Influencing factors and short-term prediction of fever clinic visits in Shenzhen, Guangdong, 2023−2025, a study based on generalized additive model

Objective To investigate the impacts of common community-acquired infectious diseases and fever-related Baidu search index on fever clinic visits in Shenzhen, Guangdong province, from both disease and behavioral dimensions, and develop a short-term trend prediction model for fever clinic visits.

Methods The data of fever clinic visits, incidence of common community-acquired infectious diseases, and fever-related Baidu search index were collected from the China Disease Prevention and Control Information System and Baidu search engine. The data were from fever clinics in secondary and higher-level hospitals and urban community health service centers in Shenzhen from April 2023 to June 2025. Five core fever-related infectious diseases were selected to construct the Fever Baidu Search Index (FBSI). A generalized additive model (GAM) was applied to identify the influencing factors associated with fever clinic visits. The data from April 3, 2023 to May 25, 2025 (112 weeks) were used as the training set, and the data from May 26 to June 22, 2025 (4 weeks) were used as the prediction set for short-term trend prediction.

Results The final selected model was GAM1.0, with explanatory variables including coronavirus disease 2019 (COVID-19), influenza, and mumps. The adjusted coefficient of determination (adjusted R²_-sq) was 0.88, and the explained deviance was 91.80%. Among the included variables, influenza showed the greatest contribution to fever clinic visits (F=30.062, P<0.001), followed by COVID-19 (F=10.549, P<0.001). The correlation coefficient between FBSI and fever clinic visits was 0.85 (P<0.001). However, the effect of FBSI was not statistically significant in GAM1.0 (P=0.186). Further analysis revealed that when FBSI was included as the sole predictor (GAM2.0), its effect was significant (F=32.810, P<0.001). When FBSI and influenza were simultaneously included in the model (GAM3.0), the contribution of FBSI (F=15.195, P<0.001) was greater than that of influenza (F=4.943, P=0.028). The short-term trend prediction based on GAM1.0 demonstrated a strong consistency between predicted value and observed value, with a squared correlation coefficient (R²) of 0.98.

Conclusion Multiple common community-acquired infectious diseases and fever-related Baidu search index were the important determinants of fever clinic visits in Shenzhen from 2023 to 2025. The GAM1.0 model showed excellent performance in short-term trend prediction and may provide reliable evidence to improve fever clinic resource allocation and strengthen infectious disease prevention and control.