Predictive Modeling of Enterovirus Hospital Burden Using Machine Learning and Age-Specific Surveillance Data: Operational Forecasting in Taiwan During the Postpandemic Era

Background: Enterovirus infections cause substantial pediatric morbidity worldwide, with severe cases requiring hospitalization. Accurate forecasting of hospitalization burden supports proactive resource allocation and clinical preparedness. During the postpandemic period (2023-2024), Taiwan experienced a resurgence of enterovirus activity following COVID-19-related suppression, although at levels below prepandemic baselines, creating unique operational forecasting challenges. Objective: This study aimed to develop and validate random forest models for 1-week-ahead enterovirus hospitalization forecasting using postpandemic surveillance data and to evaluate the impact of epidemiological regime alignment on predictive performance. Methods: We analyzed weekly enterovirus surveillance data from Taiwan's Centers for Disease Control covering 2023 to 2024, including outpatient, emergency department, and hospitalization counts stratified by five age groups (0-2, 3-4, 5-9, 10-14, and ≥15 y). Random forest models were trained on data from 2023 week 1 to 2024 week 40 (n=91 wk after lag preprocessing) and validated on a temporally independent test set covering 2024 weeks 41 to 52 (n=11 wk). Feature engineering incorporated age-specific indicators, 1- to 4-week temporal lags, seasonal variables, and derived epidemiological ratios. Results: The random forest model achieved strong 1-week-ahead forecasting performance on the test set (R²=0.216, root mean square error 23.5 hospitalizations per week, mean absolute percentage error 17.27%). Age-specific outpatient visits among children aged 0 to 2 and 3 to 4 years were the most influential predictors (feature importance=0.0839 and 0.0908, respectively), followed by seasonal week-of-year effects (feature importance=0.0803). The mean absolute error was 17.6 hospitalizations per week, demonstrating practical utility for hospital capacity planning. Test-period hospitalizations averaged 126.5 cases per week, representing a 3.4-fold increase from pandemic suppression levels (28.4 cases per week during 2020-2022) while remaining 24% below prepandemic baselines (165 cases per week during 2008-2019). Conclusions: Machine learning models trained on recent postpandemic surveillance data provide useful short-term forecasts of enterovirus hospitalization burden in Taiwan. A mean absolute percentage error of 17.27% represents reasonable accuracy for 1-week-ahead hospital resource planning. Age-specific pediatric outpatient surveillance offers valuable early signals for hospitalization forecasting, supporting the integration of such models into routine public health practice during postpandemic recovery.