Document text
Principal Investigator: Aditya Subhash Khanna
Organization: BROWN UNIVERSITY
Fiscal Year: 2024
Award: $207,669
Funding agency: National Institute on Minority Health and Health Disparities
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.
Terms: <Address><Affect><Attitude><Automobile Driving><Behavior><Biologic Models><Biological Models><COVID associated morbidity><COVID induced morbidity><COVID morbidity><COVID prevalence><COVID related morbidity><COVID testing><COVID-19><COVID-19 associated death><COVID-19 associated fatality><COVID-19 associated morbidity><COVID-19 associated mortality><COVID-19 death><COVID-19 disease prevalence><COVID-19 fatality><COVID-19 induced death><COVID-19 induced fatality><COVID-19 induced morbidity><COVID-19 induced mortality><COVID-19 infection><COVID-19 morbidity><COVID-19 mortality><COVID-19 outbreak><COVID-19 prevalence><COVID-19 prevention><COVID-19 related death><COVID-19 related fatality><COVID-19 related morbidity><COVID-19 related mortality><COVID-19 testing><COVID-19 transmission><COVID-19 vaccination><COVID-19 virus infection><COVID-19 virus transmission><COVID19 associated death><COVID19 associated fatality><COVID19 associated mortality><COVID19 death><COVID19 fatality><COVID19 induced death><COVID19 induced fatality><COVID19 induced mortality><COVID19 infection><COVID19 mortality><COVID19 outbreak><COVID19 related death><COVID19 related fatality><COVID19 related mortality><COVID19 testing><CV-19><Common Data Element><Communities><Community Networks><Complex><Coronavirus Infectious Disease 2019><Crowding><Data><Diffusion><Disparities><Disparity><Drug Courts><Ensure><Environment><Family><Friends><General Population><General Public><Graph><Health><Health Information System><Health behavior><Imprisonment><Incidence><Individual><Inequity><Infection><Insurance><Intervention><Intervention Strategies><Investigation><Jail><Legal><Legal system><Machine Learning><Medical><Minnesota><Misinformation><Model System><Modeling><Modernization><Morbidity><Morbidity - disease rate><Network Analysis><Outcome><Pathway Analysis><Persons><Policies><Population><Position><Positioning Attribute><Preventative strategy><Prevention strategy><Preventive><Preventive strategy><Prisons><RADx Underserved Populations><RADx-UP><Rapid Acceleration in Diagnostics for Underserved Populations><Rapid Acceleration of Diagnostics Underrepresented Persons><Rapid Acceleration of Diagnostics Underserved Populations><Research Resources><Resources><Rhode Island><SARS-CoV-2 associated death><SARS-CoV-2 associated fatality><SARS-CoV-2 associated mortality><SARS-CoV-2 death><SARS-CoV-2 fatality><SARS-CoV-2 induced death><SARS-CoV-2 induced fatality><SARS-CoV-2 induced mortality><SARS-CoV-2 infection><SARS-CoV-2 infection prevalence><SARS-CoV-2 mortality><SARS-CoV-2 outbreak><SARS-CoV-2 prevalence><SARS-CoV-2 related death><SARS-CoV-2 related fatality><SARS-CoV-2 related mortality><SARS-CoV-2 testing><SARS-CoV-2 transmission><SARS-CoV-2 vaccination><SARS-CoV2 infection><Safety><Sampling><Severe acute respiratory syndrome coronavirus 2 infection><Severe acute respiratory syndrome coronavirus 2 outbreak><Severe acute respiratory syndrome coronavirus 2 vaccination><Site><Social Network><Socio-economic status><Socioeconomic Status><Structural Racism><Structure><Study models><System><Techniques><Testing><US State><United States><Vaccinated><Vaccination><Vaccines><Vulnerable Populations><Washington><accept vaccination><accept vaccine><access to health care><access to healthcare><access to vaccination><access to vaccines><accessibility of health care><accessibility to health care><accessibility to healthcare><adverse consequence><adverse outcome><barrier to care><barrier to health care><barrier to healthcare><barrier to treatment><community re-entry><community reentry><community setting><comparative><coronavirus disease 2019><coronavirus disease 2019 associated death><coronavirus disease 2019 associated fatality><coronavirus disease 2019 associated morbidity><coronavirus disease 2019 associated mortality><coronavirus disease 2019 death><coronavirus disease 2019 fatality><coronavirus disease 2019 induced death><coronavirus disease 2019 induced fatality><coronavirus disease 2019 induced morbidity><coronavirus disease 2019 induced mortality><coronavirus disease 2019 infection><coronavirus disease 2019 morbidity><coronavirus disease 2019 mortality><coronavirus disease 2019 outbreak><coronavirus disease 2019 prevention><coronavirus disease 2019 related death><coronavirus disease 2019 related fatality><coronavirus disease 2019 related morbidity><coronavirus disease 2019 related mortality><coronavirus disease 2019 testing><coronavirus disease 2019 transmission><coronavirus disease 2019 vaccination><coronavirus disease 2019 virus transmission><coronavirus disease associated morbidity><coronavirus disease induced morbidity><coronavirus disease morbidity><coronavirus disease related morbidity><coronavirus disease testing><coronavirus disease-19><coronavirus disease-19 mortality><coronavirus disease-19 outbreak><coronavirus disease-19 prevention><coronavirus infectious disease-19><coronavirus testing><data integration><death due to COVID-19><death due to COVID19><death due to SARS-CoV-2><death due to coronavirus disease 2019><death due to severe acute respiratory syndrome coronavirus 2><death in COVID><death in COVID-19><death in SARS-CoV-2><death in coronavirus disease><death in coronavirus disease 2019><death in severe acute respiratory syndrome coronavirus 2><diffused><diffuses><diffusing><diffusions><disparity in health><driving><dynamic system><dynamical system><experience><fatality due to COVID-19><fatality due to COVID19><fatality due to SARS-CoV-2><fatality due to coronavirus disease 2019><fatality due to severe acute respiratory syndrome coronavirus 2><future pandemic><group of color><health care access><health care availability><health care service access><health care service availability><health disparity><health related behavior><healthcare access><healthcare accessibility><healthcare availability><healthcare service access><healthcare service availability><hesitant to vaccination><improved><incarcerated><incarceration><individual of color><infected with COVID-19><infected with COVID19><infected with SARS-CoV-2><infected with SARS-CoV2><infected with coronavirus disease 2019><infected with severe acute respiratory syndrome coronavirus 2><infection rate><innovate><innovation><innovative><insight><intervention effect><interventional strategy><machine based learning><machine learning based method><machine learning method><machine learning methodologies><mortality><mortality due to COVID-19><mortality due to COVID19><mortality due to SARS-CoV-2><mortality due to coronavirus disease 2019><mortality due to severe acute respiratory syndrome coronavirus 2><network models><next pandemic><obstacle to care><obstacle to healthcare><outbreak of SARS-CoV-2><outcome following vaccination><outcome following vaccine><parole><peer><people of color><person of color><population of color><prevent COVID-19><prevent coronavirus disease 2019><probation><rate of infection><result following vaccination><result following vaccine><severe acute respiratory syndrome coronavirus 2 associated death><severe acute respiratory syndrome coronavirus 2 associated fatality><severe acute respiratory syndrome coronavirus 2 associated mortality><severe acute respiratory syndrome coronavirus 2 death><severe acute respiratory syndrome coronavirus 2 fatality><severe acute respiratory syndrome coronavirus 2 induced death><severe acute respiratory syndrome coronavirus 2 induced fatality><severe acute respiratory syndrome coronavirus 2 induced mortality><severe acute respiratory syndrome coronavirus 2 mortality><severe acute respiratory syndrome coronavirus 2 related death><severe acute respiratory syndrome coronavirus 2 related fatality><severe acute respiratory syndrome coronavirus 2 related mortality><severe acute respiratory syndrome coronavirus 2 testing><severe acute respiratory syndrome coronavirus 2 transmission><social health determinants><social structural><social structure><socio-economic position><socio-structural><socioeconomic position><sociostructural><structural determinants><structural factors><syndemic><synergistic epidemic><testing for COVID><testing of COVID><transmitted COVID-19><transmitted SARS-CoV-2><transmitted coronavirus disease 2019><transmitted severe acute respiratory syndrome coronavirus 2><uptake><vaccinate against COVID-19><vaccinate against SARS-CoV-2><vaccinate against coronavirus disease 2019><vaccinate against severe acute respiratory syndrome coronavirus 2><vaccination acceptability><vaccination acceptance><vaccination access><vaccination against COVID-19><vaccination against SARS-CoV-2><vaccination against Severe acute respiratory syndrome coronavirus 2><vaccination against coronavirus disease 2019><vaccination availability><vaccination confidence><vaccination hesitancy><vaccination outcome><vaccination result><vaccination uptake><vaccination willingness><vaccine acceptability><vaccine acceptance><vaccine access><vaccine availability><vaccine confidence><vaccine hesitancy><vaccine hesitant><vaccine outcome><vaccine result><vaccine uptake><vaccine willingness><vulnerable group><vulnerable individual><vulnerable people>