论文标题
使用Google移动性数据的部分微分方程来预测亚利桑那州的Covid-19
Using A Partial Differential Equation with Google Mobility Data to Predict COVID-19 in Arizona
论文作者
论文摘要
Covid-19的爆发破坏了世界上许多人的生活。亚利桑那州在美国，是该国最新的Covid-19热点之一。对COVID-19案件的准确预测将有助于政府采取必要的措施，并说服更多人采取个人预防措施来应对病毒。由于涉及许多人为因素，因此很难准确预测Covid-19案例。本文旨在借助Google社区流动性报告的人类活动数据，为Covid-19案例提供预测模型。为了实现这一目标，开发和验证了特定的部分微分方程（PDE），并通过美国亚利桑那州纽约时报的COVID-19数据进行了验证。该纽约时报来自美国亚利桑那州的县一级。该拟议模型描述了亚利桑那州县群集在亚利桑那州和人类活动中对COVID-19的传播的跨界差异的综合影响。结果表明，该模型的预测准确性是可以接受的（94 \％以上）。此外，我们研究了个人预防措施的有效性，例如戴着口罩以及在当地一级的COVID-19案件上实践社会疏远。局部分析结果可用于帮助减缓19日在亚利桑那州的蔓延。据我们所知，这项工作是首次尝试使用Google社区移动性报告将PDE模型应用于COVID-19的预测。
The outbreak of COVID-19 disrupts the life of many people in the world. The state of Arizona in the U.S. emerges as one of the country's newest COVID-19 hot spots. Accurate forecasting for COVID-19 cases will help governments to implement necessary measures and convince more people to take personal precautions to combat the virus. It is difficult to accurately predict the COVID-19 cases due to many human factors involved. This paper aims to provide a forecasting model for COVID-19 cases with the help of human activity data from the Google Community Mobility Reports. To achieve this goal, a specific partial differential equation (PDE) is developed and validated with the COVID-19 data from the New York Times at the county level in the state of Arizona in the U.S. The proposed model describes the combined effects of transboundary spread among county clusters in Arizona and human actives on the transmission of COVID-19. The results show that the prediction accuracy of this model is well acceptable (above 94\%). Furthermore, we study the effectiveness of personal precautions such as wearing face masks and practicing social distancing on COVID-19 cases at the local level. The localized analytical results can be used to help to slow the spread of COVID-19 in Arizona. To the best of our knowledge, this work is the first attempt to apply PDE models on COVID-19 prediction with the Google Community Mobility Reports.
