Empirical model for short-time prediction of COVID-19 spreading

  1. Martí Català
  2. Sergio Alonso
  3. Enrique Alvarez-Lacalle
  4. Daniel López
  5. Pere-Joan Cardona
  6. Clara Prats

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Abstract

The appearance and fast spreading of Covid-19 took the international community by surprise. Collaboration between researchers, public health workers, and politicians has been established to deal with the epidemic. One important contribution from researchers in epidemiology is the analysis of trends so that both the current state and short-term future trends can be carefully evaluated. Gompertz model has been shown to correctly describe the dynamics of cumulative confirmed cases, since it is characterized by a decrease in growth rate showing the effect of control measures. Thus, it provides a way to systematically quantify the Covid-19 spreading velocity and it allows short-term predictions and longer-term estimations. This model has been employed to fit the cumulative cases of Covid-19 from several European countries. Results show that there are systematic differences in spreading velocity among countries. The model predictions provide a reliable picture of the short-term evolution in countries that are in the initial stages of the Covid-19 outbreak, and may permit researchers to uncover some characteristics of the long-term evolution. These predictions can also be generalized to calculate short-term hospital and intensive care units (ICU) requirements.

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  1. Version published to 10.1371/journal.pcbi.1008431
  2. ScreenIT

    SciScore for 10.1101/2020.05.13.20101329: (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
    Evaluation and propagation of errors: The fitting of the Gompertz function to the data is done with a matlab routine using the minimum least squares method.
    matlab
    suggested: (MATLAB, RRID:SCR_001622)

    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:
    However, the main limitation of the regional approach is the lack of detailed data and/or the difference in the protocols and definitions taken by local authorities. Finally, we would like to note that the use a generic function is an empiric tool to treat future local and global epidemics, as it has been begun recently with other growth functions like Verhust and Richards models [17]. We plan to continuously update the approach employed here to adapt to any special particularity of any new epidemics. Presently, the same data is applied to guide public policy in hospital administrations giving assessment to regional governments regarding the short-term evolution of health needs. In order to take adequate and precise control measurements politicians need updated information of the epidemics and a clear representation of the phase of the epidemic among several countries or in a particular country of the different regions. The short-time predictions area valuable information of great interest to politicians.

    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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  3. Version published to 10.1101/2020.05.13.20101329v1 on medRxiv