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 data
Epi 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 data
Covidestim 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 repository
The 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 data
To 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 data
The 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 data
IHME uses a hybrid modeling approach to generate its forecasts, which incorporates elements of statistical and disease transmission models.Raw data
The 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 data
MIT 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 data
The 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 data
The 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 Repository
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 cases, 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
This model by Vladimir Minin and Damon Bayer at UC Irvine is a SEIR type model stratified by Omicron vs non-Omicron infection with hospitalization compartments. The model is calibrated to California hospital data from the California open data portal using data on COVID variant prevalence from GISAIDRaw data
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 data
CovidNearTerm 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 data
Model 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 methods
Optimistic waning assumes slow immune waning with a median transition time to a partially immune state of 10 months, and a 40% reduction in protection from baseline levels.
Pessimistic waning asssumes fast immune waning with a median transition time to a partially immune state of 4 months, and a 80% reduction in protection from baseline levels.
No new variant assumes there is the same mix of strains circulating as at the beginning of the projection period.
New variant assumes on May 1, 2022 there is a new variant with a constant influx of 50 weekly infections which has 30% immune escape and the same transmissibility.
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 cases, 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.
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.