论文标题
一个深入学习模型，用于评估和预测锁定政策对Covid-19情况的影响
A deep-learning model for evaluating and predicting the impact of lockdown policies on COVID-19 cases
论文作者
论文摘要
为了减少Covid-19的影响，大多数国家大多数国家都实施了几种反例，以控制病毒蔓延，包括学校和边境关闭，关闭公共交通和工作场所以及对聚会的限制。在这项研究工作中，我们提出了一个深入学习预测模型，用于评估和预测各种锁定政策对每日Covid-19案件的影响。这是通过具有类似锁定政策的第一个聚类国家来实现的，然后根据每个集群中这些国家的日常案例以及描述其锁定政策的数据训练预测模型。训练模型后，它可以用于评估与锁定政策相关的几种方案，并研究其对预测的Covid案件的影响。我们在卡塔尔作为用例进行的评估实验表明，所提出的方法实现了竞争性的预测准确性。此外，我们的发现强调，尤其是在学校和边境开放方面的提升限制将导致研究期间的案件数量大幅增加。
To reduce the impact of COVID-19 pandemic most countries have implemented several counter-measures to control the virus spread including school and border closing, shutting down public transport and workplace and restrictions on gathering. In this research work, we propose a deep-learning prediction model for evaluating and predicting the impact of various lockdown policies on daily COVID-19 cases. This is achieved by first clustering countries having similar lockdown policies, then training a prediction model based on the daily cases of the countries in each cluster along with the data describing their lockdown policies. Once the model is trained, it can used to evaluate several scenarios associated to lockdown policies and investigate their impact on the predicted COVID cases. Our evaluation experiments, conducted on Qatar as a use case, shows that the proposed approach achieved competitive prediction accuracy. Additionally, our findings highlighted that lifting restrictions particularly on schools and border opening would result in significant increase in the number of cases during the study period.
