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Less than a month ago, when the CDC began releasing forecast models of COVID-19 deaths, the number of official deaths was just over 22,000 and there were hopes expressed by the government that strict social distancing might limit that number to around 60,000 by mid-May (see graphic above).
Overnight, the official COVID-19 death toll surpassed 80,000 in the United States, and the latest ensemble projection predicts we'll see in excess of 100,000 deaths by the end of May.Welcome to the world of statistical modeling, where the end result changes every time fresh data is entered into the equation. This is by design, in order to make the model responsive to daily changes in the data.
And this is why we have multiple models, so over time we can figure out which models are the most reliable (and that can vary depending on what changes in the data).A week ago, in CDC: COVID-19 - Updated Forecasts (May 1st), we looked at revised forecasts - which added 5 new models to the data set- that predicted somewhere between around 90,000 deaths (ensemble average) by June 1st (see below).
On May 6th the CDC published another update (see below), this time increasing the number of models from 9 to 14. The IHME, which had been removed from the May 1st run, was reinstated along with the addition of 4 new models.
The latest ensemble forecast calls for just over 100,000 deaths by the end of May.
The wild card in all of this is how well people continue to practice social distancing and good hygiene measures as more and more states begin to reopen their economies. Essentially, these models suggest where we are going, but our actions can change the itinerary.
COVID-19 Forecasts
Updated May 6, 2020
Interpretation of Cumulative Death Forecasts
National-level forecasts now include fourteen individual forecasts, and all indicate an increase in deaths in the coming weeks. Predicted rates of increase differ among the forecasts, depending on assumptions about the strength and coverage of social distancing behaviors.
State-level ensemble forecasts (only shown for states and territories with at least two forecasts) indicate that some states may have limited additional deaths in the coming weeks, while substantial increases may occur in others.
State Forecasts
- These forecasts show cumulative reported COVID-19 deaths since February and forecasted deaths for the next four weeks in the United States.
- Models make various assumptions about the levels of social distancing and other interventions. See model descriptions below for details.
State-level forecasts show observed and forecasted state-level cumulative COVID-19 deaths in the US.
Forecasts fall into one of three categories
- The LANL and UMass-MB models do not explicitly model the effects of individual social distancing measures but assume that implemented interventions will continue, resulting in decreased growth.
- The Geneva, MIT, MOBS, UT, and YYG models are conditional on existing social distancing measures continuing through the projected time period.
- The CU, IHME, and YYG models make different assumptions about how levels of social distancing will change in the future.
Download state forecasts pdf icon[12 pages]
Download forecast data excel icon[XLS – 837 KB]
Why Forecasting COVID-19 Deaths in the US is Critical
CDC is responding to a pandemic of coronavirus disease 2019 (COVID-19) caused by a novel coronavirus, SARS-CoV-2, that is spreading from person to person. The federal government is working closely with state, tribal, local, and territorial health departments, and other public health partners, to respond to this situation. Forecasts of deaths will help inform public health decision-making by projecting the likely impact in coming weeks.
What the Forecasts Aim to Predict
Forecasts based on statistical or mathematical models aim to predict changes in national- and state-level cumulative reported COVID-19 deaths for the next four weeks. Forecasting teams predict numbers of deaths using different types of data (e.g., COVID-19 data, demographic data, mobility data), methods (see below), and estimates of the impacts of interventions (e.g. social distancing, use of face coverings).
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