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Plot Options:

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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

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

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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

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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

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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

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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

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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

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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

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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

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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.

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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

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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

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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

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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

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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

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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

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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

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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

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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).

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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.

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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

• Johns Hopkins University Infectious Disease Dynamics COVID-19 Working Group's COVID Scenario Pipeline
• Northeastern University MOBS Lab — GLEAM COVID
• University of Southern California (USC) — SIkJα
• University of Virginia — adaptive
• University of Virginia - EpiHiper
• University of North Carolina at Charlotte - HierBin
• Columbia University - Age Stratefied
• University of Texas at Austin - ImmunoSEIRS

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.

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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

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Institute for Health Metrics and Evaluation (IHME)

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

Best masks assumes mask usage reaches 80% within 7 days

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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 (R_eff) 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.

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Plot Options:

Download Scenarios in Graph

Use slide below graph to adjust date range.

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.