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 seven (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 dataCovidestim calculates state-level effective reproductive numbers, taking cases, deaths and test positivity rates as inputs. It has corrections to account for lags in diagnosis, disease duration and mortality risk.
Code repositoryCOVID-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 dataThe UCLA ML Lab provides state and California county projections of mortality, the number of confirmed cases, and hospitalizations/ICU beds. At the state and county levels in CA, they also publish the current R-effective estimate. UCLA ML uses a modified SEIR model with a compartment for unreported cases. The model parameters learn to minimize the historical prediction error for the number of confirmed cases and deaths.
Raw dataICL provides state-level infection and mortality projections. The reproduction number R_t (which epidemiologists also call R_e, or R-effective) is calibrated using mobility data. Using Bayesian methods, the model calculates backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, and to predict the current rate of transmission.
Raw dataLEMMA is an open-source SEIR model with compartments for hospitalization and symptom severity. The model is calibrated to hospitalization, ICU, and death data using Bayesian methods. LEMMA provides credibility intervals and scenarios for future hospitalization, ICU, deaths, and R_t. Users can download the LEMMA package and input their own data and priors for parameters using R or a simple Excel interface.
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 UCSF model estimates time-varying reproduction numbers (R_t, or R-effective), the average number of cases infected by a given case over the course of that individual’s disease progression, for select Bay Area and California counties/regions. Model inputs are publically available daily counts of COVID-19 cases, archived by the New York Times. The UCSF researchers use the Wallinga-Teunis technique of real-time estimation of reproduction numbers.
Raw dataTo obtain the Rt estimate, the Harvard Xihong Lin Group uses the EpiEstim method (Cori, A., et al., 2013; Thompson, R.N., et al., 2019) to estimate the daily Rt value, as implemented in the EpiEstim R package. The EpiEstim method requires the following inputs: daily positive increase in cases (source used is JHU-CSSE), the time window of daily positive increase in cases to be averaged (7-day window is used), and the serial interval (used a mean of 5.2 days and a SD of 5.1 days).
Raw dataThe 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 multi-stage 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 four (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. This algorithm calibrates 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 open-source SEIR model with compartments for hospitalization and symptom severity. The model is calibrated to hospitalization, ICU and death data using Bayesian methods. LEMMA provides credibility intervals and scenarios for future hospitalization, ICU, deaths and Rt. Users can download the LEMMA package and input their own data and priors for parameters using R or a simple Excel interface.
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 Columbia model projects nationwide, county-level estimates of R-effective, daily new confirmed case, daily new infection (both reported and unreported), cumulative demand of hospital beds, ICU, and ventilators, as well as daily mortality (2.5, 25, 50, 75 and 97.5 percentiles). County-level case and death data are compiled from Johns Hopkins University and USAFACTS.
Raw dataUCSD-CovidReadi (UCSD COVID-19 Resource Allocation Decisionmaking Information Model) is an age-structured dynamic compartmental SARS-CoV-2 transmission and disease progression model. The model is calibrated to county-level data using an Approximate Bayesian Computation Sequential Monte-Carlo Scheme (ABC SMC) to daily counts of COVID-19 hospital census (confirmed+suspected), COVID-19 intensive care unit bed census (confirmed+suspected), and cumulative COVID-19 mortality provided by the California Department of Public Health. Key policy changes, like stay at home orders and business closures/reopenings, are incorporated mechanistically through allowing step changes in age-stratified contact rates on these event dates with wide uninformative priors.
Raw dataThe model is an aggregate of several component models. The best-performing components are neural networks, but the aggregate has a variety of other Artificial-Intelligence models in addition to epidemiological models. The aggregation method is designed to emphasize different components in areas where they are strongest. The emphasis changes for different pandemic conditions and in different locations.
Raw dataModel forecasts are the result of utilizing the forecast package's automatic ARIMA forecasting model. Note that the form of the model may vary between counties and over subsequent published forecasts.
Modeling methodsGoogle Cloud’s COVID-19 Public Forecasts provide data on the estimated spread of COVID-19 throughout the United States. The forecasts predict projected death toll, confirmed case counts, hospitalizations, and other important values for tracking and projecting the spread of COVID-19. The forecasts predict data for 28 days, on a state and county level. The model expands upon the SEIR model, which assigns each individual in the population to a “compartment” based on their disease state. The model uses machine learning to estimate how quickly individuals transition between compartments based on historical data and taking into account other relevant factors (“covariates”) that influence the transition rates.
Raw state dataThe COVID Scenario Pipeline is a county-level metapopulation model that incorporates commuting patterns and stochastic SEIR disease dynamics. To produce long-term planning scenarios, the model is calibrated to weekly county-level incident cases and deaths as reported by USAFacts using a novel Bayesian inference algorithm. The model projects into the future by 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 methodsSC-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 methods