Tracking R of COVID-19: A new real-time estimation using the Kalman filter

  1. Francisco Arroyo-Marioli
  2. Francisco Bullano
  3. Simas Kucinskas
  4. Carlos Rondón-Moreno

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Abstract

We develop a new method for estimating the effective reproduction number of an infectious disease ( R ) and apply it to track the dynamics of COVID-19. The method is based on the fact that in the SIR model, R is linearly related to the growth rate of the number of infected individuals. This time-varying growth rate is estimated using the Kalman filter from data on new cases. The method is easy to implement in standard statistical software, and it performs well even when the number of infected individuals is imperfectly measured, or the infection does not follow the SIR model. Our estimates of R for COVID-19 for 124 countries across the world are provided in an interactive online dashboard , and they are used to assess the effectiveness of non-pharmaceutical interventions in a sample of 14 European countries.

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  1. Version published to 10.1371/journal.pone.0244474
  2. Version published to 10.1101/2020.04.19.20071886v3 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.04.19.20071886: (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

    Software and Algorithms
    SentencesResources
    In particular, we use the pystan interface to call Stan from Python. A.8 Empirical Validation: In this section, we perform two empirical validation exercises to check the performance of our estimates in practice.
    Python
    suggested: (IPython, RRID:SCR_001658)
    For information on movement, we use aggregated smartphone location data collected by Google and published in their “COVID-19 Community Mobility Reports” (Google, 2020)
    Google
    suggested: (Google, RRID:SCR_017097)

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

    About SciScore

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  4. Version published to 10.1101/2020.04.19.20071886v2 on medRxiv
  5. Version published to 10.1101/2020.04.19.20071886v1 on medRxiv