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There is increasing interest in the use of mechanism-based multi-scale computational models (such as agent-based models (ABMs)) to generate simulated clinical populations in order to discover and evaluate potential diagnostic and therapeutic modalities. The description of the environment in which a biomedical simulation operates (model context) and parameterization of internal model rules (model content) requires the optimization of a large number of free parameters. In this work, we utilize a nested active learning (AL) workflow to efficiently parameterize and contextualize an ABM of systemic inflammation used to examine sepsis. Contextual parameter space was examined using four parameters external to the model's rule set. The model's internal parameterization, which represents gene expression and associated cellular behaviors, was explored through the augmentation or inhibition of signaling pathways for 12 signaling mediators associated with inflammation and wound healing. We have implemented a nested AL approach in which the clinically relevant (CR) model environment space for a given internal model parameterization is mapped using a small Artificial Neural Network (ANN). The outer AL level workflow is a larger ANN that uses AL to efficiently regress the volume and centroid location of the CR space given by a single internal parameterization. We have reduced the number of simulations required to efficiently map the CR parameter space of this model by approximately 99%. In addition, we have shown that more complex models with a larger number of variables may expect further improvements in efficiency. [ABSTRACT FROM AUTHOR]

A framework for agent-based modeling of the formation of social and political identities is proposed. This model addresses issues about decision making and political policy, as well as the impacts of external shocks and their effect on other areas.

Black men who have sex with men (MSM) and transgender women are disproportionately affected by criminal justice involvement (CJI) and HIV. This study recruited 618 young Black MSM and transgender women in Chicago, IL, using respondent-driven sampling between 2013 and 2014. Random effects logistic regression evaluated predictors of incident CJI over 18 months of follow-up. Controlling for respondent age, gender and sexual identity, spirituality (aOR 0.56, 95% CI 0.33-0.96), and presence of a mother figure (aOR 0.41, 95% CI 0.19-0.89) were protective against CJI. Economic hardship (financial or residential instability vs. neither aOR 2.23, 95% CI 1.10-4.51), two or more past episodes of CJI vs. none (aOR 2.66, 95% CI 1.40-5.66), and substance use (marijuana use vs. none aOR 2.79, 95% CI 1.23-6.34; other drug use vs. none aOR 4.49, 95% CI 1.66-12.16) were associated with CJI during follow-up. Research to identify and leverage resilience factors that can buffer the effects of socioeconomic marginalization may increase the effectiveness of interventions to address the socio-structural factors that increase the risk for CJI among Black MSM and transgender women. Given the intersection of incarceration, HIV and other STIs, and socio-structural stressors, criminal justice settings are important venues for interventions to reduce health inequities in these populations. [ABSTRACT FROM AUTHOR]

Background: Blood-based biomarker tests can potentially change the landscape of colorectal cancer (CRC) screening. We characterize the conditions under which blood test screening would be as effective and cost-effective as annual fecal immunochemical testing or decennial colonoscopy., Methods: We used the 3 Cancer Information and Surveillance Modeling Network-Colon models to compare scenarios of no screening, annual fecal immunochemical testing, decennial colonoscopy, and a blood test meeting Centers for Medicare & Medicaid (CMS) coverage criteria (74% CRC sensitivity and 90% specificity). We varied the sensitivity to detect CRC (74%-92%), advanced adenomas (10%-50%), screening interval (1-3 years), and test cost ($25-$500). Primary outcomes included quality-adjusted life-years (QALY) gained from screening and costs for a US average-risk cohort of individuals aged 45 years., Results: Annual fecal immunochemical testing yielded 125-163 QALY gained per 1000 at a cost of $3811-$5384 per person, whereas colonoscopy yielded 132-177 QALY gained at a cost of $5375-$7031 per person. A blood test with 92% CRC sensitivity and 50% advanced adenoma sensitivity yielded 117-162 QALY gained if used every 3 years and 133-173 QALY gained if used every year but would not be cost-effective if priced above $125 per test. If used every 3 years, a $500 blood test only meeting CMS coverage criteria yielded 83-116 QALY gained at a cost of $8559-$9413 per person., Conclusion: Blood tests that only meet CMS coverage requirements should not be recommended to patients who would otherwise undergo screening by colonoscopy or fecal immunochemical testing because of lower benefit. Blood tests need higher advanced adenoma sensitivity (above 40%) and lower costs (below $125) to be cost-effective., (© The Author(s) 2024. Published by Oxford University Press.)

Access to treatment and medication for opioid use disorder (MOUD) is essential in reducing opioid use and associated behavioral risks, such as syringe sharing among persons who inject drugs (PWID). Syringe sharing among PWID carries high risk of transmission of serious infections such as hepatitis C and HIV. MOUD resources, such as methadone provider clinics, however, are often unavailable to PWID due to barriers like long travel distance to the nearest methadone provider and the required frequency of clinic visits. The goal of this study is to examine the uncertainty in the effects of travel distance in initiating and continuing methadone treatment and how these interact with different spatial distributions of methadone providers to impact co-injection (syringe sharing) risks. A baseline scenario of spatial access was established using the existing locations of methadone providers in a geographical area of metropolitan Chicago, Illinois, USA. Next, different counterfactual scenarios redistributed the locations of methadone providers in this geographic area according to the densities of both the general adult population and according to the PWID population per zip code. We define different reasonable methadone access assumptions as the combinations of short, medium, and long travel distance preferences combined with three urban/suburban travel distance preference. Our modeling results show that when there is a low travel distance preference for accessing methadone providers, distributing providers near areas that have the greatest need (defined by density of PWID) is best at reducing syringe sharing behaviors. However, this strategy also decreases access across suburban locales, posing even greater difficulty in regions with fewer transit options and providers. As such, without an adequate number of providers to give equitable coverage across the region, spatial distribution cannot be optimized to provide equitable access to all PWID. Our study has important implications for increasing interest in methadone as a resurgent treatment for MOUD in the United States and for guiding policy toward improving access to MOUD among PWID., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Tatara et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Purpose: To calibrate Cancer Intervention and Surveillance Modeling Network (CISNET)'s SimCRC, MISCAN-Colon, and CRC-SPIN simulation models of the natural history colorectal cancer (CRC) with an emulator-based Bayesian algorithm and internally validate the model-predicted outcomes to calibration targets., Methods: We used Latin hypercube sampling to sample up to 50,000 parameter sets for each CISNET-CRC model and generated the corresponding outputs. We trained multilayer perceptron artificial neural networks (ANNs) as emulators using the input and output samples for each CISNET-CRC model. We selected ANN structures with corresponding hyperparameters (i.e., number of hidden layers, nodes, activation functions, epochs, and optimizer) that minimize the predicted mean square error on the validation sample. We implemented the ANN emulators in a probabilistic programming language and calibrated the input parameters with Hamiltonian Monte Carlo-based algorithms to obtain the joint posterior distributions of the CISNET-CRC models' parameters. We internally validated each calibrated emulator by comparing the model-predicted posterior outputs against the calibration targets., Results: The optimal ANN for SimCRC had 4 hidden layers and 360 hidden nodes, MISCAN-Colon had 4 hidden layers and 114 hidden nodes, and CRC-SPIN had 1 hidden layer and 140 hidden nodes. The total time for training and calibrating the emulators was 7.3, 4.0, and 0.66 h for SimCRC, MISCAN-Colon, and CRC-SPIN, respectively. The mean of the model-predicted outputs fell within the 95% confidence intervals of the calibration targets in 98 of 110 for SimCRC, 65 of 93 for MISCAN, and 31 of 41 targets for CRC-SPIN., Conclusions: Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis, such as the CISNET CRC models. In this work, we present a step-by-step guide to constructing emulators for calibrating 3 realistic CRC individual-level models using a Bayesian approach., Highlights: We use artificial neural networks (ANNs) to build emulators that surrogate complex individual-based models to reduce the computational burden in the Bayesian calibration process.ANNs showed good performance in emulating the CISNET-CRC microsimulation models, despite having many input parameters and outputs.Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis.This work aims to support health decision scientists who want to quantify the uncertainty of calibrated parameters of computationally intensive simulation models under a Bayesian framework., Competing Interests: The authors have no conflicts of interest to declare.

Despite the availability of direct-acting antivirals that cure individuals infected with the hepatitis C virus (HCV), developing a vaccine is critically needed in achieving HCV elimination. HCV vaccine trials have been performed in populations with high incidence of new HCV infection such as people who inject drugs (PWID). Developing strategies of optimal recruitment of PWID for HCV vaccine trials could reduce sample size, follow-up costs and disparities in enrollment. We investigate trial recruitment informed by machine learning and evaluate a strategy for HCV vaccine trials termed PREDICTEE-Predictive Recruitment and Enrichment method balancing Demographics and Incidence for Clinical Trial Equity and Efficiency. PREDICTEE utilizes a survival analysis model applied to trial candidates, considering their demographic and injection characteristics to predict the candidate's probability of HCV infection during the trial. The decision to recruit considers both the candidate's predicted incidence and demographic characteristics such as age, sex, and race. We evaluated PREDICTEE using in silico methods, in which we first generated a synthetic candidate pool and their respective HCV infection events using HepCEP, a validated agent-based simulation model of HCV transmission among PWID in metropolitan Chicago. We then compared PREDICTEE to conventional recruitment of high-risk PWID who share drugs or injection equipment in terms of sample size and recruitment equity, with the latter measured by participation-to-prevalence ratio (PPR) across age, sex, and race. Comparing conventional recruitment to PREDICTEE found a reduction in sample size from 802 (95%: 642-1010) to 278 (95%: 264-294) with PREDICTEE, while also reducing screening requirements by 30%. Simultaneously, PPR increased from 0.475 (95%: 0.356-0.568) to 0.754 (95%: 0.685-0.834). Even when targeting a dissimilar maximally balanced population in which achieving recruitment equity would be more difficult, PREDICTEE is able to reduce sample size from 802 (95%: 642-1010) to 304 (95%: 288-322) while improving PPR to 0.807 (95%: 0.792-0.821). PREDICTEE presents a promising strategy for HCV clinical trial recruitment, achieving sample size reduction while improving recruitment equity.

Background: Understanding the impact of incarceration on HIV transmission among Black men who have sex with men is important given their disproportionate representation among people experiencing incarceration and the potential impact of incarceration on social and sexual networks, employment, housing, and medical care. We developed an agent-based network model (ABNM) of 10,000 agents representing young Black men who have sex with men in the city of Chicago to examine the impact of varying degrees of post-incarceration care disruption and care engagement interventions following release from jail on HIV incidence., Methods: Exponential random graph models were used to model network formation and dissolution dynamics, and network dynamics and HIV care continuum engagement were varied according to incarceration status. Hypothetical interventions to improve post-release engagement in HIV care for individuals with incarceration (e.g., enhanced case management, linkage to housing and employment services) were compared to a control scenario with no change in HIV care engagement after release., Finding: HIV incidence at 10 years was 4.98 [95% simulation interval (SI): 4.87, 5.09 per 100 person-years (py)] in the model population overall; 5.58 (95% SI 5.38, 5.76 per 100 py) among those with history of incarceration, and 12.86 (95% SI 11.89, 13.73 per 100 py) among partners of agents recently released from incarceration. Sustained post-release HIV care for agents with HIV and experiencing recent incarceration resulted in a 46% reduction in HIV incidence among post-incarceration partners [incidence rate (IR) per 100 py = 5.72 (95% SI 5.19, 6.27) vs. 10.61 (95% SI 10.09, 11.24); incidence rate ratio (IRR) = 0.54; (95% SI 0.48, 0.60)] and a 19% reduction in HIV incidence in the population overall [(IR per 100 py = 3.89 (95% SI 3.81-3.99) vs. 4.83 (95% SI 4.73, 4.92); IRR = 0.81 (95% SI 0.78, 0.83)] compared to a scenario with no change in HIV care engagement from pre-to post-release., Interpretation: Developing effective and scalable interventions to increase HIV care engagement among individuals experiencing recent incarceration and their sexual partners is needed to reduce HIV transmission among Black men who have sex with men., Funding: This work was supported by the following grants from the National Institutes of Health: R01DA039934; P20 GM 130414; P30 AI 042853; P30MH058107; T32 DA 043469; U2C DA050098 and the California HIV/AIDS Research Program: OS17-LA-003; H21PC3466., Competing Interests: The authors have no competing interests to declare. ALH: none, FL: none, DS: none, JO: none, NC: none, ME: none, BMA: none, RB: none, KMS: none, KF: none, NTH: Nina T. Harawa has received honoraria for speaking at events co-sponsored by Gilead Sciences, Inc and for participating in a one-time advisory panel for the company. She is a co-investigator, with in-kind support, on a Gilead Sciences-focused on HIV prevention center and has previously received research support from the company. She was a member of the Scientific Advisory Board for the Black AIDS Institute and has provided expert testimony for potential lawsuits related to COVID-19 in carceral settings., JAS: none, ASK: none., (© 2023 The Author(s).)

Context: Medicaid expansion has been nationally shown to improve engagement in the human immunodeficiency virus (HIV) treatment and prevention continua, which are vital steps to stopping the HIV epidemic. New HIV infections in the United States are disproportionately concentrated among young Black men who have sex with men (YBMSM). Houston, TX, is the most populous city in the Southern United States with a racially/ethnically diverse population that is located in 1 of 11 US states that have not yet expanded Medicaid coverage as of 2021., Methods: An agent-based model that incorporated the sexual networks of YBMSM was used to simulate improved antiretroviral treatment and pre-exposure prophylaxis (PrEP) engagement through Medicaid expansion in Houston, TX. Analyses considered the HIV incidence (number of new infections and as a rate metric) among YBMSM over the next 10 years under Medicaid expansion as the primary outcome. Additional scenarios, involving viral suppression and PrEP uptake above the projected levels achieved under Medicaid expansion, were also simulated., Results: The baseline model projected an HIV incidence rate of 4.96 per 100 person years (py) and about 368 new annual HIV infections in the 10th year. Improved HIV treatment and prevention continua engagement under Medicaid expansion resulted in a 14.9% decline in the number of annual new HIV infections in the 10th year. Increasing viral suppression by an additional 15% and PrEP uptake by 30% resulted in a 44.0% decline in new HIV infections in the 10th year, and a 27.1% decline in cumulative infections across the 10 years of the simulated intervention., Findings: Simulation results indicate that Medicaid expansion has the potential to reduce HIV incidence among YBMSM in Houston. Achieving HIV elimination objectives, however, might require additional effective measures to increase antiretroviral treatment and PrEP uptake beyond the projected improvements under expanded Medicaid., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc.)

The increasing availability of high-performance computing (HPC) has accelerated the potential for applying computational simulation to capture ever more granular features of large, complex systems. This tutorial presents Repast4Py, the newest member of the Repast Suite of agent-based modeling toolkits. Repast4Py is a Python agent-based modeling framework that provides the ability to build large, MPI-distributed agent-based models (ABM) that span multiple processing cores. Simplifying the process of constructing large-scale ABMs, Repast4Py is designed to provide an easier on-ramp for researchers from diverse scientific communities to apply distributed ABM methods. We will present key Repast4Py components and how they are combined to create distributed simulations of different types, building on three example models that implement seven common distributed ABM use cases. We seek to illustrate the relationship between model structure and performance considerations, providing guidance on how to leverage Repast4Py features to develop well designed and performant distributed ABMs.

Purpose: Even with an efficacious vaccine, protective behaviors (social distancing, masking) are essential for preventing COVID-19 transmission and could become even more important if current or future variants evade immunity from vaccines or prior infection., Methods: We created an agent-based model representing the Chicago population and conducted experiments to determine the effects of varying adult out-of-household activities (OOHA), school reopening, and protective behaviors across age groups on COVID-19 transmission and hospitalizations., Results: From September-November 2020, decreasing adult protective behaviors and increasing adult OOHA both substantially impacted COVID-19 outcomes; school reopening had relatively little impact when adult protective behaviors and OOHA were maintained. As of November 1, 2020, a 50% reduction in young adult (age 18-40) protective behaviors resulted in increased latent infection prevalence per 100,000 from 15.93 (IQR 6.18, 36.23) to 40.06 (IQR 14.65, 85.21) and 19.87 (IQR 6.83, 46.83) to 47.74 (IQR 18.89, 118.77) with 15% and 45% school reopening. Increasing adult (age ≥18) OOHA from 65% to 80% of prepandemic levels resulted in increased latent infection prevalence per 100,000 from 35.18 (IQR 13.59, 75.00) to 69.84 (IQR 33.27, 145.89) and 38.17 (IQR 15.84, 91.16) to 80.02 (IQR 30.91, 186.63) with 15% and 45% school reopening. Similar patterns were observed for hospitalizations., Conclusions: In areas without widespread vaccination coverage, interventions to maintain adherence to protective behaviors, particularly among younger adults and in out-of-household settings, remain a priority for preventing COVID-19 transmission., (Copyright © 2022 Elsevier Inc. All rights reserved.)

The coronavirus disease 2019 pandemic required significant public health interventions from local governments. Early in the pandemic, RAND researchers developed a decision support tool to provide policymakers with insight into the trade-offs they might face when choosing among nonpharmaceutical intervention levels. Using an updated version of the model, the researchers performed a stress-test of a variety of alternative reopening plans, using California as an example. This article presents the general lessons learned from these experiments and discusses four characteristics of the best reopening strategies., (Copyright © 2022 RAND Corporation.)

Objectives: We examined whether molecular cluster membership was associated with public health identification of HIV transmission potential among named partners in Chicago., Design: Historical cohort study., Methods: We matched and analyzed HIV surveillance and partner services data from HIV diagnoses (2012-2016) prior to implementation of cluster detection and response interventions. We constructed molecular clusters using HIV-TRACE at a pairwise genetic distance threshold of 0.5% and identified clusters exhibiting recent and rapid growth according to the Centers for Disease Control and Prevention definition (three new cases diagnosed in past year). Factors associated with identification of partners with HIV transmission potential were examined using multivariable Poisson regression., Results: There were 5208 newly diagnosed index clients over this time period. Average age of index clients in clusters was 28; 47% were Black, 29% Latinx/Hispanic, 6% female and 89% MSM. Of the 537 named partners, 191 (35.6%) were linked to index cases in a cluster and of those 16% were either new diagnoses or viremic. There was no statistically significant difference in the probability of identifying partners with HIV transmission potential among index clients in a rapidly growing cluster versus those not in a cluster [adjusted relative risk 1.82, (0.81-4.06)]., Conclusion: Partner services that were initiated from index clients in a molecular cluster yielded similar new HIV case finding or identification of those with viremia as did interviews with index clients not in clusters. It remains unclear whether these findings are due to temporal disconnects between diagnoses and cluster identification, unobserved cluster members, or challenges with partner services implementation., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Microsimulation models are mathematical models that simulate event histories for individual members of a population. They are useful for policy decisions because they simulate a large number of individuals from an idealized population, with features that change over time, and the resulting event histories can be summarized to describe key population-level outcomes. Model calibration is the process of incorporating evidence into the model. Calibrated models can be used to make predictions about population trends in disease outcomes and effectiveness of interventions, but calibration can be challenging and computationally expensive., Methods: This paper develops a technique for sequentially updating models to take full advantage of earlier calibration results, to ultimately speed up the calibration process. A Bayesian approach to calibration is used because it combines different sources of evidence and enables uncertainty quantification which is appealing for decision-making. We develop this method in order to re-calibrate a microsimulation model for the natural history of colorectal cancer to include new targets that better inform the time from initiation of preclinical cancer to presentation with clinical cancer (sojourn time), because model exploration and validation revealed that more information was needed on sojourn time, and that the predicted percentage of patients with cancers detected via colonoscopy screening was too low., Results: The sequential approach to calibration was more efficient than recalibrating the model from scratch. Incorporating new information on the percentage of patients with cancers detected upon screening changed the estimated sojourn time parameters significantly, increasing the estimated mean sojourn time for cancers in the colon and rectum, providing results with more validity., Conclusions: A sequential approach to recalibration can be used to efficiently recalibrate a microsimulation model when new information becomes available that requires the original targets to be supplemented with additional targets., (© 2022. The Author(s).)

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.

Objectives: Getting to Zero (GTZ) initiatives focus on expanding use of antiretroviral treatment (ART) and pre-exposure prophylaxis (PrEP) to eliminate new HIV infections. Computational models help inform policies for implementation of ART and PrEP continuums. Such models, however, vary in their design, and may yield inconsistent predictions. Using multiple approaches can help assess the consistency in results obtained from varied modeling frameworks, and can inform optimal implementation strategies., Methods: A study using three different modeling approaches is conducted. Two approaches use statistical time series analysis techniques that incorporate temporal HIV incidence data. A third approach uses stochastic stimulation, conducted using an agent-based network model (ABNM). All three approaches are used to project HIV incidence among a key population, young Black MSM (YBMSM), over the course of the GTZ implementation period (2016-2030)., Results: All three approaches suggest that simultaneously increasing PrEP and ART uptake is likely to be more effective than increasing only one, but increasing ART and PrEP by 20% points may not eliminate new HIV infections among YBMSM. The results further suggest that a 20% increase in ART is likely to be more effective than a 20% increase in PrEP. All three methods consistently project that increasing ART and PrEP by 30% simultaneously can help reach GTZ goals., Conclusions: Increasing PrEP and ART uptake by about 30% might be necessary to accomplish GTZ goals. Such scale-up may require addressing psychosocial and structural barriers to engagement in HIV and PrEP care continuums. ABNMs and other flexible modeling approaches can be extended to examine specific interventions that address these barriers and may provide important data to guide the successful intervention implementation.