Publication: Hand, Foot, and Mouth Disease in Thailand: A Comprehensive Modelling of Epidemic Dynamics
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Issued Date
2021-01-01
Resource Type
ISSN
17486718
1748670X
1748670X
Other identifier(s)
2-s2.0-85102646282
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Mahidol University
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SCOPUS
Bibliographic Citation
Computational and Mathematical Methods in Medicine. Vol.2021, (2021)
Suggested Citation
Suraj Verma, M. A. Razzaque, U. Sangtongdee, C. Arpnikanondt, B. Tassaneetrithep, D. Arthan, C. Paratthakonkun, N. Soonthornworasiri Hand, Foot, and Mouth Disease in Thailand: A Comprehensive Modelling of Epidemic Dynamics. Computational and Mathematical Methods in Medicine. Vol.2021, (2021). doi:10.1155/2021/6697522 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/76398
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Title
Hand, Foot, and Mouth Disease in Thailand: A Comprehensive Modelling of Epidemic Dynamics
Abstract
Hand, foot, and mouth disease (HFMD) is a highly contagious disease with several outbreaks in Asian-Pacific countries, including Thailand. With such epidemic characteristics and potential economic impact, HFMD is a significant public health issue in Thailand. Generally, contagious/infectious diseases' transmission dynamics vary across geolocations due to different socioeconomic situations, demography, and lifestyles. Hence, a nationwide comprehensive model of the disease's epidemic dynamics can provide information to understand better and predict a potential outbreak of this disease and efficiently and effectively manage its impact. However, there is no nationwide and comprehensive (i.e., the inclusion of reinfections in the model) model of HFDM dynamics for Thailand. This paper has endeavoured to promote nationwide comprehensive modelling of HFMD's epidemic dynamics and comprehend the reinfection cases. We have formulated the SEIRS epidemiological model with dynamic vitals, including reinfections, to explore this disease's prevalence. We also introduced periodic seasonality to reproduce the seasonal effect. The pattern of spread of this disease is uneven across the provinces in Thailand, so we used K-means clustering algorithm to cluster those provinces into three groups (i.e., highly, moderately, and least affected levels). We also analysed health records collected from district hospitals, which suggest significant reinfection cases. For example, we found that 11% (approximately) of infectious patients return for repeat treatment within the study period. We also performed sensitivity analysis which indicates that the basic reproduction number (R0) is sensitive to the rate of transmission (β) and the rate at which infected people recover (γ). By fitting the model with HFMD confirmed data for the provinces in each cluster, the basic reproduction number (R0) was estimated to be 2.643, 1.91, and 3.246 which are greater than 1. Based on this high R0, this study recommends that this disease will persist in the coming years under identical cultural and environmental conditions.