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)
Download R-eff for Counties

Statewide Map of R-effective by County

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

Short-term COVID-19 Forecasts in California

Short-term forecasts take into account the most recent trends in cases, hospitalizations and deaths and apply statistical models to that data to generate anticipated trends in the coming 2-4 weeks.


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.

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

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 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

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.


Johns Hopkins Model Options:
IHME Model Options:
RAND Model Options:
Legend

Download Scenarios in Graph
Use slide below graph to adjust date range; will also adjust date range of static plot.
Note: Some modelers do not produce scenario models for all counties.

Selected Model Descriptions - See Technical Notes tab for more information




Nowcasts

Rt.live

rt.live

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.

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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 7 days.

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Epi Forecasts

Epi Forecasts

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.

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COVID-19 Projections (covid19-projections.com)

COVID-19 Projections (Youyang Gu)

COVID-10 Projections fits a parameterized S-curve (logistic function) 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.

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UCLA Machine Learning Lab

UCLA ML Lab

UCLA ML Lab provides state and California county projections of mortality and the number of confirmed cases. At the the state level, they publish current R-effective. UCLA ML uses a modified SEIR model with a compartment for undetected cases. The model parameters are selected to minimize the historical prediction error of the number of current cases and number of recovered patients.

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Imperial College London (ILC)

Imperical College London COVID-19 State-Level Tracking

ICL provides state-level infection and mortality projections. The reproduction number R_t is calibrated using mobility data. The model estimates the probability of model inputs which best match the distribution of historical mortality data, using Bayesian methods.

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Forecasts

Covid Act Now

Covid Act Now

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.

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Institute for Health Metrics and Evaluation

IHME

IHME 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.

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GLEAM

MOBS

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.

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MIT Delphi

MIT

MIT DELPHI is a standard SEIR model with compartments for undetected cases and hospitalizations. R-effective is modeled as an S-curve (arctan) to reflect government interventions and social distancing.

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COVID-19 Forecast Hub

Reich Lab

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 4 weeks, at the state and national level.

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Johns Hopkins University Infectious Disease Dynamics Group

http://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 is used to calibrate 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

Scenarios

Johns Hopkins University Infectious Disease Dynamics Group

http://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, we calibrate the model to weekly county-level incident cases and deaths as reported by USAFacts using a novel Bayesian inference algorithm. We then project forward into the future 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.

Projections assume:
  • Baseline unmitigated R0: 2-3
  • Mean incubation period: 5.2 days
  • Mean infectious period: 2.6-6 days
  • Infection fatality ratio: 1%

Code Repository
Scenario Modeling Methods
Inference Methods

Covid Act Now

Covid Act Now

The CovidActNow model is a SEIR model with compartments for disease severity and medical intervention. Parameters such as R0, infectious period and IFR are model inputs. Different scenarios parameterize future values of R-effective.

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Institute for Health Metrics and Evaluation

IHME

IHME 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 data