SubEpiPredict:A tutorial-based primer and toolbox for fitting and forecasting growth trajectories using the ensemble n-sub-epidemic modeling framework  

作　　者：Gerardo Chowell Sushma Dahal Amanda Bleichrodt Amna Tariq James M.Hyman Ruiyan Luo 

机构地区：[1]Department of Population Health Sciences,School of Public Health,Georgia State University,Atlanta,GA,USA [2]Department of International Epidemiology and Population Studies,Fogarty International Center,National Institutes of Health,Bethesda,MD,USA [3]Department of Pediatrics,School of Medicine,Stanford University,Palo Alto,CA,USA [4]Department of Mathematics,Center for Computational Science,Tulane University,New Orleans,LA,USA

出　　处：《Infectious Disease Modelling》2024年第2期411-436,共26页传染病建模（英文）

基　　金：G.C.is partially supported from NSF grants 2125246 and 2026797 and R01 GM 130900.

摘　　要：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.

关 键 词：n-Sub-epidemic model Forecasting MATLAB Sub-epidemics Phenomenological models Performance metrics Model evaluation 

分 类 号：R181.3[医药卫生—流行病学]