Predicting the incidence of rifampicin resistant tuberculosis in Yunnan, China: a seasonal time series analysis based on routine surveillance data

We found the incidence of RR-TB was based on that of all TB in Yunnan. The SARIMA model successfully predicted the seasonal incidence trend of RR-TB in Yunnan Province in 2019 to 2023, but the prediction precision could be influenced by factors such as new infectious disease outbreaks or pandemics, social issues, environmental challenges or other unknown risks. Hence CDCs should pay special attention to the post epidemic effects of new infectious disease outbreaks or pandemics, carry out monitoring and early warning, and better optimize disease prediction models.

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Publication Details

Journal
BMC Infectious Diseases
Published
2024-08-16
DOI
https://doi.org/10.1186/s12879-024-09740-z
Citations
1
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
0.40
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article

Predicting the incidence of rifampicin resistant tuberculosis in Yunnan, China: a seasonal time series analysis based on routine surveillance data

Jinou Chen, Lin Xu, Yubing Qiu, Liangli Liu et al.
1 citations
BMC Infectious Diseases
COVID-19 epidemiological studies
0.40
article

Predicting the incidence of rifampicin resistant tuberculosis in Yunnan, China: a seasonal time series analysis based on routine surveillance data

Jinou Chen, Lin Xu, Yubing Qiu, Liangli Liu, Ling Li, Yun-bin Yang, Wei Wu
article en
1 citations

Abstract

We found the incidence of RR-TB was based on that of all TB in Yunnan. The SARIMA model successfully predicted the seasonal incidence trend of RR-TB in Yunnan Province in 2019 to 2023, but the prediction precision could be influenced by factors such as new infectious disease outbreaks or pandemics, social issues, environmental challenges or other unknown risks. Hence CDCs should pay special attention to the post epidemic effects of new infectious disease outbreaks or pandemics, carry out monitoring and early warning, and better optimize disease prediction models.

BMC Infectious DiseasesVol. 24(1)
Yunnan Center for Disease Control And Prevention (CN)
Good health and well-being
Openalex Percentile: Top 42%
COVID-19 epidemiological studies
0.40
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