Modeling Communicable Diseases to Inform State and Local Response






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.

R-effective severity legend:

Less than 0.7

Between 0.7 and 0.9

Between 0.9 and 1.1

Between 1.1 and 1.3

Greater than 1.3

Unavailable



Latest Estimate of R-effective is:



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. See COVID Technical Notes tab for more information.

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. See COVID Technical Notes tab for more information.

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). See COVID Technical Notes tab for more information.

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). See COVID Technical Notes tab for more information.

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. See COVID Technical Notes tab for more information.
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. See COVID Technical Notes tab for more information.
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. See COVID Technical Notes tab for more information.
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 Cases:

Projected Daily Total:

Plot Options:

Show Total Tests
Download Cases Forecasts

Statewide Cases Forecasts

The black box (left) represents the current number of COVID cases in California. The blue box represents the forecasted number of cases at the 30 day mark based on models for California. Please note that cases 'Actuals' are updated weekly.

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 Case Forecasts

Select a county to see how modeled number of cases compare with actual numbers to date.

Current Daily Cases:

Projected Daily Total:

Plot Options:

Show Total Tests
Download County Cases Forecasts
Please note that cases 'Actuals' are updated weekly.

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

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.

Plot Options:

Show Total Tests
Please note that cases 'Actuals' are updated weekly.


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.

Cases | Hospitalizations | ICU | Total Deaths

Plot Options:

Show Total Tests
Please note that cases 'Actuals' are updated weekly.


County Adjacency Source File

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.

Cases | Hospitalizations | ICU | Total Deaths

Plot Options:

Show Total Tests
Please note that cases 'Actuals' are updated weekly.


Long term Scenarios

Long-term forecasts predicated on various specific assumptions

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

Plot Options:
Show 50% Uncertainty Inverval

Download Scenarios in Graph

Legend

Use slide below graph to adjust date range.

Scenario Model Descriptions - See Technical Notes tab for more information



Variant Proportions

Variant proportions are derived from both the Integrated Genomic Epidemiology Dataset (IGED)* and Terra**. Variant proportions are estimated weekly for the past 3 months with weeks defined each Wednesday to align with data updates that occur each Tuesday. Because sequencing SARS-CoV-2 samples takes time, observed variant proportions are lagged from the present date, generally by 3-4 weeks, and models are used to project current variant proportions. Projections may fluctuate week to week as new data is generated, lineages are re-designated, data backlogs and errors are resolved, and new variants are identified.

Note - Because of recent updates of Pangolin software leading to reclassification of several SARS-CoV-2 sequences, BQ.* lineage estimates are currently not generated from IGED.

*Integrated Genomic Epidemiology Dataset (IGED) includes a comprehensive record of California SARS-CoV-2 lineages derived using whole genome sequencing along with case demographic and epidemiologic information reported to the state per updates to Title 17 of the California Code of Regulations, section 2505, subsection (q). The dataset is maintained by CDPH and is also used to update the Variants - Coronavirus COVID-19 Dashboard weekly on SARS-CoV-2 trends occurring in California.
**Terra is a cloud-native platform for biomedical researchers to access data, run analysis tools, and collaborate. CDPH-associated labs and public health labs throughout California use Terra to analyze and store SARS-CoV-2 sequences.

Variant with highest proportion:

Variant with fastest growing proportion:

Download Variant Proportions

Statewide Variant Proportions

The connected points in the plot below show actual variant proportions derived from the IGED, while lines in the shaded region/to the right of the vertical line show model-derived projections of variant proportions for the last three weeks.
Particularly in regions with smaller populations, uncertainty in the actual proportions and projections should be heeded. Uncertainty can be viewed by hovering over the relevant data.
The black box (left) represents the variant with highest actual proportion. The blue box represents the projected fastest growing variant for California.

Variant with highest proportion:

Variant with fastest growing proportion:

Download Variant Proportions

Regional Variant Proportions

The connected points in the plot below show actual variant proportions derived from the IGED, while lines in the shaded region/to the right of the vertical line show model-derived projections of variant proportions for the last three weeks.
Particularly in regions with smaller populations, uncertainty in the actual proportions and projections should be heeded. Uncertainty can be viewed by hovering over the relevant data.
The black box (left) represents the variant with highest actual proportion. The blue box represents the projected fastest growing variant for the selected Local Health Officer Region in California.

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

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 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
Code repository


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

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

CDPH JRC

This is the European Commission's Joint Research Center's methodology for calculating R0 and R-effective, as implemented by the CDPH. The method is based on estimating the slope of the exponential growth curve of the time series for new cases. CDPH uses officially reported California case data indexed by episode date, and a serial interval of 3 days.

Modeling methods

CDPH Cislaghi

This method calculates R-effective as the number of newly diagnosed cases on day s over the number of newly diagnosed cases on day (s - T), where T is the incubation time. CDPH uses officially reported California case data indexed by episode date, and an incubation period (T) of 3 days. To smooth the curve and to avoid strong daily variations due to noise, R-effective was calculated as a seven-day moving average.

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 uses a hybrid modeling approach to generate its forecasts, which incorporates elements of statistical and disease transmission models.

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

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

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 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
Code repository


UC Irvine

vnminin.github.io

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 GISAID."

Raw data
Code repository

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).

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

COVID-19 Scenario Modeling Hub

Scenario Modeling Hub

Scenario Hub Viz
Round 15

Early boosters assumes that the reformulated boosters will be available from Sep 11, 2022 for all adults with booster coverage reduced by 10% as compared to the historical seasonal flu vaccination.

Late boosters assumes that the reformulated boosters will be available from Nov 13, 2022 for all adults with booster coverage reduced by 10% as compared to the historical seasonal flu vaccination.

No new variant assumes there is the same mix of strains circulating as at the beginning of the projection period.

New variant assumes on Sep 4th, 2022 there is a new variant, with a constant influx of 50 weekly infections for 16 weeks, which has 40% immune escape and 20% increased risk of hospitalization and death relative to Omicron.

Round 14

Risk-focused booster policy assumes a booster campaign starting on October 1, 2022 only for ages 55+ and individuals with chronic conditions with booster uptake reduced by 15% as compared to the first booster.

Expanded booster policy assumes a booster campaign starting on October 1, 2022 for ages 18+ with booster coverage reduced by 10% as compared to the 2021-2022 flu vaccine coverage.

No new variant assumes protection from natural immunity and VE decline only due to immunity waning.

New variant assumes on September 4, 2022 there is a new variant with a constant influx of 50 weekly infections, for 16 weeks, which has 40% immune escape and 20% increased risk of hospitalization and death as compared to Omicron.

Scenario Hub Contributors
Scenario Hub Ensemble LOP Untrimmed
The LOP untrimmed ensemble projection is calculated by averaging cumulative probabilities of a given value across submissions. All values are included in the average. From the resulting distribution, medians and uncertainty intervals are derived. Ensemble projection includes only those submissions that reported quantiles for their targets.


LEMMA

localepi.github.io

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.
Raw data


Institute for Health Metrics and Evaluation (IHME)

https://www.healthdata.org/covid/faqs#Scenarios

Best masks assumes mask usage reaches 80% within 7 days


Columbia

Columbia University, Shaman Group

The Shaman group from Columbia University 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).

No change assumes that current contact rates will remain unchanged in the future.

1x increased transmission (5_1xbeta_Lmin0p5) assumes a one-time 5% increase in contact rates at the start of the projection. The following week, the effective reproduction number R is set to 1. As the simulation progresses, R_eff decreases as the fraction of susceptible individuals in the population decreases.

Raw data
Modeling methods

Variants

CDPH Projection Models

Models based on previously observed variant proportions are used to generate real-time estimates of variant proportions. Each model is applied separately to both the integrated genomic epidemiology dataset (IGED) and Terra.

The logistic proportion model is based on methods presented in Althaus et al 2021. It uses the last 60 days of data to estimate a trend in variant growth assuming it is following a logistic pattern.

The multinomial spline model is based on methods presented in Davies et al 2021. It uses a multinomial logistic regression with cubic spline terms to estimate temporal trends in the frequency each variant is observed.

The variant simple growth model is currently only estimated statewide and uses variants specific effective reproduction numbers (Reff) and simple growth models to estimate variant specific case counts. Variant proportions are then estimated from these case counts as the proportion of projected new cases caused by each variant. The model to estimate variant specific reproduction numbers is based on methods presented in Figgins & Bedford 2021.

The ensemble mean model is the average of the available projection models listed above. The range displayed in the plot is the lowest to highest uncertainty estimates from the input models.

Note: Particularly in regions with smaller populations, uncertainty in the actual proportions and projections should be heeded. Uncertainty can be viewed by hovering over the relevant data in the plots.

Modeling Influenza to Inform State and Local Response





Short-term Influenza Forecasts in California

Short-term forecasts take into account the most recent trends in hospitalizations and apply statistical models to that data to generate anticipated trends in the coming 2-4 weeks. With the disruptions due to COVID-19 pandemic, we cannot always guarantee models or ensemble estimates will not contain unexpected results.


Current Weekly Hospital Admits:

CDPH Ensemble Projection:

Download Admits Forecasts

Statewide Hospital Admits Forecasts

The black box (left) represents the current number of hospital admits in California. The blue box represents the forecasted number of hospital admits by CDPH Ensemble at the 30 day mark for California.

California County Flu Hospital Admits Forecasts

Select a county to see how modeled number of hospital cases compare with actual numbers to date.

Current Weekly Hospital Admits:

CDPH Ensemble Projection:

Download County Admits 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.

Current Weekly Hospital Admits:

CDPH Ensemble Projection:

Download Regional Admits Forecasts

Note: Please refer to the map on the left to see the counties in the selected Region.



Actual flu hospitalization data is from California Hospital Association data on flu admissions.

Models trained on historical seasons prior to the 2020-2021 season were trained on HCAI flu admission data.

Each region is fitted separately using Public Health and Clinical Lab data (with the exception of RANCHO which also includes ILI data).

Only counties (> 250K population) with both Clinical Lab and Public Health Lab surveillance are included for forecasts.

Long term Scenarios

Long-term forecasts predicated on various specific assumptions.

Horizontal dashed lines correspond to peak hospital admissions for historically low (2015-2016) and high (2017-2018) flu seasons in California. These historical burdens were estimated from the incidence of influenza-associated hospitalizations in California Emerging Infections Program counties.


Plot Options:
Show 50% Uncertainty Inverval

Download Scenarios in Graph

Legend

Use slide below graph to adjust date range.

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



Forecasts

CDC FluSight

CDC FluSight GitHub
CMU-TimeSeries

A basic quantile autoregression fit using influenza-related hospitalizations and doctors visits, jointly trained across locations using the most recently available 21 days of data.

SGroup-RandomForest

Random forest ensemble of the predictors generated from the SGroup-SIkJalpha submission, HHS data, as well as historical FluSurv-NET data.

UMass-trends_ensemble

Equally weighted ensemble of simple time-series baseline models. Each baseline model calculates first differences of incidence in recent weeks. These differences are sampled and then added to the most recently observed incidence.

CDC Ensemble

The CDC Ensemble from the CDC FluSight Challenge includes all available flu forecasts for the state of California.

CDPH Models

These forecasts consist of four time series forecast approaches:

  • Autoregressive integrated moving average (ARIMA): uses a weighted linear sum of recent past observations or lags
  • Holt’s linear trend method: extended simple exponential smoothing to allow the forecasting of data with a trend (Holt 2004)
  • Damped Holt’s: an exponential smoothing model designed to damp erratic trends (Gardner & Mckenzie 1985)
  • Neural network autoregression (NNETAR): feed-forward neural networks with a single hidden layer and lagged inputs
At the state level, the hierarchical public health region and flu surveillance region models are sums of the ensemble estimates of each respective regional grouping.
CDPH Ensemble
The ensemble model is the median value of all available CDPH models. The lower and upper 95% prediction intervals for the ensemble are based on the minimum and maximum 95% prediction intervals, respectively, of all available models for a given geographic region.

Scenarios

Flu Scenario Modeling Hub
Scenario Hub Viz

Round 1

The optimistic prior immunity scenarios are meant to project the impact of a regular influenza season, with the same average immunity conditions as pre COVID-19.

The pessimistic prior immunity scenarios are meant to project the impact of a high-transmission influenza season, driven by the immunity gap left by two years of low influenza circulation. This immunity gap is assumed to be primarily driven by loss of immunity from natural infection. Flu vaccination has proceeded as usual each fall since 2020 in the US, but vaccine-derived immunity is short lasting.

In high vaccination protection scenarios, vaccination coverage is 10% higher than the 2020-2021 flu season for each age group (60% for adults), and vaccine effectiveness against medically attended influenza illnesses and hospitalizations is equal to 60% (comparable to the 2010-2011 flu season).

In low vaccination protection scenarios, vaccination coverage is 10% lower than the 2020-2021 flu season for each age group (40% for adults), and vaccine effectiveness against medically attended influenza illnesses and hospitalizations is equal to 30% (comparable to the 2018-2019 flu season).

Scenario Hub Contributors
California Department of Public Health – FluCAT
The model is a stochastic, mechanistic model implemented via the tau leap method for the state of California.
Northeastern University MOBS Lab — GLEAM Flu
GLEAM is an age-structured metapopulation model that includes high resolution demographic data, short-range commuting flows, domestic air traffic, and age-specific contact patterns.
University of Southern California (USC) — SIkJα
Discrete heterogeneous rate model where rates are learned using regression weighing the recently seen data higher. Past seasons' rates are used as a proxy for seasonality.
University of Texas at Austin - ImmunoSEIRS
A two virus model that explicitly tracks the immunity caused by natural infections and vaccination and its impact on the average chances of infection, hospitalization and death.
University of Virginia - EpiHiper and EpiHiperFlu
EpiHiper is an agent-based model that computes stochastic transmissions of a disease in a synthetic contact network between individuals and stochastic state transitions within each individual.
University of Virginia — FluXSim
Metapopulation simulation over age- and spatially-stratified synthetic contact network.
Scenario Hub Ensemble LOP Untrimmed
The LOP untrimmed ensemble projection is calculated by averaging cumulative probabilities of a given value across submissions. All values are included in the average. From the resulting distribution, medians and uncertainty intervals are derived. Ensemble projection includes only those submissions that reported quantiles for their targets.




Version 🦃🥧 | Released November 10, 2022

CONTACT: covmodeling@cdph.ca.gov
CDPH COVID-19 Page | ca.gov COVID-19 Page | CalCAT Open Source
Icons provided by cdc.gov:

COVID Virus | Influenza Virus

Icons provided by the nounproject.com: Magnify | Binoculars | Telescope


Version 🦃🥧 | Released November 10, 2022

CONTACT: covmodeling@cdph.ca.gov
CDPH COVID-19 Page | ca.gov COVID-19 Page | CalCAT Open Source
Icons provided by cdc.gov:

COVID Virus | Influenza Virus

Icons provided by the nounproject.com: Magnify | Binoculars | Telescope