Impact of policy shifts and festival holidays on tuberculosis surveillance: a time-series analysis in Southwest China (2014–2024)

Background Accurate forecasting of pulmonary tuberculosis (PTB) is frequently impeded by surveillance artifacts, most notably health policy shifts and festival holidays. This study proposes a practical epidemiological forecasting framework to address these real-world disruptions within China’s monitoring data. Methods We utilized an 11-year historical cohort (2014–2024) from Jiulongpo District, Chongqing. To ensure longitudinal comparability, we first performed an epidemiological standardization to mitigate the artificial structural break caused by the 2018 diagnostic reclassification. To establish a robust “biological baseline” (the natural transmission trend devoid of reporting artifacts), a parsimonious SARIMA model was benchmarked against alternative statistical and modern machine learning algorithms, and validated via a multi-year rolling window cross-validation (2022–2024). Results Rolling cross-validation confirmed SARIMA’s temporal stability, effectively avoiding the overfitting tendencies observed in complex machine learning models. The optimal SARIMA(0,1,1) × (0,1,1) 12 model demonstrated high predictive accuracy for the 2024 annual caseload (Relative Error: 6.17%). Notably, actual incidence in January and February fell significantly below the forecasted 95% prediction intervals (PIs), which was immediately followed by a reported “catch-up” spike in March. This reflects a temporary reporting disruption associated with the Lunar New Year holidays, acting as a socio-cultural filter. Conclusion Epidemiological standardization is an important prerequisite for accurate trend evaluation amidst policy shifts. Furthermore, by treating holiday-associated deviations as diagnostic indicators, this rigorously benchmarked framework functions as an anomaly detection tool to estimate potential notification delays. Although broader geographic application requires localized recalibration, this strategy provides crucial evidence for post-holiday targeted Active Case Finding (ACF).