Epidemic prediction of dengue fever based on vector compartment model and Markov chain Monte Carlo method
-
Published:2021-11
Issue:S5
Volume:22
Page:
-
ISSN:1471-2105
-
Container-title:BMC Bioinformatics
-
language:en
-
Short-container-title:BMC Bioinformatics
Author:
Lee Chien-Hung,
Chang Ko,
Chen Yao-Mei,
Tsai Jinn-Tsong,
Chen Yenming J.ORCID,
Ho Wen-Hsien
Abstract
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.
Funder
National Health Research Institutes
Ministry of Science and Technology, Taiwan
Research Center for Environmental Medicine, Kaohsiung Medical University
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology
Reference18 articles.
1. Kaohsiung-D.C.B. Dengue information. Disease Control Bureau, the Department of Health, Kaohsiung City Government, Kaohsiung; 2000–2016.
2. Taiwan-CDC. Centers for disease control: Taiwan national infectious disease statistics system, 2000–2019. Taiwan Ministry of Health and Welfare. http://nidss.cdc.gov.tw/en/SingleDisease.aspx?dc=1&dt=2&disease=055. Accessed 11 Aug 2019.
3. Shen Y, Christina A, John ND, Gregory FC. Estimating the joint disease outbreak-detection time when an automated biosurveillance system is augmenting traditional clinical case finding. J Biomed Inform. 2008;41(2):224–31.
4. Andraud M, Niel H, Christiaan M, Philippe B. Dynamic epidemiological models for dengue transmission: a systematic review of structural approaches. PLoS ONE. 2012;7(11):e49085.
5. Nuraini N, Soewono E, Sidarto KA. Mathematical model of dengue disease transmission with severe DHF compartment. Bull Malays Math Sci Soc. 2007;30(2):143–57.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献