论文标题
全球短期预测Covid-19案件
Global Short-Term Forecasting of Covid-19 Cases
论文作者
论文摘要
全球医疗服务的COVID-19案件不断增长。因此，准确的短期预测对于支持国家级政策制定至关重要。国家采取的战略对抗大流行的策略各不相同，从而产生了有关实际案件数量的不同不确定性水平。因此，考虑数据的层次结构并适应外部变异性是基本的。我们介绍了一个新的建模框架，以非常准确地描述大流行的过程，并为世界上每个国家提供短期的每日预测。我们表明，我们的模型可产生高度准确的预测，最多六天，并根据最近的事件将估计的模型组件用于聚类国家。我们介绍了统计新颖性，以建模自回归参数作为时间的函数，提高预测能力和灵活性以适应每个国家。我们的模型还可以用于预测死亡人数，研究协变量（例如锁定政策）的影响，并对国家 /地区较小地区的预测产生预测。因此，它对全球计划和决策具有很大的影响。我们不断更新预测，并通过在线闪亮的仪表板向世界上任何国家免费获得所有结果。
The continuously growing number of COVID-19 cases pressures healthcare services worldwide. Accurate short-term forecasting is thus vital to support country-level policy making. The strategies adopted by countries to combat the pandemic vary, generating different uncertainty levels about the actual number of cases. Accounting for the hierarchical structure of the data and accommodating extra-variability is therefore fundamental. We introduce a new modelling framework to describe the course of the pandemic with great accuracy, and provide short-term daily forecasts for every country in the world. We show that our model generates highly accurate forecasts up to six days ahead, and use estimated model components to cluster countries based on recent events. We introduce statistical novelty in terms of modelling the autoregressive parameter as a function of time, increasing predictive power and flexibility to adapt to each country. Our model can also be used to forecast the number of deaths, study the effects of covariates (such as lockdown policies), and generate forecasts for smaller regions within countries. Consequently, it has strong implications for global planning and decision making. We constantly update forecasts and make all results freely available to any country in the world through an online Shiny dashboard.
