We develop a model for a regional decision-maker to analyze the requirement of medical equipment capacity in the early stages of a spread of infections. We use a stochastic differential equation to capture the growth of infections in a community spread and shutdown model. We develop results to determine shutdown time, to show how to compensate for limited medical equipment capacity, and to show how capacity-sharing across regions can deliver a peak-timing benefit beyond traditional risk pooling.

During the early weeks of the COVID-19 pandemic, hospitals managed surge capacities by transferring patients among different hospitals within the same health network, repurposing operating rooms as ICU beds, and cancelling elective surgeries. Using publicly available data we develop an analytical framework to study how these policies can be implemented, individually or in combination, in order to optimize the pandemic response in a region, while delivering care to the uninfected patients.

The classical SIR model and its variants have succeeded in predicting infectious diseases' spread. To better capture the COVID-19 outbreak, we extend the SIR model to impose a testing capacity. We study how to choose the best type of testing method, how to allocate limited testing capacity over time and across symptomatic and asymptomatic people. We use the COVID-19 data and a sliding window method to calibrate our model and point out its public policy implications.