Radically different
Americans are seeing their doctors, getting hospital procedures, and filling prescriptions more frequently than economists and budget experts anticipated. Weight loss drugs, in particular, have morphed into their own special category of spending and are pushing budgets across the country to their limits.
Combining an increased amount of care with the country’s high baseline of prices has resulted in the health care system taking up more of the economy, new data show — findings that again reflect people’s widespread discontent with how unaffordable health care has become.
The country spent $5.7 trillion on health care in 2025, a 7.3% increase from 2024, according to the latest government figures published in the journal Health Affairs on Wednesday. That amounted to almost $16,500 per person.
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 (²=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.
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