Modeling the COVID-19 outbreak in Ecuador: Is it the right time to lift social distancing containment measures?

  1. Miguel Reina Ortiz
  2. Vinita Sharma

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Objective. Model the effect of partial and full reversal of containment measures on COVID-19 morbidity and mortality in Ecuador. Methods. Susceptible, Infected, Recovered (SIR) models were used to simulate the transmission dynamics of COVID-19 before and after the implementation (and reversal) of containment measures. A Healthcare Compartmental Epidemic Model (HeCEM), which accounts for Hospital and Intensive Care Unit admission rates, was developed to also simulate the effect of reversing social distancing containment measures. Reported COVID-19 cases between February 29th and April 23rd, 2020 were obtained from the Servicio Nacional de Gesti&oacuten de Riesgos y Emergencia, the national emergency management office of Ecuador. An ARIMA model was used to forecast reported number of cases based on the reported number of cases. SIR, HeCEM, and ARIMA model prediction errors were estimated. Results. SIR and HeCEM models predict that, at the moment, hospital and ICU bed needs for COVID-19 patients exceed the capacity in Ecuador. Partial or full reversal of containment measures before reaching the point where hospital and ICU beds are enough to meet the expected demand will result in secondary waves that delay reaching this equilibrium, resulting in thousands of excess deaths. Forecasts predict over 50,000 reported COVID-19 cases by July 25th, 2020. Conclusion. Partial reversal of containment measures should occur only after enough hospital and beds are available to meet the demand.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.05.21.20109520: (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: We detected the following sentences addressing limitations in the study:
    There are some limitations of this study. First, the study simulates the outbreak for the whole nation. It is important to keep in mind that in-country variations might exist. Similar approaches should be done at a local scale – we have started by providing two local HeCEM models. Second, the models assume that once a person recovers, they will not be re-infected. There is limited evidence regarding lifelong COVID-19 immunity. (33) Third, our models do not consider the socioeconomic impact of containment measures and therefore it does not include them in the analysis of the impact of reversing such measures. In terms of forecasting, the ARIMA models are based on the officially reported figures, which are believed to be an underrepresentation of the real number of cases. (24)

    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

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.05.21.20109520v1 on medRxiv