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Change point detection in brucellosis time series from 2010 to 2023 in Xinjiang China using the BEAST algorithm
Liping Yang1, Chunxia Wang2, Pan Zhou2
1College of Public Health, Xinjiang Medical University, Urumqi, 830017, China.
Scientific Reports
|January 30, 2025
Summary
This study identified significant change points in Xinjiang
Area of Science:
- Epidemiology
- Infectious Diseases
- Time Series Analysis
Background:
- Brucellosis poses a global health challenge.
- Epidemiological studies on brucellosis in Xinjiang are limited, particularly from a change point perspective.
Purpose of the Study:
- To identify significant change points in brucellosis incidence in Xinjiang.
- To analyze temporal patterns using sequence decomposition.
Main Methods:
- Utilized the Xinjiang Disease Prevention and Control Information System data (2010-2023).
- Employed the Bayesian Evolutionary Analysis Using Sampling Techniques (BEAST) algorithm for time series decomposition.
- Identified change points in seasonal and trend components of brucellosis data.
Main Results:
- Four significant change points were detected in the seasonal component (August 2013, August 2017, February 2022, May 2023).
- Five significant change points were identified in the trend component (March 2013, August 2015, July 2017, February 2020, May 2023).
Conclusions:
- Change point analysis is valuable for epidemiological investigations.
- Findings provide critical insights for brucellosis surveillance and early warning systems in Xinjiang.
