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

Reza Yaesoubi (), Shiying You, Qin Xi, Nicolas A. Menzies, Ashleigh Tuite, Yonatan H. Grad and Joshua A. Salomon
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Reza Yaesoubi: Yale School of Public Health
Shiying You: 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
Joshua A. Salomon: Stanford University School of Medicine

Health Care Management Science, 2023, vol. 26, issue 2, No 7, 312 pages

Abstract: 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.

Keywords: Surveillance; Prediction; Decision tree; Machine learning; Simulation; COVID-19 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10729-023-09629-4

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