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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] [What about the content of this article? (0)] [Affiliation(s)] [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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van Duuren LA, Ozik J, Spliet R, Collier NT, Lansdorp-Vogelaar I, Meester RGS. An Evolutionary Algorithm to Personalize Stool-Based Colorectal Cancer Screening. Front Physiol 2022; 12:718276. [PMID: 35153804 PMCID: PMC8826712 DOI: 10.3389/fphys.2021.718276] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 12/21/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Fecal immunochemical testing (FIT) is an established method for colorectal cancer (CRC) screening. Measured FIT-concentrations are associated with both present and future risk of CRC, and may be used for personalized screening. However, evaluation of personalized screening is computationally challenging. In this study, a broadly applicable algorithm is presented to efficiently optimize personalized screening policies that prescribe screening intervals and FIT-cutoffs, based on age and FIT-history. METHODS We present a mathematical framework for personalized screening policies and a bi-objective evolutionary algorithm that identifies policies with minimal costs and maximal health benefits. The algorithm is combined with an established microsimulation model (MISCAN-Colon), to accurately estimate the costs and benefits of generated policies, without restrictive Markov assumptions. The performance of the algorithm is demonstrated in three experiments. RESULTS In Experiment 1, a relatively small benchmark problem, the optimal policies were known. The algorithm approached the maximum feasible benefits with a relative difference of 0.007%. Experiment 2 optimized both intervals and cutoffs, Experiment 3 optimized cutoffs only. Optimal policies in both experiments are unknown. Compared to policies recently evaluated for the USPSTF, personalized screening increased health benefits up to 14 and 4.3%, for Experiments 2 and 3, respectively, without adding costs. Generated policies have several features concordant with current screening recommendations. DISCUSSION The method presented in this paper is flexible and capable of optimizing personalized screening policies evaluated with computationally-intensive but established simulation models. It can be used to inform screening policies for CRC or other diseases. For CRC, more debate is needed on what features a policy needs to exhibit to make it suitable for implementation in practice.
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Affiliation(s)
- Luuk A. van Duuren
- Department of Public Health, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Jonathan Ozik
- Decision and Infrastructure Sciences, Argonne National Laboratory, Lemont, IL, United States
| | - Remy Spliet
- Econometric Institute, Erasmus University Rotterdam, Rotterdam, Netherlands
| | - Nicholson T. Collier
- Decision and Infrastructure Sciences, Argonne National Laboratory, Lemont, IL, United States
| | | | - Reinier G. S. Meester
- Department of Public Health, Erasmus University Medical Center, Rotterdam, Netherlands
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Tatara E, Collier NT, Ozik J, Gutfraind A, Cotler SJ, Dahari H, Major M, Boodram B. MULTI-OBJECTIVE MODEL EXPLORATION OF HEPATITIS C ELIMINATION IN AN AGENT-BASED MODEL OF PEOPLE WHO INJECT DRUGS. Proc Winter Simul Conf 2019; 2019:1008-1019. [PMID: 32624641 PMCID: PMC7335458 DOI: 10.1109/wsc40007.2019.9004747] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Hepatitis C (HCV) is a leading cause of chronic liver disease and mortality worldwide and persons who inject drugs (PWID) are at the highest risk for acquiring and transmitting HCV infection. We developed an agent-based model (ABM) to identify and optimize direct-acting antiviral (DAA) therapy scale-up and treatment strategies for achieving the World Health Organization (WHO) goals of HCV elimination by the year 2030. While DAA is highly efficacious, it is also expensive, and therefore intervention strategies should balance the goals of elimination and the cost of the intervention. Here we present and compare two methods for finding PWID treatment enrollment strategies by conducting a standard model parameter sweep and compare the results to an evolutionary multi-objective optimization algorithm. The evolutionary approach provides a pareto-optimal set of solutions that minimizes treatment costs and incidence rates.
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Affiliation(s)
| | | | - Jonathan Ozik
- Decision and Infrastructure Sciences Division, Argonne National Laboratory, 5735 S Ellis Ave, Chicago, IL 60637, USA
| | | | | | - Harel Dahari
- Division of Hepatology, Dept of Medicine, Loyola University Medical Center, 2160 S 1st Ave, Maywood, IL 60153, USA
| | - Marian Major
- Division of Viral Products, US Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD 20993, USA
| | - Basmattee Boodram
- School of Public Health, University of Illinois at Chicago, 1603 W Taylor St, Chicago, IL 60612, USA
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Wozniak JM, Jain R, Balaprakash P, Ozik J, Collier NT, Bauer J, Xia F, Brettin T, Stevens R, Mohd-Yusof J, Cardona CG, Essen BV, Baughman M. CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research. BMC Bioinformatics 2018; 19:491. [PMID: 30577736 PMCID: PMC6302440 DOI: 10.1186/s12859-018-2508-4] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Current multi-petaflop supercomputers are powerful systems, but present challenges when faced with problems requiring large machine learning workflows. Complex algorithms running at system scale, often with different patterns that require disparate software packages and complex data flows cause difficulties in assembling and managing large experiments on these machines. RESULTS This paper presents a workflow system that makes progress on scaling machine learning ensembles, specifically in this first release, ensembles of deep neural networks that address problems in cancer research across the atomistic, molecular and population scales. The initial release of the application framework that we call CANDLE/Supervisor addresses the problem of hyper-parameter exploration of deep neural networks. CONCLUSIONS Initial results demonstrating CANDLE on DOE systems at ORNL, ANL and NERSC (Titan, Theta and Cori, respectively) demonstrate both scaling and multi-platform execution.
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Affiliation(s)
| | - Rajeev Jain
- Argonne National Laboratory, Argonne, IL, USA
| | | | | | | | - John Bauer
- Argonne National Laboratory, Argonne, IL, USA
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Ozik J, Collier NT, Wozniak JM, Macal C, An G. Extreme-scale Dynamic Exploration of a Distributed Agent-based Model with the EMEWS Framework. IEEE Trans Comput Soc Syst 2018; 5:884-895. [PMID: 30349868 PMCID: PMC6195352 DOI: 10.1109/tcss.2018.2859189] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Agent-based models (ABMs) integrate multiple scales of behavior and data to produce higher-order dynamic phenomena and are increasingly used in the study of important social complex systems in biomedicine, socio-economics and ecology/resource management. However, the development, validation and use of ABMs is hampered by the need to execute very large numbers of simulations in order to identify their behavioral properties, a challenge accentuated by the computational cost of running realistic, large-scale, potentially distributed ABM simulations. In this paper we describe the Extreme-scale Model Exploration with Swift (EMEWS) framework, which is capable of efficiently composing and executing large ensembles of simulations and other "black box" scientific applications while integrating model exploration (ME) algorithms developed with the use of widely available 3rd-party libraries written in popular languages such as R and Python. EMEWS combines novel stateful tasks with traditional run-to-completion many task computing (MTC) and solves many problems relevant to high-performance workflows, including scaling to very large numbers (millions) of tasks, maintaining state and locality information, and enabling effective multiple-language problem solving. We present the high-level programming model of the EMEWS framework and demonstrate how it is used to integrate an active learning ME algorithm to dynamically and efficiently characterize the parameter space of a large and complex, distributed Message Passing Interface (MPI) agent-based infectious disease model.
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Affiliation(s)
- Jonathan Ozik
- Argonne National Laboratory and The University of Chicago
| | | | | | - Charles Macal
- Argonne National Laboratory and The University of Chicago
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Abstract
As high-performance computing resources have become increasingly available, new modes of computational processing and experimentation have become possible. This tutorial presents the Extreme-scale Model Exploration with Swift/T (EMEWS) framework for combining existing capabilities for model exploration approaches (e.g., model calibration, metaheuristics, data assimilation) and simulations (or any "black box" application code) with the Swift/T parallel scripting language to run scientific workflows on a variety of computing resources, from desktop to academic clusters to Top 500 level supercomputers. We will present a number of use-cases, starting with a simple agent-based model parameter sweep, and ending with a complex adaptive parameter space exploration workflow coordinating ensembles of distributed simulations. The use-cases are published on a public repository for interested parties to download and run on their own.
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Affiliation(s)
- Jonathan Ozik
- Argonne National Laboratory, 9700 S. Cass Ave., Argonne, IL 60439, USA
| | | | - Justin M Wozniak
- Argonne National Laboratory, 9700 S. Cass Ave., Argonne, IL 60439, USA
| | - Carmine Spagnuolo
- Dipartimento di Informatica, ISISLab, Università degli Studi di Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano SA, Salerno, ITALY
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North MJ, Collier NT, Ozik J, Tatara ER, Macal CM, Bragen M, Sydelko P. Complex adaptive systems modeling with Repast Simphony. ACTA ACUST UNITED AC 2013. [DOI: 10.1186/2194-3206-1-3] [Citation(s) in RCA: 314] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Abstract
Purpose
This paper is to describe development of the features and functions of Repast Simphony, the widely used, free, and open source agent-based modeling environment that builds on the Repast 3 library. Repast Simphony was designed from the ground up with a focus on well-factored abstractions. The resulting code has a modular architecture that allows individual components such as networks, logging, and time scheduling to be replaced as needed. The Repast family of agent-based modeling software has collectively been under continuous development for more than 10 years.
Method
Includes reviewing other free and open-source modeling libraries and environments as well as describing the architecture of Repast Simphony. The architectural description includes a discussion of the Simphony application framework, the core module, ReLogo, data collection, the geographical information system, visualization, freeze drying, and third party application integration.
Results
Include a review of several Repast Simphony applications and brief tutorial on how to use Repast Simphony to model a simple complex adaptive system.
Conclusions
We discuss opportunities for future work, including plans to provide support for increasingly large-scale modeling efforts.
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Abstract
C-quence is a software application that matches sequential patterns of qualitative data specified by the user and calculates the rate of occurrence of these patterns in a data set. Although it was designed to facilitate analyses of face-to-face interaction, it is applicable to any data set involving categorical data and sequential information. C-quence queries are constructed using a graphical user interface. The program does not limit the complexity of the sequential patterns specified by the user.
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Affiliation(s)
- Starkey Duncan
- Department of Psychology, University of Chicago, 5848 S. University Ave., Chicago, IL 60637, USA.
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