  Environments such as shopping malls, airports, or hospital emergency
departments often experience crowding, with many people simultaneously
requesting service. Crowding is highly noisy, with sudden overcrowding
"spikes". Past research has either focused on average behavior or used
context-specific non-generalizable models. Here we show that a stochastic
population model, previously applied to a broad range of natural phenomena, can
aptly describe hospital emergency-department crowding, using data from
five-year minute-by-minute emergency-department records. The model provides
reliable forecasting of the crowding distribution. Overcrowding is highly
sensitive to the patient arrival-flux and length-of-stay: a 10% increase in
arrivals triples the probability of overcrowding events. Expediting patient
exit-rate to shorten the typical length-of-stay by just 20 minutes (8.5%)
reduces severe overcrowding events by 50%. Such forecasting is crucial in
prevention and mitigation of breakdown events. Our results demonstrate that
despite its high volatility, crowding follows a dynamic behavior common to many
natural systems.
