Key result
The ensemble Kalman filter-based dynamic model accurately predicted the peak and magnitude of hand-foot-and-mouth disease outbreaks, achieving RMSE values below 1000 for inpatients and below 10000 for outpatients, with correlation coefficients exceeding 0.9.
Why the study?
Hand-foot-and-mouth disease presents significant public health challenges in Korea due to unpredictable epidemic patterns, making accurate prediction vital for disease management.
Effect estimate: RMSE < 1000 for inpatients, < 10000 for outpatients; correlation > 0.9
A dynamic transmission model using the ensemble Kalman filter accurately predicted the peak and magnitude of HFMD outbreaks in Korea, offering a tool for real-time epidemic forecasting.
HFMD transmission model may aid Korean epidemic forecasting; leaves open prospective validation before public health adoption.
BACKGROUND: In 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. METHODS: We 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. RESULTS: The 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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Abbas et al. (2025) studied Hand-foot-and-mouth disease (HFMD) (n=51,000,000). Ensemble Kalman filter (EnKF) based dynamic transmission model was evaluated on Forecasting accuracy (Root-mean-square error and correlation coefficient) (RMSE < 1000 for inpatients, < 10000 for outpatients; correlation > 0.9). The ensemble Kalman filter-based dynamic model accurately predicted the peak and magnitude of hand-foot-and-mouth disease outbreaks, achieving RMSE values below 1000 for inpatients and below 10000 for outpatients, with correlation coefficients exceeding 0.9.
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