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Starting with just two models, 4 weeks ago the CDC began publishing forecasts of COVID-19 hospitalizations 4 weeks into the future. While the number of models have since grown to 5, the models are producing highly divergent numbers.
So far, in the month of June, each week's forecast has produced an ever-widening range of predictions.
- On June 10th, the forecast estimated that there would be between 2,500 and 12,000 new COVID-19 hospitalizations per day in the United States on July 1st. Nearly a 500% spread.
- A week later, the forecast estimated between 1,000 to 11,000 new COVID-19 hospitalizations per day 4 weeks out. An even greater 1100% spread.
- And today, that range widens once more, to between 1,000 and 15,000 cases a month from now. A 1500% spread.
Over time, these models will become more refined, and we'll have a better idea which methods are the most reliable. For now, if they were forecasting rain probabilities for the 4th of July, you wouldn't know whether to bring an umbrella or a row boat.
We'll continue to watch these models to see how each performs going forward.
Hospitalization Forecasts
Updated June 23, 2020
Interpretation of Forecasts of New Hospitalizations
- Numbers and trends in new COVID-19 hospitalizations per day vary among national forecasts. This week, national forecasts from two models suggest an increase in the number of daily hospitalizations over the next four weeks, while the other three models forecast stable numbers or slight declines. By mid-July, the forecasts estimate 1,000 to 15,000 new COVID-19 hospitalizations per day.
- State-level forecasts also show a high degree of variability across forecasts.
- Variation results from multiple factors. Hospitalization forecasts use different data sets of COVID-19 cases or deaths, with different limitations, and make different assumptions about social distancing.
National Forecasts
State Forecasts
- The five national forecasts show the predicted number of new COVID-19 hospitalizations per day for the next four weeks in the United States.
- The forecasts make different assumptions about hospitalization rates and levels of social distancing and other interventions and use different methods to estimate the number of new hospitalizations. See models below for details.
Seven state-level models predicting the number of new hospitalizations were submitted this week. These forecasts show the predicted number of new COVID-19 hospitalizations per day for the next four weeks in each state. Each state forecast uses a different scale, due to differences in the number of new COVID-19 cases occurring per day in each state.
Download state forecasts pdf [PDF – 937 KB]1
Download forecast data excel [XLS – 3 MB]
Forecast Assumptions
These forecasts make different assumptions about social distancing measures and use different methods and data sets to estimate the number of new hospitalizations. Individual models are described in more detail below.
Social distancing is incorporated into the forecasts in two different ways:The rate of new hospitalizations is estimated using three approaches:
- The national and state-level forecasts from Columbia University, the COVID-19 Simulator Consortium, the Institute for Health Metrics and Evaluation (IHME), and the Johns Hopkins University Infectious Disease Dynamics Lab (JHU) make assumptions about how levels of social distancing will change in the future.
- The national and state-level forecasts from the Georgia Institute of Technology, College of Computing, and the state-level forecasts from COVID Act Now and the Los Alamos National Laboratory (LANL) assume that existing social distancing measures in each state will continue through the projected four-week time period.
- The forecasts from Columbia University, COVID Act Now, the COVID-19 Simulator Consortium, LANL, and JHU assume that a certain fraction of infected people will be hospitalized.
- The IHME forecast estimates hospitalizations based on numbers of forecasted deaths.
- The forecast from the Georgia Institute of Technology, College of Computing, uses COVID-19 hospitalization data reported by some states to forecast future hospitalizations.
