The current COVID-19 pandemic has kick-started a world of forecasting and “modeling” designed to anticipate the need for scarce medical resources.
Across the country, modelers are producing estimates for leadership that have been orders of magnitude wrong, like in the case of the depletion of hospital beds.
In Emergency Preparedness, many risk managers are locked into a deterministic model, but at their core, they are based on
single-point estimates of various “uncertain” variables.
These assumptive single-point estimates are used throughout the spectrum of problems and response scenarios healthcare professionals face, from estimating available hospital beds, mortality expectation, general population evacuation, medical special needs sheltering, etc.
In almost every case, there is considerable front-end debates and meetings to generate “planning factors” and “rate-limiting factors”.
In other words, everyone is looking for “the number”.
In this webinar, we will review the history of these planning efforts over the last 15 years and glance at some stochastic models we’ve employed based on distributions of data with @RISK
Utilizing Monte Carlo simulation in statistical methods has been validated by real-world results in providing the small efforts in formulating a better approach.