COVID-19 effective reproduction number determination: an application, and a review of issues and influential factors

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Abstract

Objectives

An essential indicator of COVID-19 transmission is the effective reproduction number ( R t ), the number of cases which an infected individual is expected to infect at a particular point in time; curves of the evolution of R t over time (transmission curves) reflect the impact of preventive measures and whether an epidemic is controlled.

Methods

We have created a Shiny/R web application ( https://alfredob.shinyapps.io/estR0/ ) with user-selectable features: open data sources with daily COVID-19 incidences from all countries and many regions, customizable preprocessing options (smoothing, proportional increment, etc.), different MonteCarlo-Markov-Chain estimates of the generation time or serial interval distributions and state-of-the-art R t estimation frameworks (EpiEstim, R 0 ). This application could be used as a tool both to obtain transmission estimates and to perform interactive sensitivity analysis. We have analyzed the impact of these factors on transmission curves. We also have obtained curves at the national and sub-national level and analyzed the impact of epidemic control strategies, superspreading events, socioeconomic factors and outbreaks.

Results

Reproduction numbers showed earlier anticipation compared to active prevalence indicators (14-day cumulative incidence, overall infectivity). In the sensitivity analysis, the impact of different R t estimation methods was moderate/small, and the impact of different serial interval distributions was very small. We couldn’t find conclusive evidence regarding the impact of alleged superspreading events. As a limitation, dataset quality can limit the reliability of the estimates.

Conclusions

The thorough review of many examples of COVID-19 transmission curves support the usage of R t estimates as a robust transmission indicator.

Article activity feed

  1. SciScore for 10.1101/2020.07.15.20154039: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.


    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).


    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    4.5 Study limitations and advantages: In addition to the data limitations, using the serial interval as a surrogate for the generation time has a number of potential issues (32), and the serial interval distributions could be biased (overestimation/underestimation) due to several factors(30). For example, the serial interval might be contracted during the epidemic peak due to early quarantine of symptomatic infected individuals. We have not observed marked epidemiologically-relevant differences between the results obtained with any of the five distributions tested; so we believe it is often acceptable in practical terms to use the serial interval as a surrogate for the generation time. Compartmental models can be used to estimate many infectious disease parameters, including the reproduction numbers(71)(62)(3). These models often divide estimate the initial reproduction number R0 and the control reproduction number Rc (after control measures have been initiated). Stochastic SIR models can also be used to obtain reproduction numbers in the initial phase without preventive measures (R0) and reproduction numbers after preventive measures have been implemented (Rc, control reproduction numbers) which showed some variability between different regions but incontrovertibly demonstrated the efficacy of preventive measures(72). In the UK before the lockdown period the R0 was estimated as 6.94 (6.52-7.39, credible interval) with a SEIR model(3). Initial R0 (February) estimates in Wuhan...

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.


    Results from JetFighter: We did not find any issues relating to colormaps.


    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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