COVID-19 Models for Hospital Surge Capacity Planning: A Systematic Review

Abstract

Objective: Health system preparedness for COVID-19 includes projecting the number and timing of cases requiring various types of treatment. Several tools were developed to assist in this planning process. This review highlights models that project both caseload and hospital capacity requirements over time. Methods: We systematically reviewed the medical and engineering literature according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We completed searches using PubMed, EMBASE, ISI Web of Science, Google Scholar and the Google search engine. Results: The search strategy identified 690 articles. For detailed review, we selected six models that met our pre-defined criteria. Half of the models did not include age-stratified parameters, and only one included the option to represent a second wave. Hospital patient flow was simplified in all models; however, some considered more complex patient pathways. One model included fatality ratios with Length of Stay (LOS) adjustments for survivors versus those who die, and accommodated different LOS for critical care patients with or without a ventilator. Conclusion: The results of our study provide information to physicians, hospital administrators, emergency response personnel and governmental agencies on available models for preparing scenario-based plans for responding to the COVID-19 or similar type of outbreak.