The effect of population size for pathogen transmission on prediction of COVID-19 spread

Abstract Extreme public health interventions play a critical role in mitigating the local and global prevalence and pandemic potential. Here, we use population size for pathogen transmission to measure the intensity of public health interventions, which is a key characteristic variable for nowcastin...

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Autores principales:	Xuqi Zhang, Haiqi Liu, Hanning Tang, Mei Zhang, Xuedong Yuan, Xiaojing Shen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/72d1c7cca77d45d4a32962381152bc5e
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spelling	oai:doaj.org-article:72d1c7cca77d45d4a32962381152bc5e2021-12-02T17:19:16ZThe effect of population size for pathogen transmission on prediction of COVID-19 spread10.1038/s41598-021-97578-92045-2322https://doaj.org/article/72d1c7cca77d45d4a32962381152bc5e2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97578-9https://doaj.org/toc/2045-2322Abstract Extreme public health interventions play a critical role in mitigating the local and global prevalence and pandemic potential. Here, we use population size for pathogen transmission to measure the intensity of public health interventions, which is a key characteristic variable for nowcasting and forecasting of COVID-19. By formulating a hidden Markov dynamic system and using nonlinear filtering theory, we have developed a stochastic epidemic dynamic model under public health interventions. The model parameters and states are estimated in time from internationally available public data by combining an unscented filter and an interacting multiple model filter. Moreover, we consider the computability of the population size and provide its selection criterion. With applications to COVID-19, we estimate the mean of the effective reproductive number of China and the rest of the globe except China (GEC) to be 2.4626 (95% CI: 2.4142–2.5111) and 3.0979 (95% CI: 3.0968–3.0990), respectively. The prediction results show the effectiveness of the stochastic epidemic dynamic model with nonlinear filtering. The hidden Markov dynamic system with nonlinear filtering can be used to make analysis, nowcasting and forecasting for other contagious diseases in the future since it helps to understand the mechanism of disease transmission and to estimate the population size for pathogen transmission and the number of hidden infections, which is a valid tool for decision-making by policy makers for epidemic control.Xuqi ZhangHaiqi LiuHanning TangMei ZhangXuedong YuanXiaojing ShenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xuqi Zhang Haiqi Liu Hanning Tang Mei Zhang Xuedong Yuan Xiaojing Shen The effect of population size for pathogen transmission on prediction of COVID-19 spread
description	Abstract Extreme public health interventions play a critical role in mitigating the local and global prevalence and pandemic potential. Here, we use population size for pathogen transmission to measure the intensity of public health interventions, which is a key characteristic variable for nowcasting and forecasting of COVID-19. By formulating a hidden Markov dynamic system and using nonlinear filtering theory, we have developed a stochastic epidemic dynamic model under public health interventions. The model parameters and states are estimated in time from internationally available public data by combining an unscented filter and an interacting multiple model filter. Moreover, we consider the computability of the population size and provide its selection criterion. With applications to COVID-19, we estimate the mean of the effective reproductive number of China and the rest of the globe except China (GEC) to be 2.4626 (95% CI: 2.4142–2.5111) and 3.0979 (95% CI: 3.0968–3.0990), respectively. The prediction results show the effectiveness of the stochastic epidemic dynamic model with nonlinear filtering. The hidden Markov dynamic system with nonlinear filtering can be used to make analysis, nowcasting and forecasting for other contagious diseases in the future since it helps to understand the mechanism of disease transmission and to estimate the population size for pathogen transmission and the number of hidden infections, which is a valid tool for decision-making by policy makers for epidemic control.
format	article
author	Xuqi Zhang Haiqi Liu Hanning Tang Mei Zhang Xuedong Yuan Xiaojing Shen
author_facet	Xuqi Zhang Haiqi Liu Hanning Tang Mei Zhang Xuedong Yuan Xiaojing Shen
author_sort	Xuqi Zhang
title	The effect of population size for pathogen transmission on prediction of COVID-19 spread
title_short	The effect of population size for pathogen transmission on prediction of COVID-19 spread
title_full	The effect of population size for pathogen transmission on prediction of COVID-19 spread
title_fullStr	The effect of population size for pathogen transmission on prediction of COVID-19 spread
title_full_unstemmed	The effect of population size for pathogen transmission on prediction of COVID-19 spread
title_sort	effect of population size for pathogen transmission on prediction of covid-19 spread
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/72d1c7cca77d45d4a32962381152bc5e
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The effect of population size for pathogen transmission on prediction of COVID-19 spread

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