Stanford University School of Medicine, Stanford, California, USA.
Department of Industrial and Systems Engineering, University of Washington, Seattle, Washington, USA.
Med Decis Making. 2023 Jan;43(1):42-52. doi: 10.1177/0272989X221115364. Epub 2022 Jul 29.
Historically, correctional facilities have had large outbreaks of respiratory infectious diseases like COVID-19. Hence, importation and exportation of such diseases from correctional facilities raises substantial concern.
We developed a stochastic simulation model of transmission of respiratory infectious diseases within and between correctional facilities and the community. We investigated the infection dynamics, key governing factors, and relative importance of different infection routes (e.g., incarcerations and releases versus correctional staff). We also developed machine-learning meta-models of the simulation model, which allowed us to examine how our findings depended on different disease, correctional facility, and community characteristics.
We find a magnification-reflection dynamic: a small outbreak in the community can cause a larger outbreak in the correction facility, which can then cause a second, larger outbreak in the community. This dynamic is strongest when community size is relatively small as compared with the size of the correctional population, the initial community R-effective is near 1, and initial prevalence of immunity in the correctional population is low. The timing of the correctional magnification and community reflection peaks in infection prevalence are primarily governed by the initial R-effective for each setting. Because the release rates from prisons are low, our model suggests correctional staff may be a more important infection entry route into prisons than incarcerations and releases; in jails, where incarceration and release rates are much higher, our model suggests the opposite.
We find that across many combinations of respiratory pathogens, correctional settings, and communities, there can be substantial magnification-reflection dynamics, which are governed by several key factors. Our goal was to derive theoretical insights relevant to many contexts; our findings should be interpreted accordingly.
We find a magnification-reflection dynamic: a small outbreak in a community can cause a larger outbreak in a correctional facility, which can then cause a second, larger outbreak in the community.For public health decision makers considering contexts most susceptible to this dynamic, we find that the dynamic is strongest when the community size is relatively small, initial community R-effective is near 1, and the initial prevalence of immunity in the correctional population is low; the timing of the correctional magnification and community reflection peaks in infection prevalence are primarily governed by the initial R-effective for each setting.We find that correctional staff may be a more important infection entry route into prisons than incarcerations and releases; however, for jails, the relative importance of the entry routes may be reversed.For modelers, we combine simulation modeling, machine-learning meta-modeling, and interpretable machine learning to examine how our findings depend on different disease, correctional facility, and community characteristics; we find they are generally robust.
历史上，管教设施曾爆发过 COVID-19 等呼吸道传染病。因此，此类疾病从管教设施的输入和输出引起了人们的极大关注。
我们开发了一个管教设施内和设施间以及社区内呼吸道传染病传播的随机模拟模型。我们调查了感染动态、关键控制因素以及不同感染途径（例如监禁和释放与管教人员）的相对重要性。我们还开发了模拟模型的机器学习元模型，这使我们能够检查我们的发现如何取决于不同的疾病、管教设施和社区特征。
我们发现了一个放大-反射动态：社区中的小爆发会导致管教设施中的更大爆发，然后又会导致社区中的第二次更大爆发。当社区规模相对于管教人口规模较小时，当社区初始 R-有效接近 1 且管教人口中初始免疫流行率较低时，这种动态最强。管教放大和社区反射峰值的感染流行时间主要由每个环境的初始 R-有效控制。由于监狱的释放率较低，我们的模型表明管教人员可能是比监禁和释放更重要的监狱感染进入途径；在监禁和释放率高得多的监狱中，我们的模型表明情况恰恰相反。
我们发现，对于许多呼吸道病原体、管教环境和社区的组合，可能会出现大量放大-反射动态，这些动态受几个关键因素的控制。我们的目标是得出与许多情况相关的理论见解；我们的发现应相应解释。
我们发现了一个放大-反射动态：社区中的小爆发会导致管教设施中的更大爆发，然后又会导致社区中的第二次更大爆发。对于考虑最容易受到这种动态影响的情况的公共卫生决策者，我们发现当社区规模相对较小时，动态最强，社区初始 R-有效接近 1，管教人口中初始免疫流行率较低；感染流行中管教放大和社区反射峰值的时间主要由每个设置的初始 R-有效控制。我们发现，管教人员可能是比监禁和释放更重要的监狱感染进入途径；然而，对于监狱来说，进入途径的相对重要性可能会颠倒。对于建模人员，我们结合模拟建模、机器学习元模型和可解释的机器学习来检查我们的发现如何取决于不同的疾病、管教设施和社区特征；我们发现它们通常是稳健的。
