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

HE Na-na; ZHAO Hang; SUN Jin-fang; YU Xiao-jin

doi:10.16462/j.cnki.zhjbkz.2022.12.007

Volume 26 Issue 12

Dec. 2022

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HE Na-na, ZHAO Hang, SUN Jin-fang, YU Xiao-jin. Trend analysis of COVID-19 incidence and death series based on Bayesian change point model[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2022, 26(12): 1402-1406. doi: 10.16462/j.cnki.zhjbkz.2022.12.007

Citation:

HE Na-na, ZHAO Hang, SUN Jin-fang, YU Xiao-jin. Trend analysis of COVID-19 incidence and death series based on Bayesian change point model[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2022, 26(12): 1402-1406. doi: 10.16462/j.cnki.zhjbkz.2022.12.007

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

doi: 10.16462/j.cnki.zhjbkz.2022.12.007

Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, China

Funds:

Postgraduate Research & Practice Innovation Program of Jiangsu Province SJCX20_0064

The Fundamental Research Funds for the Central Universities 3225002110D

More Information

Corresponding author: YU Xiao-jin, E-mail: xiaojinyu@seu.edu.cn
Received Date: 2022-01-20
Rev Recd Date: 2022-04-20

Available Online: 2022-12-30

Publish Date: 2022-12-10

Abstract

Abstract

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 provide a basis for epidemiological analysis of infectious diseases and evaluation of prevention and control measures.
- COVID-19,
- Bayesian change point analysis,
- Interrupted time series,
- Segmented linear regression

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References(17)

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