Cluster analysis based on the characteristics of pertussis time series

CHEN Jia; SONG Qiu-yue; LI Fang; ZHANG Yan-qi; LIU Ling; YI Dong; WU Ya-zhou

doi:10.16462/j.cnki.zhjbkz.2022.09.013

Volume 26 Issue 9

Sep. 2022

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CHEN Jia, SONG Qiu-yue, LI Fang, ZHANG Yan-qi, LIU Ling, YI Dong, WU Ya-zhou. Cluster analysis based on the characteristics of pertussis time series[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2022, 26(9): 1065-1071. doi: 10.16462/j.cnki.zhjbkz.2022.09.013

Citation:

CHEN Jia, SONG Qiu-yue, LI Fang, ZHANG Yan-qi, LIU Ling, YI Dong, WU Ya-zhou. Cluster analysis based on the characteristics of pertussis time series[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2022, 26(9): 1065-1071. doi: 10.16462/j.cnki.zhjbkz.2022.09.013

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Cluster analysis based on the characteristics of pertussis time series

doi: 10.16462/j.cnki.zhjbkz.2022.09.013

Department of Military Health Statistics, Department of Military Preventive Medicine, Army Medical University, Chongqing 400038, China

Funds:

National Natural Science Foundation of China 81872716

More Information

Corresponding author: WU Ya-zhou, E-mail: asiawu@tmmu.edu.cn
Received Date: 2021-10-12
Rev Recd Date: 2022-03-24

Available Online: 2022-09-17

Publish Date: 2022-09-10

Abstract

Abstract

Objective The data on the incidence of pertussis in 25 provincial administrative regions in China were clustered using a time-series feature extraction method.Based on the clustering results, the different incidence patterns of pertussis in various regions are analyzed to provide a scientific basis for the unified planning and implementation of pertussis disease prevention and control in China. Methods The nine global features of pertussis time series from 25 provincial administrative regions in China were extracted, and the nine indicators were transformed into a feature matrix consisting of three principal components using principal component analysis for hierarchical clustering analysis.The optimal number of clusters was selected to classify the different incidence patterns of pertussis time series. Results The optimal cluster number of hierarchical clustering was three categories, i.e.corresponding to the three incidence patterns of pertussis: acyclic, seasonal and non-trend pattern (9 provincial administrative regions in total), acyclic, seasonal and trend pattern (10 provincial administrative regions in total) and cyclic, seasonal and trend pattern (6 provincial administrative regions in total). Conclusion The hierarchical clustering by time series feature extraction can well group similar patterns closely together and accurately delineate the incidence patterns of pertussis in 25 provincial administrative regions of China.The clustering results can provide a theoretical basis for relevant departments to formulate prevention and control measures for pertussis in different Provinces.
- Pertussis,
- Time series feature extraction,
- Principal component analysis,
- Cluster analysis,
- The pattern of the disease

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References(17)

References

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