Background Correctional facilities can act as amplifiers of infectious disease outbreaks. Small community outbreaks can cause larger prison outbreaks, which can in turn exacerbate the community outbreaks. However, strategies for epidemic control in communities and correctional facilities are generally not closely coordinated. We sought to evaluate different strategies for coordinated control and examine their robustness.

Methods We developed a stochastic simulation model of an epidemic spreading across a network of communities and correctional facilities. We parameterized it for the initial phases of the COVID-19 epidemic for 1) California communities and prisons based on community data from covidestim, prison data from the California Department of Corrections and Rehabilitation, and mobility data from SafeGraph; and 2) a small, illustrative network of communities and prisons. For each community or prison, control measures were defined by the intensity of two activities: a) screening to detect and isolate cases and b) non-pharmaceutical interventions (e.g., masking and social distancing) to reduce transmission. We compared the performance of different control strategies including heuristic and reinforcement learning (RL) strategies using a reward function, which accounted for both the benefit of averted infections and non-linear cost of the control measures. Finally, we performed analyses to interpret the optimal strategy and examine its robustness.

Results The RL control strategy robustly outperformed other strategies including heuristic approaches like those that were largely used during the COVID-19 epidemic. The RL strategy prioritized different characteristics of communities versus prisons when allocating control resources, and exhibited geo-temporal patterns consistent with mitigating prison amplification dynamics.

Conclusion RL is a promising method for controlling epidemic spread on networks of communities and correctional facilities, providing insights that can help guide policy.

Highlights

For modelers, we developed a stochastic simulation model of an epidemic spreading across a network of communities and correctional facilities, and we parameterized it for the initial phases of the COVID-19 epidemic for California communities and prisons in addition to an illustrative network.

We compared different control strategies, and we found that reinforcement leaning robustly outperformed the other strategies including heuristic approaches like those that were largely used during the COVID-19 epidemic.

For policy makers, our work suggests that they should consider investing in the further development of such methods and using them for future epidemics.

We offer qualitative insights into different factors that might inform resource allocation to communities versus prisons during future epidemics.

The authors have declared no competing interest.

Research reported in this publication was supported in part by the Stanford Covid-19 Emergency Response Fund, established with a gift from the Horowitz Family Foundation, awarded to Dr. Goldhaber-Fiebert; a grant (R37-DA15612) awarded to Dr. Goldhaber-Fiebert from the National Institute on Drug Abuse; and Advanced Micro Devices (Santa Clara, CA, USA), which provided a donation of servers to Dr. Goldhaber-Fiebert. The funders had no role in the study's design, conduct, or reporting, or in the publication decision.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Stanford University's Institutional Review Board approved the use and analysis of primary California Department of Corrections and Rehabilitation (CDCR) data (IRB-55835).

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Input data and code for replication and extension of our analysis will be available via GitHub concurrent with publication.