Abstract
•Introduced NPI-enhanced SIR model with min–max optimization.•SIR extensions for ventilator, ICU needs for symptomatic and asymptomatic patients.•Integrated bed and ventilator capacity planning.•Reduced mortality with non-pharmaceutical interventions integration.•Higher penalty rates link to more deaths under less effective interventions.
The COVID-19 pandemic underscored the urgent need for integrated healthcare resource planning to manage patient surges and reduce mortality. This study presents a novel optimization-based framework that jointly considers healthcare capacity planning and non-pharmaceutical interventions (NPIs), including mask mandates, social distancing, and quarantining. The framework extends traditional compartmental models by distinguishing patient categories—such as hospitalized non-ventilator patients, ventilated patients, and ICU admissions—within a mixed-integer programming (MIP) structure that remains computationally tractable. Applied to the COVID-19 outbreak in New York City, the model optimizes the allocation of hospital beds and ventilators while minimizing deaths. Results show that combining high-effectiveness NPIs with proactive and scalable resource expansions can reduce preventable deaths by up to seventy percent compared to baseline scenarios. The model also reveals that early interventions and sufficient capacity growth are especially critical during the first weeks of a pandemic. By explicitly linking epidemiological dynamics with capacity constraints and treatment prioritization, this study offers a practical decision-support tool for policymakers seeking to strengthen healthcare resilience and responsiveness in the face of public health emergencies.