Title of article :
Hand, Foot, and Mouth Disease in Thailand: A Comprehensive Modelling of Epidemic Dynamics
Author/Authors :
Verma, Suraj School of Computing - Engineering & Digital Technologies - Teesside University - Southfield Rd - Middlesbrough, UK , Razzaque, M. A School of Computing - Engineering & Digital Technologies - Teesside University - Southfield Rd - Middlesbrough, UK , Sangtongdee, U School of Computing - Engineering & Digital Technologies - Teesside University - Southfield Rd - Middlesbrough, UK , Arpnikanondt, C King Mongkut’s University of Technology Thonburi - Bangkok, Thailand , Tassaneetrithep, B Research Department - Faculty of Medicine Siriraj Hospital - Bangkok, Thailand , Arthan, D Department of Tropical Nutrition and Food Science - Faculty of Tropical Medicine - Mahidol University - Bangkok, Thailand , Paratthakonkun, C Mahidol University - Nakhon Pathom, Thailand , Soonthornworasiri, N Department of Tropical Hygiene - Faculty of Tropical Medicine - Mahidol University - Bangkok, Thailand
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.
Keywords :
Dynamics , Comprehensive , HFMD , Thailand
Journal title :
Computational and Mathematical Methods in Medicine