Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China
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Open Access
- 10 July 2020
- journal article
- research article
- Published by F1000 Research Ltd in Wellcome Open Research
Abstract
Background: A novel coronavirus disease (COVID-19) outbreak has now spread to a number of countries worldwide. While sustained transmission chains of human-to-human transmission suggest high basic reproduction number R 0, variation in the number of secondary transmissions (often characterised by so-called superspreading events) may be large as some countries have observed fewer local transmissions than others. Methods: We quantified individual-level variation in COVID-19 transmission by applying a mathematical model to observed outbreak sizes in affected countries. We extracted the number of imported and local cases in the affected countries from the World Health Organization situation report and applied a branching process model where the number of secondary transmissions was assumed to follow a negative-binomial distribution. Results: Our model suggested a high degree of individual-level variation in the transmission of COVID-19. Within the current consensus range of R 0 (2-3), the overdispersion parameter k of a negative-binomial distribution was estimated to be around 0.1 (median estimate 0.1; 95% CrI: 0.05-0.2 for R0 = 2.5), suggesting that 80% of secondary transmissions may have been caused by a small fraction of infectious individuals (~10%). A joint estimation yielded likely ranges for R 0 and k (95% CrIs: R 0 1.4-12; k 0.04-0.2); however, the upper bound of R 0 was not well informed by the model and data, which did not notably differ from that of the prior distribution. Conclusions: Our finding of a highly-overdispersed offspring distribution highlights a potential benefit to focusing intervention efforts on superspreading. As most infected individuals do not contribute to the expansion of an epidemic, the effective reproduction number could be drastically reduced by preventing relatively rare superspreading events.Keywords
Funding Information
- The Nakajima Foundation
- Alan Turing Institute
- Wellcome Trust (206250, 210758)
This publication has 18 references indexed in Scilit:
- Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected PneumoniaNew England Journal of Medicine, 2020
- A Novel Coronavirus from Patients with Pneumonia in China, 2019New England Journal of Medicine, 2020
- The role of superspreading in Middle East respiratory syndrome coronavirus (MERS-CoV) transmissionEurosurveillance, 2015
- Detecting Differential Transmissibilities That Affect the Size of Self-Limited OutbreaksPLoS Pathogens, 2014
- Inference of R0 and Transmission Heterogeneity from the Size Distribution of Stuttering ChainsPLoS Computational Biology, 2013
- Dynamics of colloids in confined geometriesJournal of Physics: Condensed Matter, 2011
- Maximum Likelihood Estimation of the Negative Binomial Dispersion Parameter for Highly Overdispersed Data, with Applications to Infectious DiseasesPLOS ONE, 2007
- Floods fail to save canyon beachesPublished by Springer Nature ,2005
- Superspreading and the effect of individual variation on disease emergenceNature, 2005
- Performance of the Gibbs, Hit-and-Run, and Metropolis SamplersJournal of Computational and Graphical Statistics, 1993