Change point detection in brucellosis time series from 2010 to 2023 in Xinjiang China using the BEAST algorithm
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 sou...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-025-88508-0 |
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| author | Liping Yang Chunxia Wang Pan Zhou Na Xie Maozai Tian Kai Wang |
| author_facet | Liping Yang Chunxia Wang Pan Zhou Na Xie Maozai Tian Kai Wang |
| author_sort | Liping Yang |
| collection | DOAJ |
| description | 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 ( $$\:Pr=0.9825$$ ), August 2017 ( $$\:Pr=0.3825$$ ), February 2022 ( $$\:Pr=0.3999$$ ), and May 2023 ( $$\:Pr=0.4146$$ ), respectively. The probability of the existence of five change points in the trend factors of brucellosis in Xinjiang is highest ( $$\:Pr=0.3250$$ ). The times at which these five change points occur, along with the probabilities of change at those moments, are as follows: March 2013 ( $$\:Pr=0.8262$$ ), August 2015 ( $$\:Pr=0.6311$$ ), July 2017 ( $$\:Pr=0.9833$$ ), February 2020 ( $$\:Pr=0.8387$$ ), and May 2023 ( $$\:Pr=0.3714$$ ). 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. |
| format | Article |
| id | doaj-art-e43c5194b6284d39abc92f098fc0f091 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
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| spelling | doaj-art-e43c5194b6284d39abc92f098fc0f0912025-02-02T12:21:07ZengNature PortfolioScientific Reports2045-23222025-01-011511910.1038/s41598-025-88508-0Change point detection in brucellosis time series from 2010 to 2023 in Xinjiang China using the BEAST algorithmLiping Yang0Chunxia Wang1Pan Zhou2Na Xie3Maozai Tian4Kai Wang5College of Public Health, Xinjiang Medical UniversityCollege of Medical Engineering and Technology, Xinjiang Medical UniversityCollege of Medical Engineering and Technology, Xinjiang Medical UniversityDepartment of Immunization Programme, Xinjiang Center for Disease Control and PreventionCollege of Medical Engineering and Technology, Xinjiang Medical UniversityCollege of Medical Engineering and Technology, Xinjiang Medical UniversityAbstract 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 ( $$\:Pr=0.9825$$ ), August 2017 ( $$\:Pr=0.3825$$ ), February 2022 ( $$\:Pr=0.3999$$ ), and May 2023 ( $$\:Pr=0.4146$$ ), respectively. The probability of the existence of five change points in the trend factors of brucellosis in Xinjiang is highest ( $$\:Pr=0.3250$$ ). The times at which these five change points occur, along with the probabilities of change at those moments, are as follows: March 2013 ( $$\:Pr=0.8262$$ ), August 2015 ( $$\:Pr=0.6311$$ ), July 2017 ( $$\:Pr=0.9833$$ ), February 2020 ( $$\:Pr=0.8387$$ ), and May 2023 ( $$\:Pr=0.3714$$ ). 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.https://doi.org/10.1038/s41598-025-88508-0BrucellosisChange point analysisSeasonalityTrend |
| spellingShingle | Liping Yang Chunxia Wang Pan Zhou Na Xie Maozai Tian Kai Wang Change point detection in brucellosis time series from 2010 to 2023 in Xinjiang China using the BEAST algorithm Scientific Reports Brucellosis Change point analysis Seasonality Trend |
| title | Change point detection in brucellosis time series from 2010 to 2023 in Xinjiang China using the BEAST algorithm |
| title_full | Change point detection in brucellosis time series from 2010 to 2023 in Xinjiang China using the BEAST algorithm |
| title_fullStr | Change point detection in brucellosis time series from 2010 to 2023 in Xinjiang China using the BEAST algorithm |
| title_full_unstemmed | Change point detection in brucellosis time series from 2010 to 2023 in Xinjiang China using the BEAST algorithm |
| title_short | Change point detection in brucellosis time series from 2010 to 2023 in Xinjiang China using the BEAST algorithm |
| title_sort | change point detection in brucellosis time series from 2010 to 2023 in xinjiang china using the beast algorithm |
| topic | Brucellosis Change point analysis Seasonality Trend |
| url | https://doi.org/10.1038/s41598-025-88508-0 |
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