COVID–19 Incarceration

As COVID-19 spreads, state and local government leaders are on the front-lines of managing the response.

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Note: Our understanding of the virus is rapidly evolving; for the most up-to-date epidemiological and state data, pull the latest version of this model daily

This tool was created to help criminal justice decision makers understand how the incarcerated population is likely to interact with the public health system as the pandemic spreads. The model aims to answer questions like:

  • When will cases peak in my facilities?
  • How many hospitalizations, ICU commitments, and fatalities will result from that peak?
  • How does changing our total incarcerated population or the demographics of our incarcerated population impact the peak?
  • How does slowing the transmission rate impact the height and timing of the peak?

What's Changed

The model is updated daily to include new case numbers for each county and state. 

In addition, we update assumptions based on new information and add additional features and capabilities that might be helpful. We recommend re-downloading the model every 1-3 days to ensure you’re using up-to-date information.

Recent functionality changes:


  • We recommend all users update to this version or later.
  • Based on feedback, the population turnover ratio can now be entered for the facility being modeled to adjust the total impacted population so that it includes individuals who left and new individuals who entered the system. (Thanks to Bret in PA for submitting this feature.)
  • Based on feedback, the model now has improved handling for mid-outbreak projections. Older versions of the model assumed all cases started roughly on Day 0, which worked well for early-stage outbreaks but underestimated deaths from the virus in the near-term when several dozen cases were already in progress. This version of the model better distributes in-progress cases along the timeline.


  • Bugfix: The outputs tab in the 4/2 version included an error that left staff totals in the incarcerated population outputs. This is fixed in the 4/3 version.
  • Based on feedback, a facility’s percent over or under capacity (as an indication of density and ability to quarantine) has been added to the model.
  • Based on feedback, we’ve updated the ‘Facilities type’ to allow for modeling facilities which are partially bunked housing and partially cells.
  • Based on feedback, we’ve added a set of controls to add a planned future release of individuals into the model.


  • Bugfix: A calculation error magnified case numbers when the population was entered by age bracket. This is fixed in the 4/2 version. 
  • Separated outcomes for staff from outcomes for incarcerated individuals to help with staffing models.
  • Tied in-facility case estimate more closely to the facility population.
  • Added case and fatality estimates for ~500 new counties.


  • Fixed an issue with counting staff as part of the in-facility population. 
  • Added support for modeling rate of spread in bunks vs. cells.


  • Added support for county jails, and for full incarceration community (prisons + jails) in a state.


  • Initial state prisons model.

Note: The date on the 'Output' tab reflects the release date of that model. Even if the model was released several days ago, if you just downloaded it today the case and fatality numbers will be current within one day.


Complete the gray boxes on the ‘Inputs’ tab; this will tailor the model for your incarcerated population.

View the output on the ‘Output’ tab.

Join the Conversation

Questions? Feedback? Need help refining this model or fitting it to your system? Join the 'Criminal Justice C19' community on Slack. This is a private community of criminal justice agency leaders and research staff sharing knowledge on how they're approaching the challenges posed by COVID-19.

To join, send an email to with the subject line ‘Join the Conversation’

Get Assistance

Recidiviz and the Council of State Governments Justice Center are offering assistance to state and county leaders looking to refine and implement the model in their system. Set up a 15 minute initial consultation with the team here and we’ll get you started.

Coming Soon

As our understanding of the virus changes, we are upgrading this tool to answer more questions that are top of mind for criminal justice leaders. In the coming days, we’ll:

  • Add an input for a prison’s ability to quarantine people individually. For now, the model assumes that anyone infected in prison is isolated within 24hrs of exhibiting symptoms. If that may not be the case for your system, assume this model’s predictions are conservative and check back for an update in a few days
  • Separate ICU bed needs from total hospitalizations
  • Give you the ability to incorporate planned population reductions on future dates


What makes this model different from the models I see online or in the news?

This model is similar to models being used publicly, but factors in common criminal justice variables (population size, potential population releases, etc).

It also uses an R0 (basic reproductive number, which estimates the rate of an infection's spread) that is specific to prison and jail populations, where airborne illnesses spread at a significantly faster rate than is seen in the general population. See relevant academic work on the spread of similar illnesses in enclosed populations cited in the 'Variables' tab.

How can I model a single facility, instead of my entire incarcerated population?

If you've restricted movement between facilities by staff and the in-facility population, you may get more accurate numbers by modeling COVID-19 by facility instead of as an entire system collectively.

To do so, make copies of this spreadsheet and input the population data for one facility per copy of the model.

Can I get help expanding this model to better fit my jail or prison system?

Yes! You can set up a 15m consultation with the team that produced this model here.

We also suggest joining the 'Criminal Justice C19' community on Slack (see ‘Join the Conversation’ above). This is a community of criminal justice department leads and research staff sharing knowledge on how they're approaching the challenges posed by COVID-19.

Where did all these numbers come from?

You can see a complete list of sources in the ‘Variables’ tab in the model. 

Where can I learn more about how others in my field are preparing for COVID-19?

Join the COVID-19 Criminal Justice community on Slack (see ‘Join the Conversation’ above). This is a community of criminal justice department leads and research staff sharing knowledge on how they're approaching the challenges posed by COVID-19.

How can I submit questions, concerns, or suggestions about the model?

First, double-check that you have the most recent version of the model, in case your issue has already been addressed (you can download the latest copy above).

If your issue persists, send your question to or set up time with the Recidiviz team here. Recidiviz is a non-profit organization applying technology to issues in criminal justice, and maintains this model to help agencies anticipate and respond to the crisis.

For general questions or suggestions, we'd recommend bringing them up in the 'Criminal Justice C19' community on Slack, where other criminal justice agencies can contribute and build on your ideas (see ‘Join the Conversation’ above).

Can I make changes to this model, or re-share it with others?

You can modify or re-share this model so long as you keep the license notice in the FAQ tab of the model unchanged.

What other guidelines or recommendations are available to prepare our system for COVID-19?

How can I help other criminal justice systems?

The best way to help the rest of the community in real-time is to join the 'Criminal Justice C19' community on Slack (see ‘Join the Conversation’ above). This is a community of criminal justice agency leads and research staff sharing knowledge on how they're approaching the challenges posed by COVID-19.


We’d like to thank the following contributors for helping us build and continuously improve the model.

  • Justine Kunz (Senior Data Scientist, Recidiviz)
  • Kristofer (Bret) Bucklen, Ph.D. (Director, Bureau of Planning, Research & Statistics, Pennsylvania Department of Corrections)
  • Ribhav Gupta, Graduate Student, Department of Epidemiology, Stanford University School of Medicine
  • Ben Packer, Ph.D. (Software Engineer, AI and ML Fairness, Google)

You can see a full list of model collaborators on