Abstract: Objective To investigate the spatiotemporal distribution, evolutionary characteristics, and high-incidence areas of influenza cases in Ganzhou, Jiangxi Province, from 2019 to 2023, with the aim of informing the development of precise prevention and control measures. Methods Influenza case data reported in Ganzhou from 2019 to 2023 were collected and subjected to spatiotemporal scan analysis using SaTScan 10.2.4. Global and local spatial autocorrelation analyses were performed with ArcGIS 10.8. Results A total of 92 051 influenza cases were reported in Ganzhou between 2019 and 2023, corresponding to an average annual incidence rate of 207.76 per 100 000 population. The annual incidence rates for the respective years were 199.66, 104.34, 56.80, 233.47, and 440.79 per 100 000. Global spatial autocorrelation analysis revealed significant spatial clustering, with Moran's I indices ranging from 0.268 to 0.716 (all P<0.001). Local spatial autocorrelation analysis identified “high-high” clusters in 62 townships, towns, and subdistricts across 10 counties (cities, districts), primarily concentrated in the central urban area, southern, southwestern, and northeastern regions of Ganzhou. Spatiotemporal analysis demonstrated pronounced clustering of influenza incidence during the study period, with the most significant cluster located in the central urban, southern, southeastern, and southwestern regions. The clustering period extended from March 1 to April 30, 2023, with a log likelihood ratio (LLR) of 12 647.25 and a relative risk (RR) of 7.95 (P<0.001). Conclusions Influenza cases in Ganzhou from 2019 to 2023 exhibited marked spatiotemporal clustering. Winter and spring emerged as critical periods for influenza prevention and control, with the central urban, southern, southeastern, southwestern, and northeastern regions identified as priority areas for targeted interventions.

Key words: Influenza, Spatial autocorrelation, Spatiotemporal clustering