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Generating simple classification rules to predict local surges in COVID-19 hospitalizations

Author

Listed:
  • Reza Yaesoubi

    (Yale School of Public Health
    Yale School of Public Health)

  • Shiying You

    (Yale School of Public Health
    Yale School of Public Health)

  • Qin Xi

    (Yale School of Public Health)

  • Nicolas A. Menzies

    (Harvard T.H. Chan School of Public Health)

  • Ashleigh Tuite

    (University of Toronto Dalla Lana School of Public Health)

  • Yonatan H. Grad

    (Harvard T. H. Chan School of Public Health
    Harvard Medical School)

  • Joshua A. Salomon

    (Stanford University School of Medicine)

Abstract

Low rates of vaccination, emergence of novel variants of SARS-CoV-2, and increasing transmission relating to seasonal changes and relaxation of mitigation measures leave many US communities at risk for surges of COVID-19 that might strain hospital capacity, as in previous waves. The trajectories of COVID-19 hospitalizations differ across communities depending on their age distributions, vaccination coverage, cumulative incidence, and adoption of risk mitigating behaviors. Yet, existing predictive models of COVID-19 hospitalizations are almost exclusively focused on national- and state-level predictions. This leaves local policymakers in urgent need of tools that can provide early warnings about the possibility that COVID-19 hospitalizations may rise to levels that exceed local capacity. In this work, we develop a framework to generate simple classification rules to predict whether COVID-19 hospitalization will exceed the local hospitalization capacity within a 4- or 8-week period if no additional mitigating strategies are implemented during this time. This framework uses a simulation model of SARS-CoV-2 transmission and COVID-19 hospitalizations in the US to train classification decision trees that are robust to changes in the data-generating process and future uncertainties. These generated classification rules use real-time data related to hospital occupancy and new hospitalizations associated with COVID-19, and when available, genomic surveillance of SARS-CoV-2. We show that these classification rules present reasonable accuracy, sensitivity, and specificity (all ≥ 80%) in predicting local surges in hospitalizations under numerous simulated scenarios, which capture substantial uncertainties over the future trajectories of COVID-19. Our proposed classification rules are simple, visual, and straightforward to use in practice by local decision makers without the need to perform numerical computations.

Suggested Citation

  • Reza Yaesoubi & Shiying You & Qin Xi & Nicolas A. Menzies & Ashleigh Tuite & Yonatan H. Grad & Joshua A. Salomon, 2023. "Generating simple classification rules to predict local surges in COVID-19 hospitalizations," Health Care Management Science, Springer, vol. 26(2), pages 301-312, June.
  • Handle: RePEc:kap:hcarem:v:26:y:2023:i:2:d:10.1007_s10729-023-09629-4
    DOI: 10.1007/s10729-023-09629-4
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    References listed on IDEAS

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    1. Sylvain Gandon & Margaret J. Mackinnon & Sean Nee & Andrew F. Read, 2001. "Imperfect vaccines and the evolution of pathogen virulence," Nature, Nature, vol. 414(6865), pages 751-756, December.
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