[Epidemiological characteristics and excess cases of varicella in Shaanxi Province, 2011-2025].

Objective: To analyze the epidemiological trends, seasonal characteristics, and excess disease burden of varicella in Shaanxi Province from 2011 to 2025, to provide a scientific basis for optimizing varicella prevention and control strategies and allocating health resources. Methods: Varicella data were sourced from the Infectious Disease Reporting Management System of the Chinese Disease Control and Prevention Information System in Shaanxi Province from 2011 to 2025. The Joinpoint regression model was applied to analyze incidence trends, with the annual percent change (APC) and average annual percent change (AAPC) calculated. The seasonal clustering was examined using a Runs test and hierarchical clustering. The negative binomial-adjusted Serfling regression model was applied to fit the baseline incidence, determine the epidemic threshold, and estimate the excess cases. Results: A total of 255 176 varicella cases were reported in Shaanxi Province from 2011 to 2025, with an average annual incidence rate of 43.97 per 100 000. The overall incidence showed a significant upward trend (AAPC=7.44%, P=0.006). Three turning points were identified by Joinpoint regression, dividing the whole period into four segments with distinct temporal trends. Varicella incidence showed an obvious bimodal seasonal pattern. The Runs test confirmed significant seasonal clustering (Z=-2.73, P=0.006), and cluster analysis identified high-incidence seasons as May-June and October-January of the following year. Multi-iteration fitting of the negative binomial-adjusted Serfling model revealed statistically significant performance at all stages (P<0.001). A total of 64 481 excess cases were detected, accounting for 25.27% of all reported cases; excess cases were concentrated in January, May, June, October, and November (61.93%). The periods 2015-2019 and 2020-2022 contributed the largest shares of excess burden. Conclusions: The prevalence of varicella in Shaanxi Province showed a fluctuating upward trend with a prominent bimodal seasonal pattern and a substantial excess disease burden from 2011 to 2025. The negative binomial-adjusted Serfling model can effectively fit the incidence baseline, identify epidemic periods, and estimate excess cases, which are useful for varicella surveillance, early warning, and quantitative evaluation of prevention and control effects. It is recommended to strengthen prevention and control during the spring and autumn epidemic peaks, reinforce epidemic response in key settings such as schools, and reduce the risk of morbidity and clustered varicella outbreaks.