进行短期预测。每日数据被输入基于长短期记忆 (LSTM) 的深度学习模型，以预测未来两周内 COVID-19 病例的累计数量。从 2020 年 8 月 1 日到 2021 年 1 月 22 日期间，模型预测的病例数与实际累积病例数之间实现了显着的平均相关性 ( r = 0.83 ( p = 0.005 ))。模型预测 87 % 的病例数r > 0.7美国各地的县。测试间隔期间病例总数 <1000 例的县报告相关性较低。平均绝对误差 (MAE) 为 605.4，并随着测试间隔期间病例总数的减少而减小。该模型能够捕捉政府应对措施对 COVID-19 病例的影响。此外，它还能够捕捉年龄人口统计数据对 COVID-19 传播的影响。结果表明，随着退休人员比例的下降，日均病例数减少，随着年轻人比例的增加，日均病例数增加。从这项研究中汲取的经验教训不仅有助于管理 COVID-19 大流行，还有助于及早有效地管理未来可能发生的大流行。本研究使用的代码已在 https://github.com/Murtadha44/covid-19-spread-risk 上公开发布。