Hospital data-driven early warning model for epidemic outbreaks based on ensemble learning techniques_West China Medical Journal

Authors：

HE Lujia ¹ , WANG Ke ² , ZENG Yuting ³ ,  ZHANG Xinli ³ , LAN Xiaokai ² ,  ZHAO Shuzhen ⁴

1. Operation and Management Department, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
2. West China Med Co. Ltd, Chengdu, Sichuan 610041, P. R. China;
3. Business School, Sichuan University, Chengdu, Sichuan 610065, P. R. China;
4. Outpatient Department, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding?author：

ZHANG Xinli, Email: zhangxinli1231@163.com; ZHAO Shuzhen, Email: ssszszzsz@126.com

Keywords：

Major epidemic outbreaks; regional early warning; ensemble learning; class balancing strategies

DOI：

10.7507/1002-0179.202508125

Video：

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Objective To combine data generated by regional general hospitals, and propose an ensemble learning approach for the precise forecasting of major epidemic outbreaks. Methods Drawing on preprocessed, multi-source data from a large general hospital in Southwest China from January 2020 to December 2022, we initially pinpointed critical early-warning departments. This step helped uncover the original variables for our forecasting framework. Subsequently, principal component analysis isolated the primary modeling factors. Ten common prediction models were established, and the base model was selected through comparison of 5 performance indicators and parameter optimization. Based on this, combined with 6 sampling techniques under 3 data balancing strategies, sampling learning models were constructed. Through performance comparison, an ensemble prediction model with confirmed cases as the outcome indicator was finally established. Results Eight online or offline original variables were identified. Among them, the number of fever outpatient visits, the number of patients with positive signs in the Department of Integrated Traditional Chinese and Western Medicine, the volume of online consultations in the Department of Respiratory and Critical Care Medicine, and the volume of online consultations in the Infectious Disease Center could be used as predictors for epidemic forecasting. After eliminating collinearity, 3 principal components were extracted. Random forest was selected as the base model from 10 initial models. Based on performance comparisons among sampling-based ensemble models, the easy ensemble classifier-random forest (EEC-RF) model exhibited comparative advantages, yielding a recall rate of 0.857, an accuracy of 0.812, and an area under the curve (AUC) of 0.911. Conclusions The EEC-RF framework offers superior classification and generalization to elevate the accuracy of forecasting sudden outbreaks when handling minority-class epidemic events. In particular, the model’s exceptional recall rate, accuracy and AUC underscore its viability as a primary tool for public health early-warning systems.

Citation： HE Lujia, WANG Ke, ZENG Yuting, ZHANG Xinli, LAN Xiaokai, ZHAO Shuzhen. Hospital data-driven early warning model for epidemic outbreaks based on ensemble learning techniques. West China Medical Journal, 2026, 41(4): 623-627. doi: 10.7507/1002-0179.202508125 Copy

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1. The Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). Covid-19 dashboard. (2023-03-10).
2. Hick JL, Biddinger PD. Novel coronavirus and old lessons - preparing the health system for the pandemic. N Engl J Med, 2020, 382(20): e55.
3. Legido-Quigley H, Asgari N, Teo YY, et al. Are high-performing health systems resilient against the COVID-19 epidemic?. Lancet, 2020, 395(10227): 848-850.
4. 祁邦國, 于石成, 王琦琦, 等. 我國早期新型冠狀病毒肺炎疫情傳染病動力學模型分析. 疾病監測, 2022, 37(12): 1588-1593.
5. 王金愷, 張虎, 賈鵬, 等. 城市級新冠肺炎(COVID-19)疫情預測和仿真模型. 計算機輔助設計與圖形學學報, 2022, 34(8): 1302-1312.
6. Ming L, Julin W. Parameter adaptive seird model for epidemic prediction//2022 34th Chinese Control and Decision Conference (CCDC). Hefei, China: IEEE, 2022: 1277-1282.
7. Benvenuto D, Giovanetti M, Vassallo L, et al. Application of the ARIMA model on the COVID-2019 epidemic dataset. Data Brief, 2020, 29: 105340.
8. Perone G. Using the SARIMA model to forecast the fourth global wave of cumulative deaths from COVID-19: evidence from 12 hard-hit big countries. Econometrics, 2022, 10(2): 18.
9. Pramanik A, Sultana S, Rahman MS. Time series analysis and forecasting of monkeypox disease using ARIMA and SARIMA model//2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT). Kharagpur, India: IEEE, 2022: 1-7.
10. 賴曉鎣, 錢俊. ARIMA-LSTM-XGBoost 加權組合模型在肺結核發病趨勢預測的研究. 現代預防醫學, 2021, 48(1): 5-9.
11. Rustam F, Reshi AA, Mehmood A, et al. COVID-19 future forecasting using supervised machine learning models. IEEE Access, 2020, 8: 101489-101499.
12. Singh V, Poonia RC, Kumar S, et al. Prediction of COVID-19 corona virus pandemic based on time series data using support vector machine. J Discret Math Sci C, 2020, 23(8): 1583-1597.
13. Fan XR, Zuo J, He WT, et al. Stacking based prediction of COVID-19 pandemic by integrating infectious disease dynamics model and traditional machine learning//Proceedings of the 2022 5th International Conference on Big Data and Internet of Things. Chongqing, China: Association for Computing Machinery, 2022, 20-26.
14. Fang J, Zhang X, Tong Y, et al. Baidu index and COVID-19 epidemic forecast: evidence from China. Front Public Health, 2021, 9: 685141.
15. Saegner T, Austys D. Forecasting and surveillance of COVID-19 spread using google trends: literature review. Int J Environ Res Public Health, 2022, 19(19): 12394.
16. Jia JS, Lu X, Yuan Y, et al. Population flow drives spatio-temporal distribution of COVID-19 in China. Nature, 2020, 582(7812): 389-394.
17. Liu FT, Ting KM, Zhou ZH. Isolation forest. 2008 Eighth IEEE International Conference on Data Mining. Pisa, Italy: IEEE, 2008: 413-422.
18. Zhang T, Rabhi F, Chen X, et al. A machine learning-based universal outbreak risk prediction tool. Comput Biol Med, 2024, 169: 107876.
19. Li D, Zheng C, Zhao J, et al. Diagnosis of heart failure from imbalance datasets using multi-level classification. Biomed Signal Process Control, 2023, 81: 104538.
20. Wang T, Lu C, Ju W, et al. Imbalanced heartbeat classification using easyensemble technique and global heartbeat information. Biomed Signal Process, 2022, 71: 103105.

West China Medical Journal

Hospital data-driven early warning model for epidemic outbreaks based on ensemble learning techniques

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