Integrated Network Analysis of RADx-UP Data to Increase COVID-19 Testing and Vaccination Among Persons Involved with Criminal Legal Systems (PCLS)

项目摘要 2019冠状病毒病（COVID-19）继续在全球造成严重的发病率和死亡率。 美国刑事法律的设置以拥挤的拘留设施和有限的医疗安全资源为特征， (CLS)经历了一些最大的COVID-19疫情。参与CLS的人员（PCLS） 此外，在释放后还面临着重大的医疗障碍，并经常返回环境 受植根于结构性种族主义的流行病因素的影响：疫苗接种率较低，检测设施较少，医疗 不信任和更高的COVID-19流行率。个人和社会网络层面之间的复杂相互作用 因素可能导致PCLS出现不利的COVID-19结果。但是，尽管他们的重要性，社会 网络影响-以及它们与个人和结构因素的相互作用对COVID-19的影响 检测、疫苗接种和更广泛的健康行为--不会进行常规检查。为了系统地解决这个问题， 我们将利用美国八个州现有的两项RADx-UP研究。社区网络驱动 美国中部最脆弱人群中的COVID-19检测”（C3）研究是独一无二的， 它已经收集了PCLS之间关于测试，疫苗接种和健康行为的纵向社会网络数据， 在美国的五个州。此外，“COVID-19检测和预防在惩教机构”（CTC）研究 评估了美国三个州的PCLS的COVID-19检测、疫苗接种和缓解策略。我们将 将通过CTC项目收集的公共数据元素与网络决定因素相结合 从C3数据估计，以开发基于代理的网络模型（ABNM）-动态系统建模 一种技术，提供了模拟个人行为、社会 结构、政策执行和人口成果的下游评估。拟议 建模研究将：（1）使用机器学习来量化网络层面对COVID-19的影响 PCLS社区内的检测、疫苗接种和健康行为;（2）建立基于代理的网络 ABNM平台，该平台集成了测试和 从CTC研究中收集的疫苗接种和C3研究中的网络决定因素;（3）模拟影响 对PCLS及其社区的COVID-19疫苗接种、检测和更广泛的健康行为的干预 社区.这一方法将使人们深入了解网络知情干预措施的潜在影响 使用RADx-UP数据、社交网络分析、机器学习和基于代理的建模来识别 采取干预措施，降低PCLS及其社区的COVID-19发病率和死亡率。

Project Summary Coronavirus Disease 2019 (COVID-19) continues to cause significant morbidity and mortality across the world. Characterized by crowded detention facilities and limited medical safety resources, US criminal legal settings (CLS) have experienced some of the largest COVID-19 outbreaks. Persons involved with CLS (PCLS) additionally experience significant barriers to health care upon release, and often return to environments impacted by syndemic factors rooted in structural racism: lower vaccine access, fewer testing facilities, medical mistrust, and higher COVID-19 prevalence. A complex interplay between individual and social network-level factors may be driving the adverse COVID-19 outcomes among PCLS. But, despite their importance, social network influences – and the effect of their interaction with individual ands structural factors on COVID-19 testing, vaccination and broader health behaviors – are not routinely examined. To systematically address this gap, we will leverage two existing RADx-UP studies across eight US states. The “Community Network Driven COVID-19 Testing Among Most Vulnerable Populations in the Central United States” (C3) study is unique in that it has collected longitudinal social network data on testing, vaccination and health behaviors among PCLS in five US states. Additionally, the “COVID-19 Testing and Prevention in Correctional Settings” (CTC) study has assessed COVID-19 testing, vaccination, and mitigation strategies for PCLS in three US states. We will integrate the common data elements collected through the CTC project with the network determinants estimated from the C3 data to develop an agent-based network model (ABNM) – a dynamic systems modeling technique that provides the ability to simulate emergent interaction between individual behaviors, social structures, policy implementation, and downstream assessment of population outcomes. The proposed modeling study will: (1) use machine learning to quantify the impact of network-level influences on COVID-19 testing, vaccination, and health behaviors within PCLS communities; (2) build an agent-based network modeling (ABNM) platform that integrates the individual common data elements (CDEs) of testing and vaccination collected from the CTC study and network determinants from the C3 study; (3) simulate the effects of interventions on COVID-19 vaccination, testing and broader health behaviors in PCLS and their communities. This approach will provide insight on the potential impacts of network-informed interventions using RADx-UP data, social network analysis, machine learning, and agent-based modeling to identify interventions to reduce COVID-19 morbidity and mortality among PCLS and their communities.