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Reconstruction of the full transmission dynamics of COVID-19 in Wuhan

Author

Listed:
  • Xingjie Hao

    (Huazhong University of Science and Technology
    Huazhong University of Science and Technology)

  • Shanshan Cheng

    (Huazhong University of Science and Technology
    Huazhong University of Science and Technology)

  • Degang Wu

    (Huazhong University of Science and Technology
    Huazhong University of Science and Technology)

  • Tangchun Wu

    (Huazhong University of Science and Technology
    Huazhong University of Science and Technology
    Huazhong University of Science and Technology)

  • Xihong Lin

    (Harvard T. H. Chan School of Public Health
    Harvard University
    Broad Institute of MIT and Harvard)

  • Chaolong Wang

    (Huazhong University of Science and Technology
    Huazhong University of Science and Technology
    Huazhong University of Science and Technology)

Abstract

As countries in the world review interventions for containing the pandemic of coronavirus disease 2019 (COVID-19), important lessons can be drawn from the study of the full transmission dynamics of its causative agent—severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)— in Wuhan (China), where vigorous non-pharmaceutical interventions have suppressed the local outbreak of this disease1. Here we use a modelling approach to reconstruct the full-spectrum dynamics of COVID-19 in Wuhan between 1 January and 8 March 2020 across 5 periods defined by events and interventions, on the basis of 32,583 laboratory-confirmed cases1. Accounting for presymptomatic infectiousness2, time-varying ascertainment rates, transmission rates and population movements3, we identify two key features of the outbreak: high covertness and high transmissibility. We estimate 87% (lower bound, 53%) of the infections before 8 March 2020 were unascertained (potentially including asymptomatic and mildly symptomatic individuals); and a basic reproduction number (R0) of 3.54 (95% credible interval 3.40–3.67) in the early outbreak, much higher than that of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS)4,5. We observe that multipronged interventions had considerable positive effects on controlling the outbreak, decreasing the reproduction number to 0.28 (95% credible interval 0.23–0.33) and—by projection—reducing the total infections in Wuhan by 96.0% as of 8 March 2020. We also explore the probability of resurgence following the lifting of all interventions after 14 consecutive days of no ascertained infections; we estimate this probability at 0.32 and 0.06 on the basis of models with 87% and 53% unascertained cases, respectively—highlighting the risk posed by substantial covert infections when changing control measures. These results have important implications when considering strategies of continuing surveillance and interventions to eventually contain outbreaks of COVID-19.

Suggested Citation

  • Xingjie Hao & Shanshan Cheng & Degang Wu & Tangchun Wu & Xihong Lin & Chaolong Wang, 2020. "Reconstruction of the full transmission dynamics of COVID-19 in Wuhan," Nature, Nature, vol. 584(7821), pages 420-424, August.
    • Handle: RePEc:nat:nature:v:584:y:2020:i:7821:d:10.1038_s41586-020-2554-8
    DOI: 10.1038/s41586-020-2554-8
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    Cited by:

    1. William E. Allen & Han Altae-Tran & James Briggs & Xin Jin & Glen McGee & Andy Shi & Rumya Raghavan & Mireille Kamariza & Nicole Nova & Albert Pereta & Chris Danford & Amine Kamel & Patrik Gothe & Evr, 2020. "Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing," Nature Human Behaviour, Nature, vol. 4(9), pages 972-982, September.
    2. Singh, Anurag & Arquam, Md, 2022. "Epidemiological modeling for COVID-19 spread in India with the effect of testing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    3. Choi, K. & Choi, Hoyun & Kahng, B., 2022. "COVID-19 epidemic under the K-quarantine model: Network approach," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    4. Zhang, Hui & Xu, Min & Ouyang, Min, 2024. "A multi-perspective functionality loss assessment of coupled railway and airline systems under extreme events," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    5. Can Wang & Xianming Meng & Mahinda Siriwardana & Tien Pham, 2022. "The impact of COVID-19 on the Chinese tourism industry," Tourism Economics, , vol. 28(1), pages 131-152, February.
    6. de Souza, Silvio L.T. & Batista, Antonio M. & Caldas, Iberê L. & Iarosz, Kelly C. & Szezech Jr, José D., 2021. "Dynamics of epidemics: Impact of easing restrictions and control of infection spread," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    7. Ramesh Neupane & Anup K C & Manoj Aryal & Kedar Rijal, 2021. "Status of ecotourism in Nepal: a case of Bhadaure-Tamagi village of Panchase area," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(11), pages 15897-15920, November.
    8. Hazhir Rahmandad & Tse Yang Lim & John Sterman, 2021. "Behavioral dynamics of COVID‐19: estimating underreporting, multiple waves, and adherence fatigue across 92 nations," System Dynamics Review, System Dynamics Society, vol. 37(1), pages 5-31, January.
    9. Moritz Kersting & Andreas Bossert & Leif Sörensen & Benjamin Wacker & Jan Chr. Schlüter, 2021. "Predicting effectiveness of countermeasures during the COVID-19 outbreak in South Africa using agent-based simulation," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-15, December.
    10. Bo Huang & Jionghua Wang & Jixuan Cai & Shiqi Yao & Paul Kay Sheung Chan & Tony Hong-wing Tam & Ying-Yi Hong & Corrine W. Ruktanonchai & Alessandra Carioli & Jessica R. Floyd & Nick W. Ruktanonchai & , 2021. "Integrated vaccination and physical distancing interventions to prevent future COVID-19 waves in Chinese cities," Nature Human Behaviour, Nature, vol. 5(6), pages 695-705, June.
    11. Lifeng Zhang & Roy E. Welsch & Zhi Cao, 2022. "The Transmission, Infection Prevention, and Control during the COVID-19 Pandemic in China: A Retrospective Study," IJERPH, MDPI, vol. 19(5), pages 1-15, March.
    12. Hazhir Rahmandad, 2022. "Behavioral responses to risk promote vaccinating high‐contact individuals first," System Dynamics Review, System Dynamics Society, vol. 38(3), pages 246-263, July.
    13. Jeffrey E. Harris, 2021. "Los Angeles County SARS-CoV-2 Epidemic: Critical Role of Multi-generational Intra-household Transmission," Journal of Bioeconomics, Springer, vol. 23(1), pages 55-83, April.
    14. Wei Duan, 2021. "Matrix-Based Formulation of Heterogeneous Individual-Based Models of Infectious Diseases: Using SARS Epidemic as a Case Study," IJERPH, MDPI, vol. 18(11), pages 1-20, May.
    15. Boeing, Philipp & Wang, Yihan, 2021. "Decoding China's Covid-19 "virus exceptionalism": Community-based digital contact tracing in Wuhan," ZEW Discussion Papers 21-028, ZEW - Leibniz Centre for European Economic Research.
    16. Ma. Melinda C. Concepcion, 2024. "Unmasking the Lived Experiences of Hotel Workers on the Impact of Covid-19," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(1), pages 1291-1306, January.
    17. Liu, Shasha & Yamamoto, Toshiyuki, 2022. "Role of stay-at-home requests and travel restrictions in preventing the spread of COVID-19 in Japan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 1-16.
    18. Derek Huang & Huanyu Tao & Qilong Wu & Sheng-You Huang & Yi Xiao, 2021. "Modeling of the Long-Term Epidemic Dynamics of COVID-19 in the United States," IJERPH, MDPI, vol. 18(14), pages 1-17, July.
    19. Bote Qi & Jingwang Tan & Qingwen Zhang & Meng Cao & Xingxiong Wang & Yu Zou, 2021. "Unfixed Movement Route Model, Non-Overcrowding and Social Distancing Reduce the Spread of COVID-19 in Sporting Facilities," IJERPH, MDPI, vol. 18(15), pages 1-9, August.
    20. Jiawei Xu & Yincai Tang, 2021. "Bayesian Framework for Multi-Wave COVID-19 Epidemic Analysis Using Empirical Vaccination Data," Mathematics, MDPI, vol. 10(1), pages 1-22, December.
    21. Hyukpyo Hong & Eunjin Eom & Hyojung Lee & Sunhwa Choi & Boseung Choi & Jae Kyoung Kim, 2024. "Overcoming bias in estimating epidemiological parameters with realistic history-dependent disease spread dynamics," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    22. Keqiang Dong & Liao Guo, 2021. "Research on the Spatial Correlation and Spatial Lag of COVID-19 Infection Based on Spatial Analysis," Sustainability, MDPI, vol. 13(21), pages 1-16, October.

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