The COrona VIrus Disease (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) has resulted in a challenging number of infections and deaths worldwide. In order to combat the pandemic, several countries worldwide enforced mitigation measures in the forms of lockdowns, social distancing, and disinfection measures. In an effort to understand the dynamics of this disease, we propose a Long Short-Term Memory (LSTM) based model. We train our model on more than four months of cumulative COVID-19 cases and deaths. Our model can be adjusted based on the parameters in order to provide predictions as needed. We provide results at both the country and county levels. We also perform a quantitative comparison of mitigation measures in various counties in the United States based on the rate of difference of a short and long window parameter of the proposed LSTM model. The analyses provided by our model can provide valuable insights based on the trends in the rate of infections and deaths. This can also be of help for countries and counties deciding on mitigation and reopening strategies. We believe that the results obtained from the proposed method will contribute to societal benefits for a current global concern.

由严重急性呼吸综合征冠状病毒 2 (SARS-CoV2) 引起的冠状病毒病 (COVID-19) 大流行已在全球范围内造成大量感染和死亡。为了抗击疫情，世界多个国家采取了封锁、社交距离和消毒措施等缓解措施。为了了解这种疾病的动态，我们提出了一种基于长短期记忆（LSTM）的模型。我们根据四个多月的累计 COVID-19 病例和死亡数据来训练我们的模型。我们的模型可以根据参数进行调整，以便根据需要提供预测。我们提供国家和县级的结果。我们还根据所提出的 LSTM 模型的短窗和长窗参数的差异率，对美国各县的缓解措施进行了定量比较。我们的模型提供的分析可以根据感染率和死亡率的趋势提供有价值的见解。这也有助于各国和县决定缓解和重新开放战略。我们相信，从所提出的方法中获得的结果将为当前全球关注的问题带来社会效益。