Transmission models of respiratory infections in carceral settings: A systematic review.

IF 3 3区 医学 Q2 INFECTIOUS DISEASES Epidemics Pub Date : 2024-12-06 DOI:10.1016/j.epidem.2024.100809
Sara N Levintow, Molly Remch, Emily P Jones, Justin Lessler, Jessie K Edwards, Lauren Brinkley-Rubinstein, Dana K Rice, David L Rosen, Kimberly A Powers
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Abstract

Background: The prevention and control of infectious disease outbreaks in carceral settings face unique challenges. Transmission modeling is a powerful tool for understanding and addressing these challenges, but reviews of modeling work in this context pre-date the proliferation of outbreaks in jails and prisons during the SARS-CoV-2 pandemic. We conducted a systematic review of studies using transmission models of respiratory infections in carceral settings before and during the pandemic.

Methods: We searched PubMed, Embase, Scopus, CINAHL, and PsycInfo to identify studies published between 1970 and 2024 that modeled transmission of respiratory infectious diseases in carceral settings. We extracted information on the diseases, populations, and settings modeled; approaches used for parameterizing models and simulating transmission; outcomes of interest and techniques for model calibration, validation, and sensitivity analyses; and types, impacts, and ethical aspects of modeled interventions.

Results: Forty-six studies met eligibility criteria, with transmission dynamics of tuberculosis modeled in 24 (52 %), SARS-CoV-2 in 20 (43 %), influenza in one (2 %), and varicella-zoster virus in one (2 %). Carceral facilities in the United States were the most common focus (15, 33 %), followed by Brazil (8, 17 %). Most studies (36, 80 %) used compartmental models (vs. individual- or agent-based). Tuberculosis studies typically modeled transmission within a single facility, while most SARS-CoV-2 studies simulated transmission in multiple places, including between carceral and community settings. Half of studies fit models to epidemiological data; three validated model predictions. Models were used to estimate past or potential future intervention impacts in 32 (70 %) studies, forecast the status quo (without changing conditions) in six (13 %), and examine only theoretical aspects of transmission in eight (17 %). Interventions commonly involved testing and treatment, quarantine and isolation, and/or facility ventilation. Modeled interventions substantially reduced transmission, but some were not well-defined or did not consider ethical issues.

Conclusion: The pandemic prompted urgent attention to transmission dynamics in jails and prisons, but there has been little modeling of respiratory infections other than SARS-CoV-2 and tuberculosis. Increased attention to calibration, validation, and the practical and ethical aspects of intervention implementation could improve translation of model estimates into tangible benefits for the highly vulnerable populations in carceral settings.

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来源期刊
Epidemics
Epidemics INFECTIOUS DISEASES-
CiteScore
6.00
自引率
7.90%
发文量
92
审稿时长
140 days
期刊介绍: Epidemics publishes papers on infectious disease dynamics in the broadest sense. Its scope covers both within-host dynamics of infectious agents and dynamics at the population level, particularly the interaction between the two. Areas of emphasis include: spread, transmission, persistence, implications and population dynamics of infectious diseases; population and public health as well as policy aspects of control and prevention; dynamics at the individual level; interaction with the environment, ecology and evolution of infectious diseases, as well as population genetics of infectious agents.
期刊最新文献
Infectious diseases: Household modeling with missing data. Transmission models of respiratory infections in carceral settings: A systematic review. Estimating effective reproduction numbers using wastewater data from multiple sewersheds for SARS-CoV-2 in California counties. Building in-house capabilities in health agencies and outsourcing to academia or industry: Considerations for effective infectious disease modelling. Real-time estimates of the emergence and dynamics of SARS-CoV-2 variants of concern: A modeling approach.
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