Managing bed capacity and timing of interventions: a COVID-19 model considering behavior and underreporting

  1. Victoria May P. Mendoza
  2. Renier Mendoza
  3. Youngsuk Ko
  4. Jongmin Lee
  5. Eunok Jung

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Abstract

Introduction

At the start of the pandemic, the Philippine capital Metro Manila was placed under a strict lockdown termed Enhanced Community Quarantine (ECQ). When ECQ was eased to General Community Quarantine (GCQ) after three months, healthcare systems were soon faced with a surge of COVID-19 cases, putting most facilities at high or critical risk and prompting a return to a stricter policy.

Methods

We developed a mathematical model considering behavior changes and underreporting to represent the first major epidemic wave in Metro Manila. Key parameters were fitted to the cumulative cases in the capital from March to September 2020. A bi-objective optimization problem was formulated that allows easing of restrictions at an earlier time and minimizes the necessary additional beds to ensure sufficient capacity in healthcare facilities once ECQ was lifted.

Results

If behavior was changed one to four weeks earlier before GCQ, then the cumulative number of cases can be reduced by up to 55% and the peak delayed by up to four weeks. Increasing the reporting ratio during ECQ threefold may increase the reported cases by 23% but can reduce the total cases, including the unreported, by 61% on June 2020. If GCQ began on May 28, 2020, 48 beds should have been added per day to keep the capacity only at high-risk (75% occupancy). Among the optimal solutions, the peak of cases is lowest if ECQ was lifted on May 20, 2020 and with at least 56 additional beds per day.

Conclusion

Since infectious diseases are likely to reemerge, the formulated model can be used as a decision support tool to improve existing policies and plan effective strategies that can minimize the socioeconomic impact of strict lockdown measures and ensure adequate healthcare capacity.

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    SciScore for 10.1101/2022.03.29.22273148: (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 our simulations, we rely on the PRCC Matlab codes presented in [42].
    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: 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.
    • Thank you for including a protocol registration statement.

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2022.03.29.22273148 on medRxiv
  3. Version published to 10.3934/math.2023114