Zhao Jing, Han Mengjie, Wang Zhenwu, Wan Benting
School of Business Administration, Xi'an Eurasia University, Yanta District, Xi'an, China.
School of Information and Engineering, Dalarna University, 79188 Falun, Sweden.
Stoch Environ Res Risk Assess. 2022;36(12):4185-4200. doi: 10.1007/s00477-022-02255-6. Epub 2022 Jun 23.
At the beginning of 2022 the global daily count of new cases of COVID-19 exceeded 3.2 million, a tripling of the historical peak value reported between the initial outbreak of the pandemic and the end of 2021. Aerosol transmission through interpersonal contact is the main cause of the disease's spread, although control measures have been put in place to reduce contact opportunities. Mobility pattern is a basic mechanism for understanding how people gather at a location and how long they stay there. Due to the inherent dependencies in disease transmission, models for associating mobility data with confirmed cases need to be individually designed for different regions and time periods. In this paper, we propose an autoregressive count data model under the framework of a generalized linear model to illustrate a process of model specification and selection. By evaluating a 14-day-ahead prediction from Sweden, the results showed that for a dense population region, using mobility data with a lag of 8 days is the most reliable way of predicting the number of confirmed cases in relative numbers at a high coverage rate. It is sufficient for both of the autoregressive terms, studied variable and conditional expectation, to take one day back. For sparsely populated regions, a lag of 10 days produced the lowest error in absolute value for the predictions, where weekly periodicity on the studied variable is recommended for use. Interventions were further included to identify the most relevant mobility categories. Statistical features were also presented to verify the model assumptions.
2022年初，全球新冠疫情每日新增病例数超过320万，这一数字是2021年底疫情爆发初期至2021年底报告的历史峰值的三倍。尽管已采取控制措施减少接触机会，但人际接触的气溶胶传播仍是疾病传播的主要原因。流动模式是理解人们如何在某一地点聚集以及停留多长时间的基本机制。由于疾病传播存在内在依赖性，将流动数据与确诊病例相关联的模型需要针对不同地区和时间段单独设计。在本文中，我们在广义线性模型框架下提出了一种自回归计数数据模型，以说明模型设定和选择的过程。通过评估瑞典提前14天的预测结果表明，对于人口密集地区，使用滞后8天的流动数据是在高覆盖率下以相对数量预测确诊病例数的最可靠方法。自回归项、研究变量和条件期望均取滞后一天就足够了。对于人口稀少地区，滞后10天的预测绝对值误差最低，建议对研究变量使用每周周期性数据。进一步纳入干预措施以确定最相关的流动类别。还展示了统计特征以验证模型假设。
