Forecasts updated October 18th, 2020. Click the legend on the right to remove or display each forecast. Use the slider bars to see past forecasts and their performace.
Forecasts based on statistical or mathematical models aim to predict changes in national level cumulative reported COVID-19 deaths and cases for the next six weeks. Forecasting teams predict numbers of deaths and cases 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).
These forecasts have been developed independently and shared publicly. It is important to bring these forecasts together to help understand how they compare with each other and how much uncertainty there is about what may happen in the upcoming six weeks.
Model name: Imperial
Intervention Assumptions: These projections do not make specific assumptions about which interventions have been implemented or will remain in place.
Methods: Ensembles of mechanistic transmission models, fit to different parameter assumptions.
Output: 1 week ahead deaths, infections, Rt
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
Output: Deaths, infections, testing, hospital resource use, social distancing
Model name: LANL
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. Over the course of the forecast, the model assumes that the rate of growth will decrease over time.
Methods
Statistical dynamical growth model accounting for population susceptibility
Output: Deaths, infections
Model name: MIT
Intervention Assumptions: The projections assume that current interventions will remain in place indefinitely.
Methods: SEIR model fit to reported death and case counts.
Output: Deaths, infections, active infections, hospitalizations, policy changes
Model name: USC
Intervention Assumptions: These projections assume that current interventions will remain unchanged during the forecasted period.
Methods: SIR Model.
Output: Deaths, infections, Rt
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.
Output: Deaths, infections, Rt
Model name: YYG
Intervention assumptions: The model accounts for reopenings and their impact on infections and deaths.
Methods
SEIS mechanistic model.
Output: Deaths, infections, Rt