A modelling study highlights the power of detecting and isolating asymptomatic or very mildly affected individuals for COVID-19 epidemic management

  1. Lía Mayorga
  2. Clara García Samartino
  3. Gabriel Flores
  4. Sofía Masuelli
  5. María V. Sánchez
  6. Luis S. Mayorga
  7. Cristián G. Sánchez

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Abstract

Background

Mathematical modelling of infectious diseases is a powerful tool for the design of management policies and a fundamental part of the arsenal currently deployed to deal with the COVID-19 pandemic.

Methods

We present a compartmental model for the disease where symptomatic and asymptomatic individuals move separately. We introduced healthcare burden parameters allowing to infer possible containment and suppression strategies. In addition, the model was scaled up to describe different interconnected areas, giving the possibility to trigger regionalized measures. It was specially adjusted to Mendoza-Argentina’s parameters, but is easily adaptable for elsewhere.

Results

Overall, the simulations we carried out were notably more effective when mitigation measures were not relaxed in between the suppressive actions. Since asymptomatics or very mildly affected patients are the vast majority, we studied the impact of detecting and isolating them. The removal of asymptomatics from the infectious pool remarkably lowered the effective reproduction number, healthcare burden and overall fatality. Furthermore, different suppression triggers regarding ICU occupancy were attempted. The best scenario was found to be the combination of ICU occupancy triggers (on: 50%, off: 30%) with the detection and isolation of asymptomatic individuals. In the ideal assumption that 45% of the asymptomatics could be detected and isolated, there would be no need for complete lockdown, and Mendoza’s healthcare system would not collapse.

Conclusions

Our model and its analysis inform that the detection and isolation of all infected individuals, without leaving aside the asymptomatic group is the key to surpass this pandemic.

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  1. Version published to 10.1186/s12889-020-09843-7
  2. Version published to 10.21203/rs.3.rs-33492/v3 on Research Square
  3. Version published to 10.21203/rs.3.rs-33492/v2 on Research Square
  4. Version published to 10.21203/rs.3.rs-33492/v1 on Research Square
  5. ScreenIT

    SciScore for 10.1101/2020.04.23.20077255: (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
    Based on recent calculations on the IFR, of 0.39-1.33% [3], and most recently of 0.01-0.19% inferred from Iceland’s statistics[4,5], we adapted the parameters for our model to our country.
    Iceland’s
    suggested: None

    Results from OddPub: Thank you for sharing your code.

    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.

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  6. Version published to 10.1101/2020.04.23.20077255v2 on medRxiv
  7. Version published to 10.1101/2020.04.23.20077255v1 on medRxiv