Last updated: 2021-04-12

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Knit directory: uci_covid_modeling2/

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This website provides reports on the dynamics and future trends of spread of SARS-CoV-2 in Orange County, CA. Please visit the github for details on the code, and read the manuscript associated with this endeavor for further methodological details.



Damon Bayer, Isaac Goldstein, and Vladimir N. Minin
Department of Statistics, University of California, Irvine

Jon Fintzi, Keith Lumbard, and Emily Ricotta
National Institute of Allergy and Infectious Diseases

Richard Chhuon, Alissa Dratch, Matthew Zahn
Orange County Health Care Agency


Vladimir Minin



We use data provided by OCHCA. An aggregated version of our data is available in the github. Crucially, we exclude repeat tests given to patients who test positive (which happens when patients are hospitalized). Our data may not correspond with publicly available data.

We also do not analyze data in real time. This is because case, test, and death counts are often updated retroactively, and we wish to give data collectors time to provide complete results. Typically, there will be at least a ten day gap between the present day and the final date analyzed in the most recent report.

Statistical methodology

Our analysis relies on a six compartment mechanistic model of the pandemic. We then use Bayesian inference to provide inference on key disease dynamics and make predictions on future observed cases and deaths. Further descriptions of the methodology are available in the manuscript.


We used the R software environment, tidyverse and workflowr packages for this website. Our analysis was conducted in stemr.