目的 分析2020—2024年杭州市富阳区流行性感冒（简称流感）流行特征及时空聚集性，为流感疫情风险评估、早期预警和精准防控提供依据。方法 从中国疾病预防控制信息系统获取2020—2024年杭州市富阳区流感病例数据，采用描述流行病学方法分析其三间分布特征；使用ArcGIS 10.8软件进行空间自相关分析，使用SaTScan 10.2软件进行时空扫描统计分析。结果 2020—2024年杭州市富阳区累计报告流感病例59 222例，无重症及死亡病例，年均发病率142.36/万。病例以学生和幼托儿童为主，占58.18%。流行高峰主要为每年11月至次年3月，2022年出现夏季高峰。2020、2022和2023年富阳区流感发病率呈空间正相关（Moran’s I均>0，P均<0.05），高-高聚集区主要位于中西部地区。时空扫描分析显示，每年均存在时空聚集区，一类聚集区主要分布在北部和西南部。结论 2020—2024年杭州市富阳区流感发病呈现明显的时空聚集性。建议在流感流行季前，针对高发乡镇（街道）实施精准防控，提升疫情防控效果。

Objective To analyze the epidemiological characteristics and spatiotemporal clustering of influenza in Fuyang District of Hangzhou from 2020 to 2024, and to provide a basis for risk assessment, early warning and targeted prevention and control of influenza outbreaks. Methods Influenza case data in Fuyang District of Hangzhou from 2020 to 2024 were obtained from the Chinese Disease Prevention and Control Information System. The descriptive epidemiological methods were used to analyze the distribution characteristics in terms of person, place, and time. Spatial autocorrelation analysis was performed with ArcGIS 10.8, and spatiotemporal scan statistical analysis was conducted using SaTScan 10.2. Results From 2020 to 2024, a total of 59 222 influenza cases were reported in Fuyang District of Hangzhou, with no severe or fatal cases. The average annual incidence rate was 142.36/10 000. Students and kindergarten children accounted for 58.18% of cases. The epidemic peak mainly occurred from November to March of the following year, with an additional peak in the summer of 2022. The incidence rates of influenza in Fuyang District in 2020, 2022 and 2023 were spatially positively correlated (all Moran’s I>0, all P<0.05), and the high-high aggregation areas were mainly located in the central and western regions. Spatiotemporal scan analysis identified significant spatiotemporal clusters each year, with the primary clusters predominantly distributed in the northern and southwestern areas. Conclusion Influenza incidence in Fuyang District of Hangzhou from 2020 to 2024 showed significant spatiotemporal clustering. It is recommended to implement precise prevention and control strategies in high-incidence towns (streets) before the influenza epidemic season to improve the effectiveness of epidemic control.