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.

- Models make various assumptions about levels of social distancing and other interventions. See model descriptions below for details.
- The ensemble model is the median of all other models. It typically performs better than individual models.
- We keep track of model performance week-to-week by computing the absolute normalized error of each prediction for deaths against the true number of deaths: (pred-actual)/actual. Displayed in the table below is the median prediction error for different forecast horizons (e.g. 1 week ahead). Prediction error is only displayed if there are at least 3 historic predictions made by that model team for the target horizion, otherwise the table value is shown as NaN.

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