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Temporal shifts in 24 notifiable infectious diseases in China before and during the COVID-19 pandemic

Author

Listed:
  • Kangguo Li

    (Xiamen University)

  • Jia Rui

    (Xiamen University)

  • Wentao Song

    (Xiamen University)

  • Li Luo

    (Xiamen University)

  • Yunkang Zhao

    (Xiamen University)

  • Huimin Qu

    (Xiamen University)

  • Hong Liu

    (Xiamen University)

  • Hongjie Wei

    (Xiamen University)

  • Ruixin Zhang

    (Xiamen University)

  • Buasiyamu Abudunaibi

    (Xiamen University)

  • Yao Wang

    (Xiamen University)

  • Zecheng Zhou

    (Xiamen University)

  • Tianxin Xiang

    (The First Affiliated Hospital, Jiangxi Medical College, Nanchang University
    Jiangxi Hospital of China–Japan Friendship Hospital)

  • Tianmu Chen

    (Xiamen University)

Abstract

The coronavirus disease 2019 (COVID-19) pandemic, along with the implementation of public health and social measures (PHSMs), have markedly reshaped infectious disease transmission dynamics. We analysed the impact of PHSMs on 24 notifiable infectious diseases (NIDs) in the Chinese mainland, using time series models to forecast transmission trends without PHSMs or pandemic. Our findings revealed distinct seasonal patterns in NID incidence, with respiratory diseases showing the greatest response to PHSMs, while bloodborne and sexually transmitted diseases responded more moderately. 8 NIDs were identified as susceptible to PHSMs, including hand, foot, and mouth disease, dengue fever, rubella, scarlet fever, pertussis, mumps, malaria, and Japanese encephalitis. The termination of PHSMs did not cause NIDs resurgence immediately, except for pertussis, which experienced its highest peak in December 2023 since January 2008. Our findings highlight the varied impact of PHSMs on different NIDs and the importance of sustainable, long-term strategies, like vaccine development.

Suggested Citation

  • Kangguo Li & Jia Rui & Wentao Song & Li Luo & Yunkang Zhao & Huimin Qu & Hong Liu & Hongjie Wei & Ruixin Zhang & Buasiyamu Abudunaibi & Yao Wang & Zecheng Zhou & Tianxin Xiang & Tianmu Chen, 2024. "Temporal shifts in 24 notifiable infectious diseases in China before and during the COVID-19 pandemic," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48201-8
    DOI: 10.1038/s41467-024-48201-8
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    References listed on IDEAS

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    1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    2. Thomas Hale & Noam Angrist & Rafael Goldszmidt & Beatriz Kira & Anna Petherick & Toby Phillips & Samuel Webster & Emily Cameron-Blake & Laura Hallas & Saptarshi Majumdar & Helen Tatlow, 2021. "A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker)," Nature Human Behaviour, Nature, vol. 5(4), pages 529-538, April.
    3. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, January.
    4. Q. Sue Huang & Tim Wood & Lauren Jelley & Tineke Jennings & Sarah Jefferies & Karen Daniells & Annette Nesdale & Tony Dowell & Nikki Turner & Priscilla Campbell-Stokes & Michelle Balm & Hazel C. Dobin, 2021. "Impact of the COVID-19 nonpharmaceutical interventions on influenza and other respiratory viral infections in New Zealand," Nature Communications, Nature, vol. 12(1), pages 1-7, December.
    5. Meng-Jie Geng & Hai-Yang Zhang & Lin-Jie Yu & Chen-Long Lv & Tao Wang & Tian-Le Che & Qiang Xu & Bao-Gui Jiang & Jin-Jin Chen & Simon I. Hay & Zhong-Jie Li & George F. Gao & Li-Ping Wang & Yang Yang &, 2021. "Changes in notifiable infectious disease incidence in China during the COVID-19 pandemic," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    6. Aiello, A.E. & Coulborn, R.M. & Perez, V. & Larson, E.L., 2008. "Effect of hand hygiene on infectious disease risk in the community setting: A meta-analysis," American Journal of Public Health, American Public Health Association, vol. 98(8), pages 1372-1381.
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