A primer on stable parameter estimation and forecasting in epidemiology by a problem-oriented regularized least squares algorithm

查看全文 作　　者：Alexandra [1]Smirnova;Gerardo [2,3]Chowell

机构地区：[1]Department of Mathematics and Statistics,Georgia State University,Atlanta,USA;[2]School of Public Health,Georgia State University,Atlanta,USA;[3]Division of International Epidemiology and Population Studies,Fogarty International Center,National Institutes of Health,Bethesda,MD,USA

出　　处：《Infectious Disease Modelling》索引2017年第2卷第2期,共8页

基　　金：Dr.Gerardo Chowell acknowledges financial support from NSF grant 1414374 as part of the joint NSF-NIH-USDA Ecology and Evolution of Infectious Diseases program;UK Biotechnology and Biological Sciences Research Council grant BB/M008894/1 and NSF grant 1610429. 摘　　要：Public health officials are increasingly recognizing the need to develop disease-forecasting systems to respond to epidemic and pandemic outbreaks.For instance,simple epidemic models relying on a small number of parameters can play an important role in characterizing epidemic growth and generating short-term epidemic forecasts.In the absence of reliable information about transmission mechanisms of emerging infectious diseases,phenomenological models are useful to characterize epidemic growth patterns without the need to explicitly model transmission mechanisms and the natural history of the disease.In this article,our goal is to discuss and illustrate the role of regularization methods for estimating parameters and generating disease forecasts using the generalized Richards model in the context of the 2014e15 Ebola epidemic in West Africa. 关键词：Generalized Richards model Parameter estimation Regularization methods Epidemic forecasting EBOLA

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A primer on stable parameter estimation and forecasting in epidemiology by a problem-oriented regularized least squares algorithm

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