Note: Detailed model scenario descriptions can be found below the graph or on the Technical Notes tab.
Rt.live provides a state-level estimate of R-effective, taking the number of cases and the input. It accounts for the delay from infection to onset of symptoms and changes in the amount of testing done.
Raw dataCovid Act Now provides statewide and county level estimates of R-effective, taking mortality and confirmed cases as inputs. Because of potential reporting delays and errors in the data, they perform smoothing, and require 10 preceding days of data. County-level estimates on the 'Nowcasts'' tab show average R-effective for the last 7 days.
Raw dataEpi Forecasts provides national and state-level estimates of R-effective, taking the number of cases as an input. It accounts for the delay from infection to onset of symptoms.
Raw dataCOVID-19 Projections fits a parameterized S-curve for R-effective to minimize the mean-squared error of historical daily mortality data. COVID-19 Projections provides state-level estimates for R-effective, mortality and the infected population.
Raw dataUCLA ML Lab provides state and California county projections of mortality and the number of confirmed cases. At the the state level, they publish current R-effective. UCLA ML uses a modified SEIR model with a compartment for undetected cases. The model parameters are selected to minimize the historical prediction error of the number of current cases and number of recovered patients.
Raw dataICL provides state-level infection and mortality projections. The reproduction number R_t is calibrated using mobility data. The model estimates the probability of model inputs which best match the distribution of historical mortality data, using Bayesian methods.
Raw dataLEMMA is an SEIR model with compartments for hospitalization and symptom severity. The model is calibrated to hospitalization data using Bayesian methods. The Rt curve is a spline with knots every 10 days. LEMMA provides projections and uncertainty bounds for cases and for hospital bed utilization.
Raw dataSC-COSMO is an age-structured, multi-compartment SEIR model calibrated to reported case numbers using a Bayesian approach. The model incorporates contacts patterns by age, the effect of population density, and estimates of the case detection rate. SC-COSMO explicitly considers contacts and transmission in households as well as contacts in work, school, and other settings and the effects of non-pharmaceutical interventions like shelter-in-place and school closures that differentially reduce contacts by venue. Model outputs include number of infections, deaths, and hospitalizations.
Modeling methodsThe CovidActNow model is a SEIR model with compartments for disease severity and medical intervention. Each county and state is calibrated separately, and R-effective is inferred using observed data.
Raw dataIHME is a multistage model, where the first stage fits an S-curve to historical daily deaths data, and the second stage is an SEIR compartment model. The SEIR model's R-effective is calibrated using the output of the first stage, but it also incorporates temperature data, population density, local testing capacity, and changes in mobility data. IHME provides projections of mortality, number of infections, and hospital utilization at the state and national level.
Raw dataThe Global Epidemic and Mobility Model (GLEAM) uses a individual-based, stochastic spatial epidemic model. The model uses mobility data and travel patterns to simulate spatial contact patterns. The likely ranges of basic parameters, such as R0 and IFR, are inferred from observed data.
Raw dataMIT DELPHI is a standard SEIR model with compartments for undetected cases and hospitalizations. R-effective is modeled as an S-curve to reflect government interventions and social distancing.
Raw dataA statistical machine learning extrapolation algorithm CLEP which forecasts deaths with MEPI prediction intervals with one week or two in advance by county. The forecast is calculated from an ensemble of linear and exponential predictors (CLEP), some of which pool data across counties or use demographic data.
Raw dataThe Reich Lab at the UMass-Amherst is an Influenza Forecasting Center of Excellence and the source for the official CDC COVID-19 Forecasting page. Taking other forecasts as the input, this is arithmetic average across eligible models of cumulative deaths forecasts. Forecasts are weekly out to 4 weeks, at the state and national level.
Raw dataThe COVID Scenario Pipeline is a county-level metapopulation model that incorporates commuting patterns and stochastic SEIR disease dynamics. To produce near-term forecasts of deaths and hospitalizations in the population, county-specific transmission and county-specific risks of hospitalization and death were inferred using a novel Bayesian inference algorithm is used to calibrate the model to weekly county-level incident cases and deaths, as reported by USAFacts. Fixed time delays from infection to case confirmation, hospitalization, and death were employed in projecting these outcomes. The estimates reported by this model incorporate uncertainty in baseline R0, the duration of the infectious period, the effectiveness of statewide intervention policies, and process stochasticity.
Code RepositoryLEMMA is an SEIR model with compartments for hospitalization and symptom severity. The model is calibrated to hospitalization data using Bayesian methods. The Rt curve is a spline with knots every 10 days. LEMMA provides projections and uncertainty bounds for cases and for hospital bed utilization.
Raw dataSC-COSMO is an age-structured, multi-compartment SEIR model calibrated to reported case numbers using a Bayesian approach. The model incorporates contacts patterns by age, the effect of population density, and estimates of the case detection rate. SC-COSMO explicitly considers contacts and transmission in households as well as contacts in work, school, and other settings and the effects of non-pharmaceutical interventions like shelter-in-place and school closures that differentially reduce contacts by venue. Model outputs include number of infections, deaths, and hospitalizations.
Modeling methodsThe COVID Scenario Pipeline is a county-level metapopulation model that incorporates commuting patterns and stochastic SEIR disease dynamics. To produce long-term planning scenarios, we calibrate the model to weekly county-level incident cases and deaths as reported by USAFacts using a novel Bayesian inference algorithm. We then project forward into the future making assumptions about the effectiveness of scenarios in different interventions, using fixed time delays from infection to case confirmation, hospitalization, and death and location-specific risks of hospitalization, ICU admission, and death. The estimates reported by this model incorporate uncertainty in baseline R0, the duration of the infectious period, the effectiveness of statewide intervention policies, and process stochasticity.
Code RepositoryThe CovidActNow model is a SEIR model with compartments for disease severity and medical intervention. Each county and state is calibrated separately, and R-effective is inferred using observed data.
Raw dataIHME is a multistage model, where the first stage fits an S-curve to historical daily deaths data, and the second stage is an SEIR compartment model. The SEIR model's R-effective is calibrated using the output of the first stage, but it also incorporates temperature data, population density, local testing capacity, and changes in mobility data. IHME provides projections of mortality, number of infections, and hospital utilization at the state and national level.
Raw dataRAND is an SEIR model with compartments for symptom severity and hospitalization, stratified by age and health status (to account for vulerable populations with chronic disease). R is estimated from a regression of the disease's growth rate. Policy interventions adjust the matrix contact rates, which account for age group and mode of interaction (such as home or school or work).
Modeling methods