Joint estimation of hand-foot-mouth disease model and prediction in korea using the ensemble kalman filter

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. Author summary: The cyclical nature of HFMD outbreaks in South Korea presents a significant and ongoing public health challenge, complicating both prevention and treatment strategies. Our study aimed to enhance forecasting accuracy for HFMD by developing a dynamic model that integrates real-time data using EnKF. Key disease dynamic factors, including susceptible, exposed, inpatient, outpatient, recovered, and deceased populations, are accounted for in this model. Employing monthly inpatient and outpatient data spanning 2011–2019, we conducted joint estimation of model parameters and states to forecast epidemic trends. The results of our study reveal a strong correspondence between predicted and actual HFMD trends, achieving a high degree of forecasting accuracy from June. The model precisely pinpointed peak epidemic periods and their severity, thus facilitating prompt and efficacious public health interventions. This research shows the utility of real-time data assimilation in improving the accuracy of infectious disease predictions, informing mitigation strategies for HFMD on a global scale, including South Korea. By bridging epidemiological modeling with actionable forecasts, our work contributes to advancing public health preparedness and reducing HFMD-related morbidity and mortality.