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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

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Custom Datasets

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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.

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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.

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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.

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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).

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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

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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).

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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.

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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

https://covid19scenariomodelinghub.org/

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

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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.
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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.

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