Evidence map›Paper›PMID 39885345›Full record

ArticleScientific reports2025

Change point detection in brucellosis time series from 2010 to 2023 in Xinjiang China using the BEAST algorithm.

Liping Yang, Chunxia Wang, Pan Zhou, Na Xie, Maozai Tian, Kai Wang

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Liping YangCollege of Public Health, Xinjiang Medical University, Urumqi, 830017, China.
Chunxia WangCollege of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830017, China.
Pan ZhouCollege of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830017, China.
Na XieDepartment of Immunization Programme, Xinjiang Center for Disease Control and Prevention, Urumqi, 830054, China.
Maozai TianCollege of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830017, China. mztian@ruc.edu.cn.
Kai WangCollege of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830017, China. wangkaimath@sina.com.

Funding

Graduate Innovation and Entrepreneurship Project of Xinjiang Medical University CXCY2024006National Natural Science Foundation of China 11961071Natural Science Foundation of Xinjiang of China 2022D01C703
6 · The paper itself

Abstract

Brucellosis is a significant global challenge, but there has been a lack of epidemiological studies on brucellosis in Xinjiang from a change point perspective. This study aims to bridge this gap by employing sequence decomposition and identifying significant change points, with datasets sourced from the Xinjiang Disease Prevention and Control Information System. This study employed the BEAST algorithm to decompose the brucellosis time series in Xinjiang from 2010 to 2023, while simultaneously identifying change points in the decomposed seasonal and trend components. The probability of four change points occurring within the seasonal component is 0.8950. And the locations where these four change points occur and the probabilities associated with each change point are August 2013 ([Formula: see text]), August 2017 ([Formula: see text]), February 2022 ([Formula: see text]), and May 2023 ([Formula: see text]), respectively. The probability of the existence of five change points in the trend factors of brucellosis in Xinjiang is highest ([Formula: see text]). The times at which these five change points occur, along with the probabilities of change at those moments, are as follows: March 2013 ([Formula: see text]), August 2015 ([Formula: see text]), July 2017 ([Formula: see text]), February 2020 ([Formula: see text]), and May 2023 ([Formula: see text]). Change point analysis holds significant utility within the field of epidemiology. These discoveries furnish pivotal insights for epidemiological investigations and the development of early warning systems tailored to brucellosis.

Indexed as

AlgorithmsBrucellosisChinaHumansSeasonsTime FactorsBrucellosisChange point analysisSeasonalityTrend

Identifiers

PMID39885345
PMCPMC11782483

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