An acute hospital demand surge planning model for the COVID-19 epidemic using stock-and-flow simulation in Excel: Part 1.

An emerging infectious disease, COVID-19, has spread from an infection cluster in Wuhan City, Hubei Provence, China, to create a global outbreak (pandemic) and, at the time of writing, has infected more than 180,000 people and caused 7,000 deaths.
Epidemiological modelling shows that non-pharmacological interventions (NPI) such as limiting movement of people and social distancing offer the hope of flattening the peak of load on the health care system of each country, and of reducing mortality.
Planning the required acute hospital bed capacity to absorb the expected epidemic surge requires prediction of the magnitude and timing of the local demand on an acute hospital.
This essay describes the science underpinning epidemiological modelling and demonstrates that a basic system dynamic (or stock-and-flow) model of an epidemic can be implemented using widely available spreadsheet software such as Excel.
Using evidence known to date, this basic model illustrates comparable system behaviour to the more sophisticated simulations that are being used to guide the national response to this rapidly developing threat. Specifically, that immediate, nationwide adoption of behaviours such as social distancing could significantly mitigate the effects.
This first model could serve as a useful informational and educational resource at a time of great uncertainty, and further development is now underway to calibrate the model so it can be used to support tactical contingency planning.