论文标题
序列时间窗口学习，近似贝叶斯计算：对流行病预测的应用
Sequential time-window learning with approximate Bayesian computation: an application to epidemic forecasting
论文作者
论文摘要
共vid-19的长时间大流行允许感染和死亡率的多次爆发，即所谓的流行波。这种复杂的行为不再可以通过简单的隔室模型来处理，并且需要更复杂的数学技术来分析流行病数据并产生可靠的预测。在这项工作中，我们提出了一个框架，用于通过将数据分开以分别分析的连续时间窗口进行分析来分析复杂的动态系统。我们通过近似贝叶斯计算（ABC）算法为每个时间窗口拟合参数，并且为一个窗口获得的参数的后验分布用作下一个窗口的先前分布。这种贝叶斯学习方法通过有关多个国家的Covid-19病例的数据进行了测试，并被证明可以改善ABC的绩效并产生良好的短期预测。
The long duration of the COVID-19 pandemic allowed for multiple bursts in the infection and death rates, the so-called epidemic waves. This complex behavior is no longer tractable by simple compartmental model and requires more sophisticated mathematical techniques for analyzing epidemic data and generating reliable forecasts. In this work, we propose a framework for analyzing complex dynamical systems by dividing the data in consecutive time-windows to be separately analyzed. We fit parameters for each time-window through an Approximate Bayesian Computation (ABC) algorithm, and the posterior distribution of parameters obtained for one window is used as the prior distribution for the next window. This Bayesian learning approach is tested with data on COVID-19 cases in multiple countries and is shown to improve ABC performance and to produce good short-term forecasting.
