This study utilized the seasonal autoregressive integrated moving average (SARIMA) model to predict the seasonal incidence of infectious diarrhea in China, based on surveillance data from 2014 to 2024. The optimal SARIMA (1,0,1)(1,1,1)₁₂ model demonstrated reliable predictive performance, with a mean absolute percentage error (MAPE) of 12.73%. Epidemiologically, infectious diarrhea exhibited stable bimodal seasonal peaks annually. The 24-month forecast indicated that this seasonal epidemic pattern would persist through 2025-2026. SARIMA time-series forecasting can support precise early warning and dynamic risk assessment. Targeted seasonal interventions, enhanced pathogen monitoring, and improved grassroots public health management are recommended for the prevention and control of infectious diarrhea.
References
[1] Roman, C., Solh, T., Broadhurst, M. (2017) Infectious diarrhea. Physician Assistant Clinics, 2(2), 229-245.
[2] Lu, Y., Zhu, H., Hu, Z., He, F., Chen, G. (2023) Epidemic characteristics, spatiotemporal pattern, and risk factors of other infectious diarrhea in Fujian Province from 2005 to 2021: retrospective analysis. JMIR Public Health and Surveillance, 9(1), e45870.
[3] Villanueva-Miranda, I., Xiao, G., Xie, Y. (2025) Artificial intelligence in early warning systems for infectious disease surveillance: a systematic review. Frontiers in Public Health, 13, 1609615.
[4] Wang, Y., Xu, C., Zhang, S., Yang, L., Wang, Z., Zhu, Y., Yuan, J. (2019) Development and evaluation of a deep learning approach for modeling seasonality and trends in hand-foot-mouth disease incidence in mainland China. Scientific Reports, 9(1), 8046.
[5] Pangestu, C. J., Piantari, E., Munir, M. (2020) Prediction of diarrhea sufferers in Bandung with seasonal autoregressive integrated moving average (sarima). Journal of Computers for Society, 1(1), 61-79.
[6] Kam, H. J., Sung, J. O., Park, R. W. (2010) Prediction of daily patient numbers for a regional emergency medical center using time series analysis. Healthcare Informatics Research, 16(3), 158-165.
[7] Singh, A., Kumar Sharma, K., Wamanrao Kalare, K., Tripathi, A., Sharma, A., Bajpai, M. K. (2025) Comparative estimation of the spread of acute diarrhea and dengue in India using statistical mathematical and deep learning models. Scientific Reports, 15(1), 34672.
[8] Fang, X., Liu, W., Ai, J., He, M., Wu, Y., Shi, Y., Bao, C. (2020) Forecasting incidence of infectious diarrhea using random forest in Jiangsu Province, China. BMC Infectious Diseases, 20(1), 222.
[9] Chen, C., Guan, Z., Huang, C., Jiang, D., Liu, X., Zhou, Y., Yang, S. (2021) Epidemiological trends and hotspots of other infectious diarrhea (OID) in mainland China: a population-based surveillance study from 2004 to 2017. Frontiers in Public Health, 9, 679853.
[10] Tosepu, R., Ningsi, N. Y. (2024) Forecasting of diarrhea disease using ARIMA model in Kendari city, southeast Sulawesi province, Indonesia. Heliyon, 10(22), e40247.
[11] Waliullah, M., Hossain, M. J., Hasan, M. R., Hannan, A., Rahman, M. M. (2025) Unveiling the future: Wavelet-ARIMAX analysis of climate and diarrhea dynamics in Bangladesh’s urban centers. BMC Public Health, 25(1), 318.
[12] Alcayna, T., Kellerhaus, F., Tremblay, L., Fletcher, C., Goodermote, R., Santos-Vega, M., Lowe, R. (2025) Integrating anticipatory action in disease outbreak preparedness and response in the humanitarian sector. BMJ Global Health, 10(7), e017721.
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Nie, J. (2026) Forecasting the Incidence Trend of Infectious Diarrhea Based on Time‑series Models and Research on Public Health Prevention and Control Strategies. Journal of Disease and Public Health, 2(3), 7-16.