Abstract: Objective To analyze the epidemiological characteristics and spatiotemporal clustering of human brucellosis in Dongguan City from 2015 to 2024, thereby providing a scientific basis for the formulation and refinement of prevention and control strategies. Methods Data pertaining to reported cases of human brucellosis in Dongguan City from 2015 to 2024 were collected. The epidemiological characteristics were described using descriptive statistics. Joinpoint regression analysis was employed to assess incidence trends. Global spatial autocorrelation and hotspot analyses were conducted using ArcMap 10.8.2, while spatiotemporal clustering analysis was performed with SaTScan 10.1. Results A total of 221 human brucellosis cases were reported in Dongguan City from 2015 to 2024. The annual reported incidence exhibited no significant trend (AAPC=1.54%, P=0.624). However, the monthly reported incidence revealed two periods of significant increase: January 2015 to April 2016 (MPC=8.10%, P=0.037) and February to May 2024 (MPC=70.44%, P=0.004). Case incidence was concentrated between March and August, accounting for 72.85% (161/221) of the total annual cases. The male-to-female ratio was 2.88∶1. The principal exposure risk factor for both occupational and non-occupational populations was contact with sheep/goats and their products. Serological surveillance of 1 672 occupationally exposed individuals from 2018 to 2024 revealed a positivity rate of 0.54% (9/1 672). Global spatial autocorrelation analysis indicated a random distribution of cases for all years except 2018. Hotspot analysis identified Qiaotou Town as the most frequent hotspot. Spatiotemporal scan statistics detected a primary cluster in Qiaotou and Xiegang towns and a secondary cluster in Shilong and Chashan towns over the 2015-2024 period. Conclusion The overall incidence of human brucellosis in Dongguan City is relatively stable; however, the disease demonstrates significant spatiotemporal clustering in specific locales. Enhanced prevention and control measures should be prioritized in these identified hotspot and clustered areas.

Key words: Brucellosis, Epidemiological characteristics, Spatiotemporal clustering, Joinpoint regression analysis