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Reconciling early-outbreak estimates of the basic reproductive number and its uncertainty: a new framework and applications to the novel coronavirus (2019-nCoV) outbreak

Abstract

Background A novel coronavirus (2019-nCoV) has recently emerged as a global threat. As the epidemic progresses, many disease modelers have focused on estimating the basic reproductive number R o, the average number of secondary cases caused by a primary case in an otherwise susceptible population. The modeling approaches and resulting estimates of R o vary widely, despite relying on similar data sources. Aim We aimed to develop a framework for comparing and combining different estimates of R o across a wide range of models. Methods We reviewed 7 model-based analyses of the 2019-nCoV outbreak that were published online between January 23--26, 2020. We decompose their R o estimates into three key quantities: the exponential growth rate r , the mean generation interval, G bar, and the generation-interval dispersion κ. We use a Bayesian multilevel model to construct pooled estimates and measure uncertainties associated with these quantities. Results We find that most early estimates of R o rely on strong assumptions, especially about the generation-interval dispersion. Estimates that rely on narrow generation-interval distributions are overly sensitive to estimates of the exponential growth rate. Conclusion Our results emphasize the importance of propagating uncertainties in all components of R o, including the shape of the generation-interval distribution, in efforts to estimate R o at the outset of an epidemic. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement BMB and DJDE were supported by Natural Sciences and Engineering Research Council (NSERC). ML was supported by Canadian Institutes of Health Research (CIHR). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ### Author Declarations All relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript. Yes All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes R code is available in GitHub (https://github.com/parksw3/nCoV_framework).

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