Forecasting Local Surges in COVID-19 Hospitalizations through Adaptive Decision Tree Classifiers

Rachel E Murray-Watson, Xavier Guaracha, Alyssa Bilinski and Reza Yaesoubi
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Rachel E Murray-Watson: School of Public Health, Imperial College London
Xavier Guaracha: Philip R. Lee Institute for Health Policy Studies, University of California San Francisco, San Francisco, CA, USA
Alyssa Bilinski: Department of Health Services, Policy and Practice, Brown University School of Public Health, Providence, RI, USA
Reza Yaesoubi: Philip R. Lee Institute for Health Policy Studies, University of California San Francisco, San Francisco, CA, USA

Medical Decision Making, 2026, vol. 46, issue 3, 371-385

Abstract: Introduction During the COVID-19 pandemic, many communities across the United States experienced surges in hospitalizations, which strained the local hospital capacity. Some risk metrics, such as the Center for Disease Control and Prevention’s (CDC’s) Community Levels, were developed to predict the impact of COVID-19 on the community-level health care system based on routine surveillance data. However, they had limited utility as they were not routinely updated based on accumulating data and were not directly linked to specific outcomes, such as surges in COVID-19 hospitalizations beyond local capacities. Methods In this article, we evaluated decision tree classifiers developed in real time to predict surges in local hospitalizations due to COVID-19 between July 2020 and November 2022. These classifiers would have provided visually intuitive and interpretable decision rules and, by being updated weekly, would have responded to changes in the epidemic. We compared the performance of these classifiers with that of logistic regression and neural network models using various metrics, including the area under the receiver-operating characteristic curve (auROC) and the area under the precision-recall curve (auPRC). Results Decision tree classifiers achieved an auROC of > 80 % for most pandemic weeks and outperformed the CDC’s Community Levels in predicting high hospital occupancy. The auPRC, sensitivity, and specificity of the classifiers varied more substantially over time (between 20 % and 100 % ) and in sync with pandemic waves. Decision tree classifiers demonstrated similar performance compared with logistic regression and neural network models while presenting more interpretable classification rules. Conclusions Using routinely collected hospital surveillance data, decision tree classifiers can be adaptively updated to predict surges in local hospitalizations. However, the sensitivity and specificity of these classifiers could change markedly during different pandemic waves. Highlights A major concern during the COVID-19 pandemic was the risk of exceeding local health care capacity due to COVID-19-related hospitalizations. To assess this risk and inform mitigating strategies, several risk assessment tools were developed during the pandemic. Many of these tools, however, did not predict local outcomes, were not updated as the pandemic progressed, and/or were not interpretable by decision makers. We propose an adaptive framework of decision tree classifiers to predict whether COVID-19–related hospital occupancy would exceed a given capacity threshold. These classifiers demonstrated reasonable and stable prediction performance over time. However, their sensitivity and specificity may change substantially over the course of pandemic waves.

Keywords: COVID-19; machine learning; decision tree; neural networks; logistic regression (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:46:y:2026:i:3:p:371-385

DOI: 10.1177/0272989X251408845

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