SubEpiPredict: A tutorial-based primer and toolbox for fitting and forecasting growth trajectories using the ensemble n-sub-epidemic modeling framework
Gerardo Chowell , Sushma Dahal , Amanda Bleichrodt , Amna Tariq , James M. Hyman , Ruiyan Luo
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引用次数: 0
Abstract
An ensemble n-sub-epidemic modeling framework that integrates sub-epidemics to capture complex temporal dynamics has demonstrated powerful forecasting capability in previous works. This modeling framework can characterize complex epidemic patterns, including plateaus, epidemic resurgences, and epidemic waves characterized by multiple peaks of different sizes. In this tutorial paper, we introduce and illustrate SubEpiPredict, a user-friendly MATLAB toolbox for fitting and forecasting time series data using an ensemble n-sub-epidemic modeling framework. The toolbox can be used for model fitting, forecasting, and evaluation of model performance of the calibration and forecasting periods using metrics such as the weighted interval score (WIS). We also provide a detailed description of these methods including the concept of the n-sub-epidemic model, constructing ensemble forecasts from the top-ranking models, etc. For the illustration of the toolbox, we utilize publicly available daily COVID-19 death data at the national level for the United States. The MATLAB toolbox introduced in this paper can be very useful for a wider group of audiences, including policymakers, and can be easily utilized by those without extensive coding and modeling backgrounds.
在以前的工作中,一个集合 n 次疫情建模框架已显示出强大的预测能力,该框架整合了次疫情以捕捉复杂的时间动态。这种建模框架可以描述复杂的流行模式,包括高原、流行病复发和由多个大小不同的峰值组成的流行波。在这篇教程论文中,我们介绍并说明了 SubEpiPredict,这是一个用户友好型 MATLAB 工具箱,用于使用集合 n 次流行建模框架拟合和预测时间序列数据。该工具箱可用于模型拟合、预测,以及使用加权区间得分(WIS)等指标评估校准和预测期的模型性能。我们还提供了这些方法的详细说明,包括 n 次流行模型的概念、从排名靠前的模型中构建集合预测等。为了说明该工具箱,我们使用了公开的美国全国 COVID-19 每日死亡数据。本文介绍的 MATLAB 工具箱对包括政策制定者在内的更多受众非常有用,没有丰富编码和建模背景的人也能轻松使用。
期刊介绍:
Infectious Disease Modelling is an open access journal that undergoes peer-review. Its main objective is to facilitate research that combines mathematical modelling, retrieval and analysis of infection disease data, and public health decision support. The journal actively encourages original research that improves this interface, as well as review articles that highlight innovative methodologies relevant to data collection, informatics, and policy making in the field of public health.