Abstract
An operationally implementable predictive model has been developed to forecast the number of COVID-19 infections in the patient population, hospital floor and ICU censuses, ventilator and related supply chain demand. The model is intended for clinical, operational, financial and supply chain leaders and executives of a comprehensive healthcare system responsible for making decisions that depend on epidemiological contingencies. This paper describes the model that was implemented at NorthShore University HealthSystem and is applicable to any communicable disease whose risk of reinfection for the duration of the pandemic is negligible.
Highlights
Upon its emergence in 2020, the COVID-19 pandemic presented immediate challenges to the operation of NorthShore University HealthSystem (NS), a comprehensive regional healthcare system located in the northern part of Chicago, Illinois, and its suburbs
A plausible alternative, an Individual-Based Model (IBM) [7], requires substantially more effort devoted to implementation and analysis [8], and is more difficult to explain to the target audience than SIR
We set the pandemic start date to March 10, 2020. The date for this example was arbitrarily chosen from past history with the requirements that the number of new cases, patient admissions and censuses on the floor, in the intensive care unit (ICU) and attached to a ventilator be reasonable large to avoid instability and that the trajectory of the pandemic be fairly well established
Summary
Upon its emergence in 2020, the COVID-19 pandemic presented immediate challenges to the operation of NorthShore University HealthSystem (NS), a comprehensive regional healthcare system located in the northern part of Chicago, Illinois, and its suburbs. The lack of reliable population data posed additional difficulty in implementing a usable model. Additional constraints of robustness, distributability and transparency imposed further requirements on the choice of the governing equations, solution algorithm and software implementation. During the initial stage of the pandemic, the model was delivered to the operational stakeholders daily; as time progressed, the frequency of dissemination was changed to once or twice a week, depending on the severity of the situation. At the onset of the pandemic, the Clinical Analytics team was tasked with providing a reliable, scalable solution relevant to the local epidemiological situation [1].
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