COVID-19 in regions with low prevalence and low density of population. An uncertainty dynamic modeling approach

  1. José M. Garrido
  2. David Martínez-Rodríguez
  3. Fernando Rodríguez-Serrano
  4. Javier Díez-Domingo
  5. COVID-19_Granada Study Group
  6. Rafael-J. Villanueva

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Abstract

The coronavirus disease 2019 (COVID-19) that emerged in China at the end of 2019 has spread worldwide. In this article, we present a mathematical SEIR model focused on analysing the transmission dynamics of COVID-19, the patients circulating in the hospitals and evaluating the effects of health policies and vaccination on the control of the pandemic. We tested the model using registered cases and population data from the province of Granada (Spain), that represents a population size near 1 million citizens with low density of population and low prevalence. After calibrating the model with the data obtained from 15 March to 22 September 2020, we simulate different vaccination scenarios - including effectiveness and availability date - in order to study the possible evolution of the disease. The results show that: 1) infected will increase until 5.6% - 7.4% of the total population over next 3-4 months (2nd wave); 2) vaccination seems not to be enough to face the pandemic and other strategies should be used; 3) we also support the claim of the WHO about the effectiveness of the vaccine, that should be, at least, of 50% to represent a substantial progress against the COVID-19; 4) after the 2nd wave, the return to normal life should be controlled and gradual to avoid a 3rd wave. The proposed study may be a useful tool for giving insight into the transmission dynamics of SARS-CoV-2 and to design vaccination and health policies.

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    SciScore for 10.1101/2020.10.21.20215418: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    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: 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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  2. Version published to 10.1101/2020.10.21.20215418v1 on medRxiv