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Evaluating Transmission Heterogeneity and Super-Spreading Event of COVID-19 in a Metropolis of China

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  • Yunjun Zhang

    (Department of Biostatistics, School of Public Health, Peking University, Xueyuan Road, Beijing 100191, China
    These authors contributed equally to this work.)

  • Yuying Li

    (Department of Biostatistics, School of Public Health, Peking University, Xueyuan Road, Beijing 100191, China
    These authors contributed equally to this work.)

  • Lu Wang

    (Beijing International Center for Mathematical Research, Peking University, Yiheyuan Road, Beijing 100871, China)

  • Mingyuan Li

    (School of Mathematical Sciences, Peking University, Peking University, Yiheyuan Road, Beijing 100871, China)

  • Xiaohua Zhou

    (Department of Biostatistics, School of Public Health, Peking University, Xueyuan Road, Beijing 100191, China
    Beijing International Center for Mathematical Research, Peking University, Yiheyuan Road, Beijing 100871, China
    Center for Statistical Science, Peking University, Yiheyuan Road, Beijing 100871, China)

Abstract

COVID-19 caused rapid mass infection worldwide. Understanding its transmission characteristics, including heterogeneity and the emergence of super spreading events (SSEs) where certain individuals infect large numbers of secondary cases, is of vital importance for prediction and intervention of future epidemics. Here, we collected information of all infected cases (135 cases) between 21 January and 26 February 2020 from official public sources in Tianjin, a metropolis of China, and grouped them into 43 transmission chains with the largest chain of 45 cases and the longest chain of four generations. Utilizing a heterogeneous transmission model based on branching process along with a negative binomial offspring distribution, we estimated the reproductive number R and the dispersion parameter k (lower value indicating higher heterogeneity) to be 0.67 (95% CI: 0.54–0.84) and 0.25 (95% CI: 0.13–0.88), respectively. A super-spreader causing six infections was identified in Tianjin. In addition, our simulation allowing for heterogeneity showed that the outbreak in Tianjin would have caused 165 infections and sustained for 7.56 generations on average if no control measures had been taken by local government since 28 January. Our results highlighted more efforts are needed to verify the transmission heterogeneity of COVID-19 in other populations and its contributing factors.

Suggested Citation

  • Yunjun Zhang & Yuying Li & Lu Wang & Mingyuan Li & Xiaohua Zhou, 2020. "Evaluating Transmission Heterogeneity and Super-Spreading Event of COVID-19 in a Metropolis of China," IJERPH, MDPI, vol. 17(10), pages 1-11, May.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:10:p:3705-:d:362373
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    References listed on IDEAS

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    1. James O Lloyd-Smith, 2007. "Maximum Likelihood Estimation of the Negative Binomial Dispersion Parameter for Highly Overdispersed Data, with Applications to Infectious Diseases," PLOS ONE, Public Library of Science, vol. 2(2), pages 1-8, February.
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    3. Seth Blumberg & James O Lloyd-Smith, 2013. "Inference of R0 and Transmission Heterogeneity from the Size Distribution of Stuttering Chains," PLOS Computational Biology, Public Library of Science, vol. 9(5), pages 1-17, May.
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