Interpretations from COVID-19 forecasting models make headlines nearly every day lately. To interpret the results of a predictive analytics model you have to understand the assumptions behind the model and its methods. I’m compiling a catalog of the more prominent models, along with ensemble visualizations that combine the forecasts of all of the models.

University of Texas COVID-19 model builds on the IHME model by adding real-time daily social-distancing data in order to project deaths from the first wave.
Arguably the most influential computer forecasting model in history, the first model from Imperial College was the one that led the UK and US governments to begin mitigation measures.
One of the most widely influential COVID-19 forecasting models has also been one of the most misinterpreted.
A model aimed at local forecasts projects a rapid spread of coronavirus in Miami-Dade and Broward counties, based on mobility, population density, age, insurance status, smoking prevalence, and weather.

Don’t treat the center line of the IHME forecast charts as a precise prediction, any more than you would assume that a hurricane will follow the center line on the NHC hurricane forecast cone images. The forecast is a wide range, not a specific number.

The widely-cited IHME model does not forecast that the COVID-19 epidemic will peak

No report from any forecast model has ever had such a significant impact on worldwide human society.