Public policy and economic dynamics of COVID-19 spread: A mathematical modeling study

  1. Uri Goldsztejn
  2. David Schwartzman
  3. Arye Nehorai

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Abstract

With the COVID-19 pandemic infecting millions of people, large-scale isolation policies have been enacted across the globe. To assess the impact of isolation measures on deaths, hospitalizations, and economic output, we create a mathematical model to simulate the spread of COVID-19, incorporating effects of restrictive measures and segmenting the population based on health risk and economic vulnerability. Policymakers make isolation policy decisions based on current levels of disease spread and economic damage. For 76 weeks in a population of 330 million, we simulate a baseline scenario leaving strong isolation restrictions in place, rapidly reducing isolation restrictions for non-seniors shortly after outbreak containment, and gradually relaxing isolation restrictions for non-seniors. We use 76 weeks as an approximation of the time at which a vaccine will be available. In the baseline scenario, there are 235,724 deaths and the economy shrinks by 34.0%. With a rapid relaxation, a second outbreak takes place, with 525,558 deaths, and the economy shrinks by 32.3%. With a gradual relaxation, there are 262,917 deaths, and the economy shrinks by 29.8%. We also show that hospitalizations, deaths, and economic output are quite sensitive to disease spread by asymptomatic people. Strict restrictions on seniors with very gradual lifting of isolation for non-seniors results in a limited number of deaths and lesser economic damage. Therefore, we recommend this strategy and measures that reduce non-isolated disease spread to control the pandemic while making isolation economically viable.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.13.20062802: (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: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our study has several limitations. We do not model how individuals react to lengthy restrictions related to infectious disease. We also do not model any individual-level differences in behavior or disease spread. This model could also be modified to incorporate the effects of testing and to have probability of infection depend on job type (31). Our model does not allow for any differences in disease spread between new and repeated contacts, which some models have incorporated (32; 33; 34). We also model isolation restrictions as completely eliminating the risk of infection. The less effective isolation policies are at lowering the risk of infection, the less attractive isolation policies become. Since testing policies can allow isolation restrictions to become more targeted, incorporating them can make the non-isolated state less costly for disease spread. Additionally, due to the newness of COVID-19, the exact disease parameters and the effects of seasonality are not precisely known (35; 36). Because of the uncertainty surrounding disease parameters, which change as the COVID-19 situation progresses, and because of the limitations of our model, our numbers should not be taken as literal predictions, but rather as illustrating the consequences of different policy approaches and individual behaviors. Our model has applications to different types of populations, as well as to future pandemics. During a fatal infectious disease outbreak of lengthy duration, making policy decisio...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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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  2. Version published to 10.1371/journal.pone.0244174
  3. Version published to 10.1101/2020.04.13.20062802 on medRxiv