Modeling COVID-19 to Inform State and Local Response





Current R-effective in California

The effective reproduction number (called "R-effective" or "R-eff") is the average number of people each infected person will pass the virus onto and represents the rate at which COVID-19 is spreading.


Latest Estimate of R-effective is:

Low/High Estimates of R-effective:

Download R-eff Values

Statewide Estimates of R-effective

The effective reproductive number (R-eff) is the average number of secondary infected persons resulting from a infected person. If R-eff > 1, the number of infected persons will increase. If R-eff < 1, the number of infected persons will decrease. At R-eff = 1, the number of infected persons remains constant.

Latest Estimate of R-effective is:

Download County R-eff Trend
NOTE: Some counties do not have sufficient case numbers in order for modelers to estimate R-effective.

R-effective Trends by County

Select a county to see how R-effective has changed over time

Download R-eff for Counties

Latest R-effective in California Counties

Using estimates and uncertainty intervals from Covid Act Now, the following graph presents the averaged R-effective over the last 7 days by county; (<1 decreasing spread, >1 increasing spread)

Covid Act Now data has been pulled for further review. Check back soon.

Download R-eff for Counties

Statewide Map of R-effective by County

The following map presents the ensemble R-effective, averaged over the last 3 days by county; (<1 decreasing spread, >1 increasing spread)

Predefined County Regions

Predefined regions include commonly used 'clusters' of counties that either share demographic, economic, planning, or organizational characteristics or purposes in California.
Note: Regional ensembles are population weighted means of the R-effective for counties identified in the region.
Latest Estimate of R-effective is:

County Adjacency

The county adjacency panel allows users to examine 'clusters' of counties by selecting a focal county, and examining all counties that are adjacent, or geographically nearest to the focal county.
Note: Regional ensembles are population weighted means of the R-effective for counties identified in the region.
Latest Estimate of R-effective is:

Custom Regions

The custom regions panel allows users to customize regions by selecting multiple counties.
Note: Regional ensembles are population weighted means of the R-effective for counties identified in the region.
Latest Estimate of R-effective is:

Short-term COVID-19 Forecasts in California

Short-term forecasts take into account the most recent trends in cases, hospitalizations, ICU patients, and deaths and apply statistical models to that data to generate anticipated trends in the coming 2-4 weeks. With the volume and pace of COVID-19 data generation, we cannot always guarantee models or ensemble estimates will not contain unexpected results.


Current Daily Hospitalizations:

Projected Daily Total:

Download Hospital Forecasts

Statewide Hospitalization Forecasts

The black box (left) represents the current number of hospitalized COVID patients in California. The blue box represents the forecasted number of hospitalized patients at the 30 day mark based on models for California.

Current Daily ICU Patients:

Projected Daily Total:

Download ICU Forecasts

Statewide ICU Forecasts

The black box (left) represents the current number of COVID ICU patients in California. The blue box represents the forecasted number of COVID ICU patients at the 30 day mark based on models for California.

Current Total Deaths:

Projected Total:

Download Total Death Forecasts

Statewide Total Death Forecast

The black box (left) represents the current number of total COVID deaths in California. The blue box represents the forecasted number of total deaths at the 30 day mark based on the COVIDhub (ReichLab) ensemble for California.

California County Hospitalization Forecasts

Select a county to see how modeled number of hospitalizations compare with actual numbers to date and with the number of licensed hospital beds (black box).

Current Daily Hospitalizations:

Projected Daily Total:

Download County Hospital Forecasts

California County ICU Forecasts

Select a county to see how modeled number of COVID ICU patients compare with actual numbers to date (black box).

Current Daily ICU Patients:

Projected Daily Total:

Download County ICU Forecasts

California County Death Forecasts

Select a county to see how modeled number of cumulative deaths compare with actual numbers to date (black box).

Current Total Deaths:

Projected Total:

Download Total Death Forecasts

Predefined County Regions

Predefined regions include commonly used 'clusters' of counties that either share demographic, economic, planning, or organizational characteristics or purposes in California.
Note: Regional ensembles are sums of the point forecasts for counties identified in the region.

Hospitalizations | ICU | Total Deaths



County Adjacency

The county adjacency panel allows users to examine 'clusters' of counties by selecting a focal county, and examining all counties that are adjacent, or geographically nearest to the focal county.
Note: Regional ensembles are sums of the point forecasts for counties identified in the region.

Hospitalizations | ICU | Total Deaths


Custom Regions

The custom regions panel allows users to customize regions by selecting multiple counties.
Note: Regional ensembles are sums of the point forecasts for counties identified in the region.

Hospitalizations | ICU | Total Deaths



Long term Scenarios

Long-term scenarios estimate the effect of various non-pharmaceutical interventions (NPIs)


Note: Detailed model scenario descriptions can be found below the graph or on the Technical Notes tab.


Scenario Hub Model Options:
Legend

Download Scenarios in Graph
Use slide below graph to adjust date range.
Note: Scenario Hub modelers do not produce scenario models for all counties.

Scenario Hub Model Descriptions - See Technical Notes tab for more information



CalCAT Download Center

The Download Center provides an interface for user's to download canned, custom, or historical data from CalCAT. Historical data is limited to data ingested and published from June 15th, 2020 on forward. Please note that ensemble estimates for historical data are back-calculated using current methods.

Canned downloads are files that underlie all visualizations on the nowcast and forecast tabs.

Custom downloads are are user defined and include the ability to request historical data.

Canned Datasets

Download Selected Dataset

Custom Datasets

Download Custom Dataset

Data Preview


Nowcasts

Covid Act Now

Covid Act Now

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
Code repository
Modeling methods

Epi Forecasts

epiforecasts.io

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
Code repository
Modeling methods

covidestim

covidestim.org

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
Modeling methods

LEMMA

localepi.github.io

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 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 data
Code repository

UCSF

ca-covid-r.info

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
Modeling methods

Harvard Lin Lab

http://metrics.covid19-analysis.org/

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
Modeling methods

CDPH Wallinga Teunis

The Wallinga and Teunis method is based on the probabilistic reconstruction of disease transmission chains to estimate the number of secondary cases per infected individual. It is robust and only requires only case incidence data and the distribution of the serial interval (the time between the onset of symptoms in a primary case and the onset of symptoms of secondary cases) to estimate the % of cases from each day which were infected by cases on previous days. This data used for the estimate is derived from the CDPH line list of cases indexed by the date of symptom onset. The serial interval is given by a Weibull distribution with mean interval of 5.5 days. The methodology is implemented in the R programming language by the R0 package.

Modeling methods

Ensemble Nowcast

The ensemble nowcast takes the median of all the nowcasts available on a given date and smooths it with a three-day moving average. The methodology aims to be robust to outliers and to avoid kinks in the median nowcast when an input source for the ensemble estimate is unavailable on a certain date.

Forecasts

Covid Act Now

covidactnow.org

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
Code repository
Modeling methods

Institute for Health Metrics and Evaluation

covid19.healthdata.org

IHME 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 data
Modeling methods

Northeastern MOBS GLEAM Project

covid19.gleamproject.org

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
Modeling methods

MIT Operations Research

covidanalytics.io

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
Code repository
Modeling methods

COVID-19 Forecast Hub

covid19forecasthub.org

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
Code repository

Johns Hopkins University Infectious Disease Dynamics Group

www.iddynamics.jhsph.edu

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
Scenario Modeling Methods
Inference Methods

LEMMA

localepi.github.io

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
Code repository

Stanford Lightweight

www.sc-cosmo.org

SC-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.


Columbia

Columbia University, Shaman Group

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
Modeling methods

UCSF COVID Near Term

UCSF, COVID Near Term

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

ARIMA-based Model

R forecast Package

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

Simple Growth Model

The simple growth forecast assumes new cases grow exponentially according to the rate given by the latest ensemble R-effective. A constant fraction (9%) of new cases are hospitalized a fixed time after contracting covid (7 days), and hospitalized patients are discharged after a fixed length of stay (8.5 days).

Google

Google COVID-19 Public Forecasts

Google 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 data
Raw county data
Modeling methods

Ensemble Forecast

The ensemble forecast takes the median of all the forecasts available on a given date and fits a smoothed spline to the trend. The methodology aims to be robust to outliers, and to avoid artifacts (i.e. abrupt kinks) when the median forecast switches from one source to another.

Scenarios

CDC Scenario Hub

https://covid19scenariomodelinghub.org/

Scenario Hub Viz

Childhood vaccination assumes that among 5-11 years old is approved and immunization begins on Nov 1. Each state's uptake rate reflects the percent coverage increases observed for 12-17 years olds since distribution began an May 13. No childhood vaccination assumes no vaccination for children under 12 years old.

New variant assumes that a more transmissible variant emerges, comprising 1% of circulating viruses on Nov 15. The new variant is 1.5X as transmissible as viruses circulating at the beginning of the projection period. No variant assumes the same mix of variants circulate throughout the projection period. No change in virus transmissibility

Scenario hub Contributors


LEMMA

localepi.github.io

LEMMA provides scenarios to depict how waning immunity might affect disease trajectories in the upcoming winter, as well as the need for boosters.

Boosters Outweigh Waning Immunity assumes vaccine efficacy against infection increases 20% while vaccine efficacy against severe disease remains stable. Boosters Balance Waning Immunity assumes vaccine efficacy against infection & severe disease both remain stable. Waning Immunity Outweighs Boosters assumes vaccine efficacy against infection decreases 30% while vaccine efficacy against severe disease decreases 3%.

Low Contact Rate (Low Spread) assumes a 10% increase in effective contact rate over 60 day period starting Nov. 1. Mid Contact Rate (Mid Spread) assumes a 25% increase in effective contact rate over 60 day period starting Nov. 1. High Contact Rate (High Spread) assumes a 50% increase in effective contact rate over 60 day period starting Nov. 1. Very High Contact Rate (Very High Spread) assumes a 100% increase in effective contact rate over 60 day period starting Nov. 1.

All LEMMA scenarios assume the following:

  • Ages 5-11 years old will be vaccinated starting Nov 1, with an uptake of 90% in San Francisco (other counties scaled up or down based on adult update)
  • ages 0-4 years old will be vaccinated starting Jan 1, with a 50% uptake in San Francisco (other counties scaled up or down based on adult update)
  • Delta is 60% more transmissible than Alpha, and twice as likely to cause hospitalizations
  • Vaccine efficacy against Delta during the summer surge is assumed as 62% (infection, combined 2 dose mRNA) and 95% (severe disease, combined 2 dose mRNA).

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