Note: Detailed model scenario descriptions can be found below the graph or on the Technical Notes tab.
Covid 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 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 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 dataThe 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 uses a hybrid modeling approach to generate its forecasts, which incorporates elements of statistical and disease transmission models.
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 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 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 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-Lightweight is a new, short-term forecasting model. For each county, it starts with daily incident detected cases and uses General Additive Model smoothing to remove day-of-the-week reporting variation. It then estimates daily R-effective values based on the smoothed time-series of detected cases employing methods by described Wallinga and Teunis (2004). R-effective is then extrapolated for the desired forecasting period by considering its current level and how long it has been above 1.0 as well as considering the duration of time spent above 1.0 from previous outbreak periods. Forecasts of incident detected cases are then generated based on: 1) the smoothed incident detected case series; 2) the R-effective estimates/extrapolations; and 3) the Covid-19 serial interval. Finally, prevalent hospitalizations are generated by calibrating the time-varying probability of each incident case being hospitalized to county-level Covid-19 hospital census data, using our prior hospitalization module with length of stay distributions derived from data reported for California hospital systems (www.sc-cosmo.org). With the extrapolated incident case series and extrapolation of the probability of hospitalization for each case and length of stay distribution, prevalent Covid-19 hospitalization prevalence is then extrapolated.
The 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 dataCovidNearTerm is a bootstrap-based method based on an autoregressive model to estimate at the county level the expected number of COVID-19 patients that will hospitalized 2-4 weeks into the future. It is based on the work of researchers at UCSF (Adam Olshen), Stanford (Kristopher Kapphahn, Ariadna Garcia, Isabel Wang and Manisha Desai) and Memorial Sloan-Kettering (Mithat Gonen).
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 dataOptimistic severity assumes that among immunonaive individuals, there is a 50% reduction in severity of Omicron infection, relative to Delta. Among previously infected or vaccinated, the residual protection for Omicron cases against hospitalization is 85%.
Higher immune escape assumes that 80% of the previously immune are susceptible to infection and Omicron R0 is 1x the seasonally-adjusted R0 of Delta (Delta R0 = Omicron R0 = 6).
Higher transmissibility assumes that 50% of the previously immune are susceptible to infection and Omicron R0 is 1.66x the seasonally-adjusted R0 of Delta (Delta R0 = 6, Omicron R0 = 10).
LEMMA provides scenarios to depict how Omicron might affect hospitalizations in California in the coming weeks and months.
Optimistic assumes the Omicron hospitalization rate in immunonaive is much lower than for Delta and length of stay is shorter than for Delta.
Central assumes the Omicron hospitalization rate in immunonaive is lower than for Delta.
Pessimistic assumes the Omicron hospitalization rate in immunonaive is slightly lower than for Delta and booster vaccine effectiveness is lower,.
LEMMA scenarios either include 'for COVID' only (no incidental COVID) or 'with COVID' (includes incidental COVID). The current model assumption is that 40% of COVID positive patients are incidental admissions (not primarily due to COVID).
LEMMA 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 data
High severity assumes the hospitalization rate for Omicron is 2.3x as high, the infection fatality rate is 4.6x as high as the reference scenario
Best masks assumes mask usage reaches 80% within 7 days
Booster assumes that 100% of those who have received two doses of vaccine will get a third dose at 6 months.
Reduce hesitancy assumes those who respond on surveys that they probably will not receive a vaccine are persuaded or mandated to receive a vaccine.