COVID-19 is one of the largest spreading pandemic diseases faced in the documented history of mankind. Human to human interaction is the most prolific method of transmission of this virus. Nations all across the globe started to issue stay at home orders and mandating to wear masks or a form of face-covering in public to minimize the transmission by reducing contact between majority of the populace. The epidemiological models used in the literature have considerable drawbacks in the assumption of homogeneous mixing among the populace. Moreover, the effect of mitigation strategies such as mask mandate and stay at home orders cannot be efficiently accounted for in these models. In this work, we propose a novel data driven approach using LSTM (Long Short Term Memory) neural network model to form a functional mapping of daily new confirmed cases with mobility data which has been quantified from cell phone traffic information and mask mandate information. With this approach no pre-defined equations are used to predict the spread, no homogeneous mixing assumption is made, and the effect of mitigation strategies can be accounted for. The model learns the spread of the virus based on factual data from verified resources. A study of the number of cases for the state of New York (NY) and state of Florida (FL) in the USA are performed using the model. The model correctly predicts that with higher mobility the cases would increase and vice-versa. It further predicts the rate of new cases would see a decline if a mask mandate is administered. Both these predictions are in agreement with the opinions of leading medical and immunological experts. The model also predicts that with the mask mandate option even a higher mobility would reduce the daily cases than lower mobility without masks. We additionally generate results and provide RMSE (Root Mean Square Error) comparison with ARIMA based model of other published work for Italy, Turkey, Australia, Brazil, Canada, Egypt, Japan, and the UK. Our model reports lower RMSE than the ARIMA based work for all eight countries which were tested. The proposed model would provide administrations with a quantifiable basis of how mobility, mask mandates are related to new confirmed cases; so far no epidemiological models provide that information. It gives fast and relatively accurate prediction of the number of cases and would enable the administrations to make informed decisions and make plans for mitigation strategies and changes in hospital resources.

COVID-19 是人类有记录以来面临的最大的传播性流行病之一。人与人之间的互动是这种病毒传播最多的方法。全球各国开始发布居家令，并要求在公共场合佩戴口罩或某种形式的面罩，以通过减少大多数民众之间的接触来最大限度地减少传播。文献中使用的流行病学模型在假设人群中的均匀混合方面存在相当大的缺陷。此外，在这些模型中无法有效地解释缓解策略的影响，例如口罩强制令和居家令。在这项工作中，我们提出了一种新的数据驱动方法，使用 LSTM（长短期记忆）神经网络模型来形成每日新增确诊病例与移动数据的功能映射，这些移动数据已从手机流量信息和掩码授权信息中量化。通过这种方法，没有使用预先定义的方程来预测扩散，没有做出均匀混合假设，并且可以考虑缓解策略的影响。该模型根据来自经过验证的资源的事实数据来了解病毒的传播。使用该模型对美国纽约州 (NY) 和佛罗里达州 (FL) 的病例数进行了研究。该模型正确地预测，随着流动性的提高，病例会增加，反之亦然。它进一步预测，如果实施戴口罩令，新病例的发生率将会下降。这两项预测都与领先的医学和免疫学专家的意见一致。该模型还预测，如果使用口罩强制选项，即使更高的流动性也会比没有口罩的更低流动性减少日常病例。我们还生成结果并提供与基于 ARIMA 的意大利、土耳其、澳大利亚、巴西、加拿大、埃及、日本和英国的其他已发表作品的模型的 RMSE（均方根误差）比较。对于所有接受测试的八个国家，我们的模型报告的 RMSE 均低于基于 ARIMA 的工作。拟议的模型将为主管部门提供一个可量化的基础，说明流动性、口罩任务与新确诊病例的关系；迄今为止，还没有流行病学模型提供该信息。