Using a partial differential equation with Google Mobility data to predict COVID-19 in Arizona
Using a partial differential equation with Google Mobility data to predict COVID-19 in Arizona
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DOI：
10.3934/mbe.2020266
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发表时间：
2020-01-01
影响因子：
2.6
通讯作者：
Yamamoto, Nao
中科院分区：
文献类型：
--
作者：
Wang, Haiyan;Yamamoto, Nao
COVID-19的爆发扰乱了世界上许多人的生活。美国亚利桑那州成为该国最新的COVID-19热点之一。对COVID-19病例的准确预测将有助于各国政府采取必要措施，并说服更多人采取个人预防措施来对抗病毒。由于涉及许多人为因素，因此难以准确预测COVID-19病例。本文旨在借助Google社区移动报告中的人类活动数据，为COVID-19病例提供一个预测模型。为了实现这一目标，我们开发了一个特定的偏微分方程（PDE），并使用来自美国亚利桑那州县级纽约的COVID-19数据进行了验证。该模型描述了亚利桑那州县群之间的跨界传播和人类活动对COVID-19传播的综合影响。结果表明，该模型的预测精度在94%以上。此外，我们研究个人预防措施（如戴口罩及在地方层面对COVID-19病例实行社交距离）的成效。本地化的分析结果可用于帮助减缓COVID-19在亚利桑那州的传播。据我们所知，这项工作是首次尝试将PDE模型应用于Google社区移动报告的COVID-19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 activities 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.