Epidemic prediction of dengue fever based on vector compartment model and Markov chain Monte Carlo method

Abstract Background Dengue epidemics is affected by vector-human interactive dynamics. Infectious disease prevention and control emphasize the timing intervention at the right diffusion phase. In such a way, control measures can be cost-effective, and epidemic incidents can be controlled before deva...

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Autores principales:	Chien-Hung Lee, Ko Chang, Yao-Mei Chen, Jinn-Tsong Tsai, Yenming J. Chen, Wen-Hsien Ho
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Dengue transmission Vector-susceptible-infectious-recovered with exogeneity (VSIRX) Epidemic prevention timing Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/942b0e969c6141f4a9d5b8d9f9bc88b2
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spelling	oai:doaj.org-article:942b0e969c6141f4a9d5b8d9f9bc88b22021-11-14T12:13:00ZEpidemic prediction of dengue fever based on vector compartment model and Markov chain Monte Carlo method10.1186/s12859-021-04059-x1471-2105https://doaj.org/article/942b0e969c6141f4a9d5b8d9f9bc88b22021-11-01T00:00:00Zhttps://doi.org/10.1186/s12859-021-04059-xhttps://doaj.org/toc/1471-2105Abstract Background Dengue epidemics is affected by vector-human interactive dynamics. Infectious disease prevention and control emphasize the timing intervention at the right diffusion phase. In such a way, control measures can be cost-effective, and epidemic incidents can be controlled before devastated consequence occurs. However, timing relations between a measurable signal and the onset of the pandemic are complex to be discovered, and the typical lag period regression is difficult to capture in these complex relations. This study investigates the dynamic diffusion pattern of the disease in terms of a probability distribution. We estimate the parameters of an epidemic compartment model with the cross-infection of patients and mosquitoes in various infection cycles. We comprehensively study the incorporated meteorological and mosquito factors that may affect the epidemic of dengue fever to predict dengue fever epidemics. Results We develop a dual-parameter estimation algorithm for a composite model of the partial differential equations for vector-susceptible-infectious-recovered with exogeneity compartment model, Markov chain Montel Carlo method, and boundary element method to evaluate the epidemic periodicity under the effect of environmental factors of dengue fever, given the time series data of 2000–2016 from three cities with a population of 4.7 million. The established computer model of “energy accumulation-delayed diffusion-epidemics” is proven to be effective to predict the future trend of reported and unreported infected incidents. Our artificial intelligent algorithm can inform the authority to cease the larvae at the highest vector infection time. We find that the estimated dengue report rate is about 20%, which is close to the number of official announcements, and the percentage of infected vectors increases exponentially yearly. We suggest that the executive authorities should seriously consider the accumulated effect among infected populations. This established epidemic prediction model of dengue fever can be used to simulate and evaluate the best time to prevent and control dengue fever. Conclusions Given our developed model, government epidemic prevention teams can apply this platform before they physically carry out the prevention work. The optimal suggestions from these models can be promptly accommodated when real-time data have been continuously corrected from clinics and related agents.Chien-Hung LeeKo ChangYao-Mei ChenJinn-Tsong TsaiYenming J. ChenWen-Hsien HoBMCarticleDengue transmissionVector-susceptible-infectious-recovered with exogeneity (VSIRX)Epidemic prevention timingComputer applications to medicine. Medical informaticsR858-859.7Biology (General)QH301-705.5ENBMC Bioinformatics, Vol 22, Iss S5, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Dengue transmission Vector-susceptible-infectious-recovered with exogeneity (VSIRX) Epidemic prevention timing Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
spellingShingle	Dengue transmission Vector-susceptible-infectious-recovered with exogeneity (VSIRX) Epidemic prevention timing Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5 Chien-Hung Lee Ko Chang Yao-Mei Chen Jinn-Tsong Tsai Yenming J. Chen Wen-Hsien Ho Epidemic prediction of dengue fever based on vector compartment model and Markov chain Monte Carlo method
description	Abstract Background Dengue epidemics is affected by vector-human interactive dynamics. Infectious disease prevention and control emphasize the timing intervention at the right diffusion phase. In such a way, control measures can be cost-effective, and epidemic incidents can be controlled before devastated consequence occurs. However, timing relations between a measurable signal and the onset of the pandemic are complex to be discovered, and the typical lag period regression is difficult to capture in these complex relations. This study investigates the dynamic diffusion pattern of the disease in terms of a probability distribution. We estimate the parameters of an epidemic compartment model with the cross-infection of patients and mosquitoes in various infection cycles. We comprehensively study the incorporated meteorological and mosquito factors that may affect the epidemic of dengue fever to predict dengue fever epidemics. Results We develop a dual-parameter estimation algorithm for a composite model of the partial differential equations for vector-susceptible-infectious-recovered with exogeneity compartment model, Markov chain Montel Carlo method, and boundary element method to evaluate the epidemic periodicity under the effect of environmental factors of dengue fever, given the time series data of 2000–2016 from three cities with a population of 4.7 million. The established computer model of “energy accumulation-delayed diffusion-epidemics” is proven to be effective to predict the future trend of reported and unreported infected incidents. Our artificial intelligent algorithm can inform the authority to cease the larvae at the highest vector infection time. We find that the estimated dengue report rate is about 20%, which is close to the number of official announcements, and the percentage of infected vectors increases exponentially yearly. We suggest that the executive authorities should seriously consider the accumulated effect among infected populations. This established epidemic prediction model of dengue fever can be used to simulate and evaluate the best time to prevent and control dengue fever. Conclusions Given our developed model, government epidemic prevention teams can apply this platform before they physically carry out the prevention work. The optimal suggestions from these models can be promptly accommodated when real-time data have been continuously corrected from clinics and related agents.
format	article
author	Chien-Hung Lee Ko Chang Yao-Mei Chen Jinn-Tsong Tsai Yenming J. Chen Wen-Hsien Ho
author_facet	Chien-Hung Lee Ko Chang Yao-Mei Chen Jinn-Tsong Tsai Yenming J. Chen Wen-Hsien Ho
author_sort	Chien-Hung Lee
title	Epidemic prediction of dengue fever based on vector compartment model and Markov chain Monte Carlo method
title_short	Epidemic prediction of dengue fever based on vector compartment model and Markov chain Monte Carlo method
title_full	Epidemic prediction of dengue fever based on vector compartment model and Markov chain Monte Carlo method
title_fullStr	Epidemic prediction of dengue fever based on vector compartment model and Markov chain Monte Carlo method
title_full_unstemmed	Epidemic prediction of dengue fever based on vector compartment model and Markov chain Monte Carlo method
title_sort	epidemic prediction of dengue fever based on vector compartment model and markov chain monte carlo method
publisher	BMC
publishDate	2021
url	https://doaj.org/article/942b0e969c6141f4a9d5b8d9f9bc88b2
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Epidemic prediction of dengue fever based on vector compartment model and Markov chain Monte Carlo method

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