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Non-Invasive Diagnosis of Liver Fibrosis in Chronic Hepatitis C using Mathematical Modeling and Simulation. ELECTRONICS 2022. [DOI: 10.3390/electronics11081260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Hepatitis C is a viral infection (HCV) that causes liver inflammation, and it was found that it affects over 170 million people around the world, with Egypt having the highest rate in the world. Unfortunately, serial liver biopsies, which can be invasive, expensive, risky, and inconvenient to patients, are typically used for the diagnosis of liver fibrosis progression. This study presents the development, validation, and evaluation of a prediction mathematical model for non-invasive diagnosis of liver fibrosis in chronic HCV. The proposed model in this article uses a set of nonlinear ordinary differential equations as its core and divides the population into six groups: Susceptible, Treatment, Responder, Non-Responder, Cured, and Fibrosis. The validation approach involved the implementation of two equivalent simulation models that examine the proposed process from different perspectives. A system dynamics model was developed to understand the nonlinear behavior of the diagnosis process over time. The system dynamics model was then transformed to an equivalent agent-based model to examine the system at the individual level. The numerical analysis and simulation results indicate that the earlier the HCV treatment is implemented, the larger the group of people who will become responders, and less people will develop complications such as fibrosis.
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Tatara E, Gutfraind A, Collier NT, Echevarria D, Cotler SJ, Major ME, Ozik J, Dahari H, Boodram B. Modeling hepatitis C micro-elimination among people who inject drugs with direct-acting antivirals in metropolitan Chicago. PLoS One 2022; 17:e0264983. [PMID: 35271634 PMCID: PMC8912265 DOI: 10.1371/journal.pone.0264983] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 02/01/2022] [Indexed: 02/03/2023] Open
Abstract
Hepatitis C virus (HCV) infection is a leading cause of chronic liver disease and mortality worldwide. Direct-acting antiviral (DAA) therapy leads to high cure rates. However, persons who inject drugs (PWID) are at risk for reinfection after cure and may require multiple DAA treatments to reach the World Health Organization's (WHO) goal of HCV elimination by 2030. Using an agent-based model (ABM) that accounts for the complex interplay of demographic factors, risk behaviors, social networks, and geographic location for HCV transmission among PWID, we examined the combination(s) of DAA enrollment (2.5%, 5%, 7.5%, 10%), adherence (60%, 70%, 80%, 90%) and frequency of DAA treatment courses needed to achieve the WHO's goal of reducing incident chronic infections by 90% by 2030 among a large population of PWID from Chicago, IL and surrounding suburbs. We also estimated the economic DAA costs associated with each scenario. Our results indicate that a DAA treatment rate of >7.5% per year with 90% adherence results in 75% of enrolled PWID requiring only a single DAA course; however 19% would require 2 courses, 5%, 3 courses and <2%, 4 courses, with an overall DAA cost of $325 million to achieve the WHO goal in metropolitan Chicago. We estimate a 28% increase in the overall DAA cost under low adherence (70%) compared to high adherence (90%). Our modeling results have important public health implications for HCV elimination among U.S. PWID. Using a range of feasible treatment enrollment and adherence rates, we report robust findings supporting the need to address re-exposure and reinfection among PWID to reduce HCV incidence.
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Affiliation(s)
- Eric Tatara
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, Illinois, United States of America
- Decision and Infrastructure Sciences, Argonne National Laboratory, Argonne, Illinois, United States of America
- * E-mail: (ET); (HD); (BB)
| | - Alexander Gutfraind
- The Program for Experimental & Theoretical Modeling, Division of Hepatology, Department of Medicine, Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois, United States of America
- Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Illinois, United States of America
| | - Nicholson T. Collier
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, Illinois, United States of America
- Decision and Infrastructure Sciences, Argonne National Laboratory, Argonne, Illinois, United States of America
| | - Desarae Echevarria
- The Program for Experimental & Theoretical Modeling, Division of Hepatology, Department of Medicine, Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois, United States of America
| | - Scott J. Cotler
- The Program for Experimental & Theoretical Modeling, Division of Hepatology, Department of Medicine, Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois, United States of America
| | - Marian E. Major
- Division of Viral Products, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Jonathan Ozik
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, Illinois, United States of America
- Decision and Infrastructure Sciences, Argonne National Laboratory, Argonne, Illinois, United States of America
| | - Harel Dahari
- The Program for Experimental & Theoretical Modeling, Division of Hepatology, Department of Medicine, Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois, United States of America
- * E-mail: (ET); (HD); (BB)
| | - Basmattee Boodram
- Division of Community Health Sciences, School of Public Health, University of Illinois at Chicago, Chicago, Illinois, United States of America
- * E-mail: (ET); (HD); (BB)
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Boodram B, Mackesy-Amiti ME, Khanna A, Brickman B, Dahari H, Ozik J. People who inject drugs in metropolitan Chicago: A meta-analysis of data from 1997-2017 to inform interventions and computational modeling toward hepatitis C microelimination. PLoS One 2022; 17:e0248850. [PMID: 35020725 PMCID: PMC8754317 DOI: 10.1371/journal.pone.0248850] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 12/13/2021] [Indexed: 02/03/2023] Open
Abstract
Progress toward hepatitis C virus (HCV) elimination in the United States is not on track to meet targets set by the World Health Organization, as the opioid crisis continues to drive both injection drug use and increasing HCV incidence. A pragmatic approach to achieving this is using a microelimination approach of focusing on high-risk populations such as people who inject drugs (PWID). Computational models are useful in understanding the complex interplay of individual, social, and structural level factors that might alter HCV incidence, prevalence, transmission, and treatment uptake to achieve HCV microelimination. However, these models need to be informed with realistic sociodemographic, risk behavior and network estimates on PWID. We conducted a meta-analysis of research studies spanning 20 years of research and interventions with PWID in metropolitan Chicago to produce parameters for a synthetic population for realistic computational models (e.g., agent-based models). We then fit an exponential random graph model (ERGM) using the network estimates from the meta-analysis in order to develop the network component of the synthetic population.
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Affiliation(s)
- Basmattee Boodram
- Division of Community Health Sciences, School of Public Health, University of Illinois at Chicago, Chicago, Illinois, United States of America
| | - Mary Ellen Mackesy-Amiti
- Division of Community Health Sciences, School of Public Health, University of Illinois at Chicago, Chicago, Illinois, United States of America,* E-mail:
| | - Aditya Khanna
- Department of Behavioral and Social Sciences, School of Public Health, Brown University, Providence, Rhode Island, United States of America
| | - Bryan Brickman
- Department of Medicine, Chicago Center for HIV Elimination, University of Chicago, Chicago, Illinois, United States of America
| | - Harel Dahari
- Division of Hepatology, Department of Medicine, Loyola University Medical Center, Maywood, Illinois, United States of America
| | - Jonathan Ozik
- Decision and Infrastructure Sciences Division, Argonne National Laboratory, Lemont, Illinois, United States of America
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Tatara E, Schneider J, Quasebarth M, Collier N, Pollack H, Boodram B, Friedman S, Salisbury-Afshar E, Mackesy-Amiti ME, Ozik J. Application of Distributed Agent-based Modeling to Investigate Opioid Use Outcomes in Justice Involved Populations. IEEE INTERNATIONAL SYMPOSIUM ON PARALLEL & DISTRIBUTED PROCESSING, WORKSHOPS AND PHD FORUM : [PROCEEDINGS]. IEEE INTERNATIONAL SYMPOSIUM ON PARALLEL & DISTRIBUTED PROCESSING, WORKSHOPS AND PHD FORUM 2021; 2021:989-997. [PMID: 35865008 PMCID: PMC9297575 DOI: 10.1109/ipdpsw52791.2021.00157] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Criminal justice involved (CJI) individuals with a history of opioid use disorder (OUD) are at high risk of overdose and death in the weeks following release from jail. We developed the Justice-Community Circulation Model (JCCM) to investigate OUD/CJI dynamics post-release and the effects of interventions on overdose deaths. The JCCM uses a synthetic agent-based model population of approximately 150,000 unique individuals that is generated using demographic information collected from multiple Chicago-area studies and data sets. We use a high-performance computing (HPC) workflow to implement a sequential approximate Bayesian computation algorithm for calibrating the JCCM. The calibration results in the simulated joint posterior distribution of the JCCM input parameters. The calibrated model is used to investigate the effects of a naloxone intervention for a mass jail release. The simulation results show the degree to which a targeted intervention focusing on recently released jail inmates can help reduce the risk of death from opioid overdose.
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Affiliation(s)
- Eric Tatara
- Decision and Infrastructure Sciences Division, Argonne National Laboratory, Lemont, IL, USA
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
| | - John Schneider
- Departments of Medicine and Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Madeline Quasebarth
- Departments of Medicine and Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Nicholson Collier
- Decision and Infrastructure Sciences Division, Argonne National Laboratory, Lemont, IL, USA
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
| | - Harold Pollack
- Crown School of Social Work Policy and Practice, University of Chicago, Chicago, IL, USA
| | - Basmattee Boodram
- Division of Community Health Sciences, School of Public Health, University of Illinois at Chicago, Chicago, IL, USA
| | - Sam Friedman
- Department of Population Health, New York University Langone Medical School, New York, NY, USA
| | - Elizabeth Salisbury-Afshar
- Department of Family Medicine and Community Health, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Mary Ellen Mackesy-Amiti
- Division of Community Health Sciences, School of Public Health, University of Illinois at Chicago, Chicago, IL, USA
| | - Jonathan Ozik
- Decision and Infrastructure Sciences Division, Argonne National Laboratory, Lemont, IL, USA
- Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL, USA
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