Bayesian validation framework for dynamic epidemic models

Complex models of infectious diseases are used to understand the transmission dynamics of the disease, project the course of an epidemic, predict the effect of interventions and/or provide information for power calculations of community level intervention studies. However, there have been relatively...

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Autores principales:	Sayan Dasgupta, Mia R. Moore, Dobromir T. Dimitrov, James P. Hughes
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Epidemiological model validation Markov Chain Monte Carlo Bayesian credible interval HIV transmission model Infectious and parasitic diseases RC109-216
Acceso en línea:	https://doaj.org/article/f1ade0d3f22e476e9f1303a98b830ced
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spelling	oai:doaj.org-article:f1ade0d3f22e476e9f1303a98b830ced2021-11-10T04:21:43ZBayesian validation framework for dynamic epidemic models1755-436510.1016/j.epidem.2021.100514https://doaj.org/article/f1ade0d3f22e476e9f1303a98b830ced2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S175543652100061Xhttps://doaj.org/toc/1755-4365Complex models of infectious diseases are used to understand the transmission dynamics of the disease, project the course of an epidemic, predict the effect of interventions and/or provide information for power calculations of community level intervention studies. However, there have been relatively few opportunities to rigorously evaluate the predictions of such models till now. Indeed, while there is a large literature on calibration (fitting model parameters) and validation (comparing model outputs to data) of complex models based on empirical data, the lack of uniformity in accepted criteria for such procedures for models of infectious diseases has led to simple procedures being prevalent for such steps. However, recently, several community level randomized trials of combination HIV intervention have been planned and/or initiated, and in each case, significant epidemic modeling efforts were conducted during trial planning which were integral to the design of these trials. The existence of these models and the (anticipated) availability of results from the related trials, provide a unique opportunity to evaluate the models and their usefulness in trial design. In this project, we outline a framework for evaluating the predictions of complex epidemiological models and describe experiments that can be used to test their predictions.Sayan DasguptaMia R. MooreDobromir T. DimitrovJames P. HughesElsevierarticleEpidemiological model validationMarkov Chain Monte CarloBayesian credible intervalHIV transmission modelInfectious and parasitic diseasesRC109-216ENEpidemics, Vol 37, Iss , Pp 100514- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Epidemiological model validation Markov Chain Monte Carlo Bayesian credible interval HIV transmission model Infectious and parasitic diseases RC109-216
spellingShingle	Epidemiological model validation Markov Chain Monte Carlo Bayesian credible interval HIV transmission model Infectious and parasitic diseases RC109-216 Sayan Dasgupta Mia R. Moore Dobromir T. Dimitrov James P. Hughes Bayesian validation framework for dynamic epidemic models
description	Complex models of infectious diseases are used to understand the transmission dynamics of the disease, project the course of an epidemic, predict the effect of interventions and/or provide information for power calculations of community level intervention studies. However, there have been relatively few opportunities to rigorously evaluate the predictions of such models till now. Indeed, while there is a large literature on calibration (fitting model parameters) and validation (comparing model outputs to data) of complex models based on empirical data, the lack of uniformity in accepted criteria for such procedures for models of infectious diseases has led to simple procedures being prevalent for such steps. However, recently, several community level randomized trials of combination HIV intervention have been planned and/or initiated, and in each case, significant epidemic modeling efforts were conducted during trial planning which were integral to the design of these trials. The existence of these models and the (anticipated) availability of results from the related trials, provide a unique opportunity to evaluate the models and their usefulness in trial design. In this project, we outline a framework for evaluating the predictions of complex epidemiological models and describe experiments that can be used to test their predictions.
format	article
author	Sayan Dasgupta Mia R. Moore Dobromir T. Dimitrov James P. Hughes
author_facet	Sayan Dasgupta Mia R. Moore Dobromir T. Dimitrov James P. Hughes
author_sort	Sayan Dasgupta
title	Bayesian validation framework for dynamic epidemic models
title_short	Bayesian validation framework for dynamic epidemic models
title_full	Bayesian validation framework for dynamic epidemic models
title_fullStr	Bayesian validation framework for dynamic epidemic models
title_full_unstemmed	Bayesian validation framework for dynamic epidemic models
title_sort	bayesian validation framework for dynamic epidemic models
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/f1ade0d3f22e476e9f1303a98b830ced
work_keys_str_mv	AT sayandasgupta bayesianvalidationframeworkfordynamicepidemicmodels AT miarmoore bayesianvalidationframeworkfordynamicepidemicmodels AT dobromirtdimitrov bayesianvalidationframeworkfordynamicepidemicmodels AT jamesphughes bayesianvalidationframeworkfordynamicepidemicmodels
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Bayesian validation framework for dynamic epidemic models

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