An improved method to estimate the effective reproduction number of the COVID-19 pandemic: lessons from its application in Greece

  1. Theodore Lytras
  2. Vana Sypsa
  3. Demosthenes Panagiotakos
  4. Sotirios Tsiodras

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Abstract

Introduction

Monitoring the time-varying effective reproduction number R t is crucial for assessing the evolution of the COVID-19 pandemic. We present an improved method to estimate R t and its application to routine surveillance data from Greece.

Methods

Our method extends that of Cori et al (2013), adding Bayesian imputation of missing symptom onset dates, imputation of infection times using an external estimate of the incubation period, and an adjustment for reporting delay. To facilitate its use, we provide an R software package named “bayEStim”. We applied the method to COVID-19 surveillance data from Greece, and examined the resulting R t estimates in relation to control measures applied, in order to assess their effectiveness. We also associated R t , as a measure of transmissibility, to population mobility as recorded in Google data and to ambient temperature. We used a serial interval between 4 and 7.5 days, and a median incubation period of 5.1 days.

Results

In Greece R t fell rapidly as the first control measures were introduced, dropping below 1 at least a week before a full lockdown came into effect. In mid-July R t started increasing again, as increased mobility associated with tourism activity was observed. Each 10% of increase in relative mobility increased R t by 8.1% (95% CrI 6.1–10.2%), whereas each unit celsius of temperature increase decreased R t by 4.6% (95% CrI 5.4–13.7%).

Conclusions

Mobility patterns significantly affect R t . Most of the reduction in COVID-19 transmissibility in Greece occurred already before the lockdown, likely as a result of decreased population mobility. Lower viral transmissibility in summer does not appear sufficient to counterbalance the increased mobility due to tourism. Monitoring R t is an essential component of COVID-19 surveillance, and it is crucial for correctly assessing the effect of control measures.

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  1. ScreenIT

    SciScore for 10.1101/2020.09.19.20198028: (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:
    Rt is a useful and easily understandable metric for this purpose, but its reliable estimation presents considerable challenges [7], especially given the inherent limitations of surveillance data [19]. Our method to estimate Rt does not require structural assumptions other than the serial interval distribution, and introduces several improvements that make it appropriate for use in a surveillance context. It works with minimal or incomplete data, it largely adjusts for reporting delays, and can produce time-accurate estimates using an external estimate of the incubation period; the latter is essential for correctly assessing the effectiveness of control measures, as any intervention to reduce infection rates will only be reflected in case reporting rates after a substantial period of time. Our method is also very easy to use through our “bayEStim” package for the R software environment. On the other hand, there are certain limitations with our approach. Imprecision can be high especially with low case counts, as our model incorporates all sources of uncertainty in Rt, serial interval, incubation period, missing symptom onset dates and delay distribution. Bayesian imputation of infection times also introduces smoothing, which is both appropriate and to an extent desirable, but can also blur abrupt changes in Rt [7]. This should be kept in mind when interpreting the results. Substantial changes in testing or ascertainment rates over time can bias the results, as can the presence...

    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.
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  2. Version published to 10.1101/2020.09.19.20198028v1 on medRxiv