Forecasts of COVID-19 Deaths
As of July 31, 2020, the Forecasts of COVID-19 Deaths webpage includes forecasts of new deaths in addition to forecasts of total deaths.
Observed and forecasted new and total reported COVID-19 deaths as of July 27, 2020.
Interpretation of Forecasts of New and Total Deaths
- This week CDC received forecasts of national COVID-19 deaths over the next 4 weeks from 32 modeling groups. Those forecasts predict:
- The number of new COVID-19 deaths reported each week (31 forecasts), which indicates how reported deaths are likely to increase or decrease in the coming weeks
- The total number of COVID-19 deaths reported by the end of each week (31 forecasts), which helps us understand the likely overall impact of the pandemic in the coming weeks
Of the 32 modeling groups, 30 provided forecasts of both new and total deaths, one provided a forecast of new deaths only, and one provided a forecast of total deaths only.
- This week’s national ensemble forecast predicts that weekly reports of new COVID-19 deaths may increase over the next month, with 5,000 to 11,000 new deaths reported during the week ending August 22. The ensemble forecast predicts that 168,000 to 182,000 total COVID-19 deaths will be reported by August 22.
- State-level ensemble forecasts predict that the number of reported new deaths per week may increase over the next four weeks in: Alabama, Kentucky, New Jersey, Puerto Rico, Tennessee, and Washington.
National Forecast
- The top row of the figure shows the number of new COVID-19 deaths reported in the United States each week from May 16 through July 25 and forecasted new deaths over the next four weeks, through August 22.
- The bottom row of the figure shows the number of total COVID-19 deaths in the United States each week from May 16 through July 25 and the forecasted number of total COVID-19 deaths over the next four weeks, through August 22.
- Models make various assumptions about the levels of social distancing and other interventions, which may not reflect recent changes in behavior. See model descriptions below for details.
State Forecasts
State-level forecasts figures show observed and forecasted state-level new and cumulative COVID-19 deaths in the US. Each state forecast uses a different scale, due to differences in the numbers of COVID-19 deaths occurring in each state.
Forecasts fall into one of two categories:
- The CMU, DDS, Columbia-UNC, ERDC, ESG, Geneva, GT-DeepCOVID, ISU, Karlen, LANL, LNQ, LSHTM, MIT-CovAlliance, MIT-ORC, MOBS, Oliver Wyman, NotreDame-Mobility, QJHong, STH, UA, UCM, UM, UMass-MB, USC, and UT forecasts assume that existing control measures will remain in place during the prediction period.
- The Columbia, COVID19Sim, GT-CHHS, IHME, JCB, JHU, NotreDame-FRED, PSI, UCLA, and YYG forecasts make different assumptions about how levels of social distancing will change in the future.
Download state forecasts [29 pages]1
Download forecast data [1 sheet]
Additional forecast data and information on forecast submission are available at the COVID-19 Forecasting Hub.
Modeling Groups
Forecasts were provided by the following modeling groups. Each modeling group submitted forecasts for both new and total deaths, unless otherwise indicated.
Note: This group submitted state-level forecasts of new deaths only.
Model name: CMU
Intervention assumptions: These projections do not make specific assumptions about which interventions have been implemented or will remain in place.
Methods: Autoregressive time-series model.
Intervention assumptions: This model assumes that contact rates will increase 5% per week over the next two weeks. The reproductive number is then set to 1 for the remainder of the projection period.
Methods: Metapopulation SEIR model.
Intervention assumptions: This model assumes that transmission intensity will peak in early July and then gradually decline.
Methods: Statistical survival-convolutional model.
Model name: Covid19Sim
Intervention assumptions: This model is based on assumptions about how levels of social distancing will change in the future.
Methods: SEIR model.
Model name: DDS
Intervention assumptions: This model assumes that the effects of interventions are reflected in the observed data and will continue going forward.
Methods: Bayesian hierarchical model.
Intervention assumptions: This model assumes that once stay-at-home orders are lifted, contact rates will gradually increase. It also assumes that some households containing symptomatic cases will self-quarantine.
Methods: Agent-based model.
Model name: GT-DeepCOVID (formerly GA_Tech)
Intervention Assumptions: This model assumes that the effects of interventions are reflected in the observed data and will continue going forward.
Methods: Deep learning.
Model name: IHME
Intervention assumptions: Projections are adjusted to reflect differences in aggregate population mobility and community mitigation policies.
Methods: Combination of a mechanistic disease transmission model and a curve-fitting approach.
Model name: ISU
Intervention Assumptions: These projections do not make specific assumptions about which interventions have been implemented or will remain in place.
Methods: Nonparametric spatiotemporal model.
Model name: JCB
Intervention assumptions: The incidence of COVID-19 in the population determines the strength and resulting impact of control measures in the future.
Methods: Phenomenological statistical model.
Model name: JHU
Intervention Assumptions: This model assumes that the effectiveness of interventions is reduced after shelter-in-place orders are lifted.
Methods: Stochastic metapopulation SEIR model.
Model name: Karlen
Intervention assumptions: This model assumes that the effects of interventions are reflected in the observed data and will continue going forward.
Methods: Discrete-time difference equations.
Note: This group submitted forecasts for new deaths only.
Model name: LNQ
Intervention assumptions: This model assumes that the effects of interventions are reflected in the observed data and will continue going forward.
Methods: Machine learning.
Model name: LSHTM
Intervention assumptions: These projections assume that current interventions will not change during the forecasted period.
Methods: This forecast is an ensemble of three different models: A time-varying reproductive number-based model, a time series model based on numbers of deaths, and a time series model based on numbers of cases and deaths.
Intervention assumptions: This model assumes that currently implemented interventions and corresponding reductions in transmission will continue, resulting in an overall decrease in the growth rate of COVID-19 deaths.
Methods: Statistical dynamical growth model accounting for population susceptibility.
Intervention Assumptions: The projections assume that current interventions will remain in place indefinitely.
Methods: SIR model.
Intervention assumptions: The projections assume that social distancing policies in place at the date of calibration are extended for the future weeks.
Methods: Metapopulation, age-structured SLIR model.
Model names:
- NotreDame-Mobility
- NotreDame-FRED (state-level forecasts only)
Intervention assumptions: These forecasts assume that population-level mobility is a reliable proxy for adherence to social distancing, and that recent trends in mobility will continue over the coming weeks.
Methods:
- NotreDame-Mobility: SEIR model fit to data on deaths, test positivity, and population mobility.
- NotreDame-FRED: Agent-based model.
Model name: Oliver Wyman
Intervention assumptions: These projections assume that current interventions, will remain unchanged during the forecasted period.
Methods: Time-dependent SIR model for detected and undetected cases.
Model name: PSI
Intervention assumptions: These projections assume that current interventions will not change during the forecasted period.
Methods: Stochastic SEIRX model.
Model name: QJHong
Intervention assumptions: These projections assume that current interventions will not change during the forecasted period.
Methods: Machine learning using case data and mobility data.
Model name: ESG
Intervention assumptions: These projections assume that current interventions will not change during the forecasted period.
Methods: Fitting reported data to multiple skewed gaussian distributions.
Model name: STH
Intervention assumptions: These projections assume that current interventions will not change during the forecasted period.
Methods: Statistical growth model.
Model name: ERDC
Intervention assumptions: These projections assume that current interventions will not change during the forecasted period.
Methods: SEIR model.
Model name: UA
Intervention assumptions: This model assumes that current interventions will remain in effect for at least four weeks after the forecasts are made.
Methods: SIR mechanistic model with data assimilation.
Intervention assumptions: This model assumes that contact rates will increase as states reopen. The increase in contact rates is calculated for each state.
Methods: Modified SEIR model.
Model name: UCM
Intervention assumptions: These projections assume that current interventions will not change during the forecasted period.
Methods: SEIR model.
Model name: Geneva
Intervention assumptions: The projections assume that social distancing policies in place at the date of calibration are extended for the future weeks.
Methods: Exponential and linear statistical models fit to the recent growth rate of cumulative deaths.
Model names: UMass-MB, Ensemble
Intervention assumptions:
- UMass-MB: These projections do not make specific assumptions about which interventions have been implemented or will remain in place.
- Ensemble: The national and state-level ensemble forecasts include models that assume certain social distancing measures will continue and models that assume those measures will not continue.
Methods:
- UMass-MB: Bayesian SEIRD model.
- Ensemble: Equal-weighted combination of 2 to 11 models, depending on the availability of national and state-level forecasts. To ensure consistency, the ensemble includes only models with 4 week-ahead forecasts and models that do not assign a significant probability to there being fewer cumulative deaths than have already been reported by the day of submission. Only one model was available for the forecasts for Guam and the Northern Mariana Islands.
Model name: UM
Intervention assumptions: These projections assume that current interventions will remain unchanged during the forecasted period.
Methods: Ridge regression
Intervention assumptions: These projections assume that current interventions will remain unchanged during the forecasted period.
Methods: SIR Model.
Model name: UT
Intervention assumptions: This model estimates the extent of social distancing using geolocation data from mobile phones and assumes that the extent of social distancing will not change during the period of forecasting. The model is designed to predict confirmed COVID-19 deaths resulting from only a single wave of transmission.
Methods: Nonlinear Bayesian hierarchical regression with a negative-binomial model for daily variation in death rates.
Model name: YYG
Intervention assumptions: The model accounts for individual state-by-state re-openings and their impact on infections and deaths.
Methods: SEIS mechanistic model.
Footnote
1 The full range of the prediction intervals is not visible for all state plots. Please see the forecast data for the full range of state-specific prediction intervals.
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