基于贝叶斯变点模型的COVID-19发病与死亡序列趋势  

Trend analysis of COVID-19 incidence and death series based on Bayesian change point model

作　　者：贺娜娜 赵航 孙金芳[1] 余小金[1] HE Na-na;ZHAO Hang;SUN Jin-fang;YU Xiao-jin(Department of Epidemiology and Health Statistics,School of Public Health,Southeast University,Nanjing 210009,China)

机构地区：[1]东南大学公共卫生学院流行病与卫生统计学系,南京210009

出　　处：《中华疾病控制杂志》2022年第12期1402-1406,1444,共6页Chinese Journal of Disease Control & Prevention

基　　金：江苏省研究生科研与实践创新计划项目(SJCX20_0064);中央高校基本科研业务费专项资金(3225002110D)。

摘　　要：目的 基于COVID-19发病和死亡序列趋势分析,探讨评价传染病发病或死亡变化趋势的方法,为同类流行病学研究数据的分析提供可能的分析策略。方法 提取中国31个省(自治区、直辖市)2020年1月23日至2020年3月18日的COVID-19累计确诊病例和累计死亡病例数据,基于贝叶斯变点分析模型确定时间序列变点,并应用间断时间序列(interrupted time series, ITS)构建分段线性回归(segmented linear regression, SLR)模型,评价序列变化趋势与干预措施、政策的一致性。结果 武汉市累计确诊病例和累计死亡病例数据各有3次变点,湖北省(除武汉市)和除湖北省以外的30个省(自治区、直辖市)的确诊病例数、死亡病例数各有4次变点。武汉市累计确诊病例数3次变点后改变量分别为1 493.885(P<0.001)、2 444.913(P<0.001)、-4 061.038(P<0.001);累计死亡病例数第2次、第3次变点后改变量分别为-66.917(P<0.001)、-19.845(P=0.034)。湖北省(除武汉市)累计确诊病例数第3次变点出现增幅降低且差异有统计学意义,改变量为-845.244(P<0.001);累计死亡病例数增幅降低出现在第3次、第4次变点,斜率改变量分别为-10.062(P<0.001)、-12.245(P<0.001)。除湖北省以外的30个省(自治区、直辖市)累计确诊病例数第2次变点后开始出现增幅降低,改变量分别为-281.494(P<0.001)、-295.080(P<0.001)、-145.054(P<0.001);累计死亡病例数差异有统计学意义的增幅降低出现在第3次、第4次变点,斜率改变量分别为-3.199(P<0.001)、-1.706(P<0.001)。结论 结合贝叶斯变点分析和ITS分析可充分考虑时间序列趋势变化的不确定性,为传染病疫情分析和防控措施评价提供依据。Objective To analyze the trend of COVID-19 based on the trend analysis of incidence and mortality data and provide analysis strategies for similar epidemiological researches. Methods We used the Bayesian change point analysis model to obtain the time series change points based on the number of cumulative confirmed and cumulative death cases of COVID-19 from January 23, 2020 to March 18, 2020 in Chinese mainland. Interrupted time series(ITS) method was applied to build a segmented linear regression(SLR) model, evaluating the consistency of trends in the series with the intervention or policy. Results There were 3 change points in cumulative confirmed cases and deaths in Wuhan, and 4 change points in cumulative confirmed cases and deaths in Hubei Province(except Wuhan) and Chinese mainland(except Hubei Province). The changes in the number of cumulative confirmed cases in Wuhan after 3 change points were 1 493.885(P<0.001), 2 444.913(P<0.001) and-4 061.038(P<0.001), respectively. The number of cumulative deaths after the second and third change points were-66.917(P<0.001) and-19.845(P=0.034), respectively. The increase in the number of cumulative confirmed cases in Hubei Province(except Wuhan) began to decrease after the third change point, and the change is-845.244(P<0.001). The the increase in the number of cumulative deaths decreased after the third and fourth change points, and the slope changes were-10.062(P<0.001) and-12.245(P<0.001), respectively. The increase in the number of cumulative confirmed cases in Chinese mainland decreased from the second change point, and the changes were-281.494(P<0.001),-295.080(P<0.001),-145.054(P<0.001), respectively. The statistically significant decrease in the increase of cumulative deaths appeared in the third and fourth change points, and the slope changes were-3.199(P<0.001) and-1.706(P<0.001), respectively. Conclusions The combination of interrupted time series analysis with Bayesian change point analysis can consider the uncertainty of time series trend changes, and pro

关 键 词：COVID-19 贝叶斯变点分析 间断时间序列 分段线性回归 

分 类 号：R195.1[医药卫生—卫生统计学]