ArticlePLoS computational biology2025
Joint estimation of hand-foot-mouth disease model and prediction in korea using the ensemble kalman filter.
Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Association between persistent organic pollutants and malaria incidence in mainland China: a population-based surveillance and modeling analysis.BMC public health · 2026Article
- Efficient sequential Bayesian inference for state-space epidemic models using ensemble data assimilation.PLoS computational biology · 2026Article
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Authors and funding
3 authors.
Funding
Abstract
backgroundIn Korea, Hand-foot-and-mouth disease (HFMD) is a recurring illness that presents significant public health challenges, primarily because of its unpredictable epidemic patterns. The accurate prediction of the spread of HFMD plays a vital role in the effective management of the disease.
methodsWe have devised a dynamic model that accurately represents the transmission dynamics of HFMD. The model includes compartments for susceptible, exposed, inpatients, outpatients, recovered, and deceased individuals. By utilizing monthly inpatient and outpatient data, the ensemble Kalman filter (EnKF) method was employed to perform a joint estimation of model parameters and state variables. The calibration of model parameters involved using data from the months of January to May, while generating forecasts for the timeframe spanning from June to December.
resultsThe findings reveal a significant alignment between the model and the observed data, as evidenced by root-mean-square error (RMSE) values below 1000 for inpatients and below 10000 for outpatients starting in June. The correlation coefficients surpassed 0.9, except for the year 2015. The implications of our findings suggest a notable shift in transmission and recovery rates, starting in 2015. DISCUSSION: The model successfully predicted the peak and magnitude of HFMD outbreaks occurring between June and December, closely matching the observed epidemic patterns. The model's efficacy in predicting epidemic trends and informing preventive strategies is reinforced by the insights gained from monthly variations in parameter estimates of HFMD transmission dynamics.
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