COVID in prisons and other congregate settings
Overview
The explosive outbreaks of COVID-19 seen in congregate settings such as prisons and nursing homes, has highlighted a critical need for effective outbreak prevention and mitigation strategies for these settings. A variety of control interventions can be represented by a three stage model. Specifically, introduction of disease into the resident population from the community can be modeled as a stochastic point process coupled to a branching process, while spread between residents can be modeled via a deterministic compartmental model that accounts for depletion of susceptible individuals. Control can be modeled as a proportional decrease in the number of susceptible residents, the reproduction number, and/or the proportion of symptomatic infections. Preliminary estimates suggest that for a reproduction number of 3.0 with density-dependent transmission, reducing the size of the susceptible population by 20% can reduce overall disease burden by 47%
The goal of this project is to refine estimates about the potential impact of control interventions in the California Prison System by fitting the model to surveillance data.
Things to consider
- This group is recommended for:
- Those who want to learn about branching process models and/or parameter estimation
- Those who are interested in linking surveillance data to predictions of the effect size of public health interventions.
- This group will have the opportunity to engage in any of the following:
- Applying modeling assumptions to raw data in order to adjust for observation bias
- Inference of parameter values and evaluation of model performance
- Review of relevant literature
Data
- Incidence data for COVID in the California prison system