Stanford study reveals why COVID19 forecasts failed

…models failed when they used more speculation and theoretical assumptions and tried to predict long-term outcomes, e.g. using early SIR-based models to predict what would happen in the entire season. However, even forecasting built directly on data alone fared badly. E.g., the IHME failed to yield accurate predictions or accurate estimates of uncertainty. Even for short-term forecasting when the epidemic wave has waned, models presented confusingly diverse predictions with huge uncertainty.


…epidemic forecasting continued to thrive, perhaps because vastly erroneous predictions typically lacked serious consequences. Actually, erroneous predictions may have been even useful. A wrong, doomsday prediction may incentivize people towards better personal hygiene. Problems starts when public leaders take (wrong) predictions too seriously, considering them crystal balls without understanding their uncertainty and the assumptions made.