JCNRJournal of Clinical and Nursing Research2208-36852208-3693Bio-Byword Scientific Publishing Pty. Ltd.10.26689/JCNR.v10i8.15439ArticleMulti-Factor-Driven Inter-City Forecasting of Viral and Bacterial Epidemics: A Comparative Modeling Studyhttps://artdesignp.com/journal/JCNR/10/8/10.26689/JCNR.v10i8.15439XiangYunhui,SunGuokang,TianLvbo,HuJiangtao,LiJianfeng,ZhangQin,WangJunxian,XiangPinpin,ChenPing,XieChunbao20261082026-08-31Objective: To compare the performance of prediction models incorporating multidimensional urban environmental factors in cross-city forecasting of viral and bacterial infectious diseases, and to explore how these multidimensional factors jointly shape epidemic dynamics, thereby providing a reference for optimizing urban epidemic forecasting strategies. Methods: Three representative cities in China were selected to characterize the correlation networks between multi-factor urban characteristics, including climate, air pollutants, economic activities, and control measures, and the incidence of viral infections (represented by hand, foot, and mouth disease and influenza) as well as bacterial infections (represented by pertussis and scarlatina). Using the seasonal auto-regressive integrated moving average (SARIMA) model as a benchmark, we systematically compared a long short-term memory (LSTM) model driven by these multidimensional factors with a SARIMAX model incorporating the same covariates. Through multi-disease forecasting experiments, the models were comprehensively evaluated across multiple dimensions, including predictive accuracy, robustness, and peak event forecasting capability. Results: The associations between multiple urban factors and infectious diseases exhibited marked spatial heterogeneity, and close statistical correlations were observed between climate variables and air pollutants, suggesting potential interactions among environmental covariates. Overall, both the multi-factor-driven LSTM and SARIMAX models significantly outperformed the traditional SARIMA model, with each demonstrating distinct advantages in predictive accuracy and peak event forecasting. For viral infections characterized by high incidence and rapid spread, the LSTM model showed the best performance, achieving the highest correlation between predicted and observed values, the smallest error metrics, and greater consistency across cross-validation folds. For bacterial infections with relatively lower incidence, the SARIMAX model performed better. Conclusions: Multidimensional urban factors may exert synergistic effects on infectious disease transmission. Multi-factor-driven forecasting strategies considerably outperformed traditional time-series models, and the respective strengths of specific models were closely associated with the epidemiological characteristics of the diseases. These findings may offer practical insights into methodological selection for constructing multi-category epidemic forecasting models adapted to heterogeneous urban environments.Infectious disease, Prediction model, Long short-term memory network, SARIMA

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