【摘要】 目的 了解滨州市人间布鲁菌病（简称“布病”）的流行病学特征，并探讨差分整合移动平均自回归（autoregressive integrated moving average, ARIMA）模型在人间布病发病预测中的应用，为制定相应的防控措施提供参考依据。方法 收集滨州市2015—2020年人间布病的监测资料，进行描述性统计分析；应用SPSS 20.0软件建立滨州市人间布病ARIMA模型，并进行短期预测。结果 滨州市2015—2020年布病疫情呈逐年下降趋势，每年的3～8月为发病高峰，5月发病例数最多。发病率居前3位的地区分别为沾化区、阳信县和无棣县。发病年龄为8个月～85岁，60～<70岁发病例数最多；男性发病率（7.90/10万）高于女性（2.77/10万）,差异有统计学意义（χ²=286.231,P<0.01）;职业以农民为主，占90.58%。ARIMA（0,1,0）（0,1,1）₁₂为选取的最优模型，能较好地拟合滨州市人间布病月发病数，预测2022年发病例数，总体趋势与往年相似。结论 2015—2020年滨州市人间布病疫情存在明显的季节、地域和人群分布特征，应进一步加强对重点地区、重点人群的干预工作，开展定期监测，落实好综合防控措施。更多还原

【Abstract】 Objective To understand the epidemiological characteristic of human brucellosis in binzhou, and to explore the application of autoregressive integrated moving average（ARIMA） model in predicting the incidence of human brucellosis, so as to provide reference for formulating corresponding preventive and control measures.Methods The surveillance data of human brucellosis in Binzhou from 2015 to 2020 were collected and analysed with descriptive statistics.The ARIMA model of human brucellosis in Binzhou was established by SPSS 20.0 software, and the short-term prediction was made.Results The brucellosis epidemics in Binzhou from 2015 to 2020 showed a decreasing trend year by year, with the peak incidence from March to August each year, and the highest number of cases in May.The top 3 areas with the highest incidence rate were Zhanhua District, Yangxin County and Wudi County.The age of onset was 8 months to 85 years old, with the greatest number of cases occurring between 60 and <70 years of age.The incidence rate of male（7.90/100 000） was higher than that of female（2.75/100 000）,the difference was statistically significant（χ²=286.231,P<0.01）.The occupation was mainly farmers, accounting for 90.58%.The ARIMA（0,1,0）（0,1,1）₁₂ was the optimal model selected, which could better fit the monthly cases of human brucellosis incidence in Binzhou, and predict the number of cases in 2022,with the overall trend similar to that in previous years.Conclusion The human brucellosis epidemics in Binzhou from 2015 to 2020 exhibit distinct seasonal, regional, and population distribution characteristics.Enhance intervention efforts in key areas and target populations should be further strengthened, conduct regular monitoring, and implement comprehensive prevention and control measures should be implemented.更多还原