Modeling COVID-19 to Inform State and Local Response





Current R-effective in California

The effective reproduction number (R-effective) 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) is the average number of secondary infected persons resulting from a infected person. If R>1, the number of infected persons will increase. If R<1, the number of infected persons will decrease. At R=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 COVIDhub (ReichLab) forecasted number of total deaths at the 30 day mark based on models 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 repositorty
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

Stanford

www.sc-cosmo.org

SC-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

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

UC Berkeley Yu Group in Statistics and EECS

covidseverity.com

A 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 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 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 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

www.sc-cosmo.org

SC-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

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

UCSB

UCSB Inter-Series Attention Model

We employ a purely data-driven model named ACTS to forecast COVID-19 related data, e.g. confirmed cases, hospitalizations and deaths, along time. We assume that the development of the pandemic in the current region will be highly similar to another region with similar patterns a few months ago. We use attention mechanism to compare and match such patterns and generate forecasts. We also leverage additional features such as demographic data and medical resources to more precisely measure the similarity between regions.

Raw data
Manuscript

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

Vaccination
All Scenarios assume 35% effectiveness after first dose, and 85% effectiveness after second doses of both Pfizer and Moderna vaccines. Johnson & Johnson are assumed to no longer be in use.

Low vaccination scenarios assume that coverage nationally reaches 70% by December 1, 2021.

High vaccination scenarios assume that coverage nationally reaches 80% by December 1, 2021.

Variants
All scenarios assume 70% prevalence of B.1.617+ (Delta) nationally by Dec 31, 2021

Low variant scenarios assume the B.1.617+ (Delta) variant is 20% more transmissible than the B.1.1.7. variant (first discovered in UK).

High variant scenarios assume the B.1.617+ (Delta) variant is 60% more transmissible than the B.1.1.7. variant (first discovered in UK).

Scenario hub Contributors


LEMMA

localepi.github.io

LEMMA provides optimmistic and pessimistic scenarios to depict how emerging variants of concern might change disease trajectories.

  • LEMMA's optimistic scenario assumes that the prevalent circulating variant (Delta) is 40% more transmissible than Alpha, that there is no increase in severity over Alpha, and there is no waning immunity.
  • The Pessimistic scenario assumes that the prevalent circulating variant (Delta) is 60% more transmissible than Alpha, that the hospitalization rate would double compared to Alpha, and that there is waning immunity over time following vaccination or infection.
  • LEMMA's forecasts (see 'Forecasts' Tab) approximate a Baseline scenario where the prevalent circulating variant (Delta) is 50% more transmissible than Alpha, that there is no increase in severity over Alpha, and there is no waning immunity.

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