We introduce a novel methodology for predicting the time evolution of the number of individuals in a given country reported to be infected with SARS-CoV-2. This methodology, which is based on the synergy of explicit mathematical formulae and deep learning networks, yields algorithms whose input is only the existing data in the given country of the accumulative number of individuals who are reported to be infected. The analytical formulae involve several constant parameters that were determined from the available data using an error-minimizing algorithm. The same data were also used for the training of a bidirectional long short-term memory network. We applied the above methodology to the epidemics in Italy, Spain, France, Germany, USA and Sweden. The significance of these results for evaluating the impact of easing the lockdown measures is discussed.

中文翻译：

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用于预测报告感染 SARS-CoV-2 的人数的数学模型和深度学习

我们引入了一种新的方法来预测特定国家报告感染 SARS-CoV-2 的人数的时间演变。这种方法基于明确的数学公式和深度学习网络的协同作用，产生的算法的输入仅是给定国家中据报告感染的累计人数的现有数据。分析公式涉及几个常数参数，这些参数是使用误差最小化算法根据可用数据确定的。相同的数据也用于训练双向长短期记忆网络。我们将上述方法应用于意大利、西班牙、法国、德国、美国和瑞典的疫情。讨论了这些结果对于评估放松封锁措施的影响的重要性。