Erratum: Sociome Data Commons: A scalable and sustainable platform for investigating the full social context and determinants of health - ERRATUM.

[This corrects the article DOI: 10.1017/cts.2023.670.].

Reducing Sample Size While Improving Equity in Vaccine Clinical Trials: A Machine Learning-Based Recruitment Methodology with Application to Improving Trials of Hepatitis C Virus Vaccines in People Who Inject Drugs.

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.

Emulator-based Bayesian calibration of the CISNET colorectal cancer models.

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 (ANN) 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 four hidden layers and 360 hidden nodes, MISCAN-Colon had 4 hidden layers and 114 hidden nodes, and CRC-SPIN had one hidden layer and 140 hidden nodes. The total time for training and calibrating the emulators was 7.3, 4.0, and 0.66 hours 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, like the CISNET CRC models. In this work, we present a step-by-step guide to constructing emulators for calibrating three realistic CRC individual-level models using a Bayesian approach.

Impact of post-incarceration care engagement interventions on HIV transmission among young Black men who have sex with men and their sexual partners: an agent-based network modeling study.

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.

Modeling suggests that virion production cycles within individual cells is key to understanding acute hepatitis B virus infection kinetics.

Hepatitis B virus (HBV) infection kinetics in immunodeficient mice reconstituted with humanized livers from inoculation to steady state is highly dynamic despite the absence of an adaptive immune response. To recapitulate the multiphasic viral kinetic patterns, we developed an agent-based model that includes intracellular virion production cycles reflecting the cyclic nature of each individual virus lifecycle. The model fits the data well predicting an increase in production cycles initially starting with a long production cycle of 1 virion per 20 hours that gradually reaches 1 virion per hour after approximately 3-4 days before virion production increases dramatically to reach to a steady state rate of 4 virions per hour per cell. Together, modeling suggests that it is the cyclic nature of the virus lifecycle combined with an initial slow but increasing rate of HBV production from each cell that plays a role in generating the observed multiphasic HBV kinetic patterns in humanized mice.

Spatial inequities in access to medications for treatment of opioid use disorder highlight scarcity of methadone providers under counterfactual scenarios.

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.

Projected long-term effects of colorectal cancer screening disruptions following the COVID-19 pandemic.

The aftermath of the initial phase of the COVID-19 pandemic may contribute to the widening of disparities in colorectal cancer (CRC) outcomes due to differential disruptions to CRC screening. This comparative microsimulation analysis uses two CISNET CRC models to simulate the impact of ongoing screening disruptions induced by the COVID-19 pandemic on long-term CRC outcomes. We evaluate three channels through which screening was disrupted: delays in screening, regimen switching, and screening discontinuation. The impact of these disruptions on long-term CRC outcomes was measured by the number of life-years lost due to CRC screening disruptions compared to a scenario without any disruptions. While short-term delays in screening of 3-18 months are predicted to result in minor life-years loss, discontinuing screening could result in much more significant reductions in the expected benefits of screening. These results demonstrate that unequal recovery of screening following the pandemic can widen disparities in CRC outcomes and emphasize the importance of ensuring equitable recovery to screening following the pandemic.

Robustness Analysis of Colorectal Cancer Colonoscopy Screening Strategies.

Colorectal Cancer (CRC) is a leading cause of cancer deaths in the United States. Despite significant overall declines in CRC incidence and mortality, there has been an alarming increase in CRC among people younger than 50. This study uses an established microsimulation model, CRC-SPIN, to perform a 'stress test' of colonoscopy screening strategies. First, we expand CRC-SPIN to include birth-cohort effects. Second, we estimate natural history model parameters via Incremental Mixture Approximate Bayesian Computation (IMABC) for two model versions to characterize uncertainty while accounting for increased early CRC onset. Third, we simulate 26 colonoscopy screening strategies across the posterior distribution of estimated model parameters, assuming four different colonoscopy sensitivities (104 total scenarios). We find that model projections of screening benefit are highly dependent on natural history and test sensitivity assumptions, but in this stress test, the policy recommendations are robust to the uncertainties considered.

Expanding Medicaid to Reduce Human Immunodeficiency Virus Transmission in Houston, Texas: Insights From a Modeling Study.

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.

Sociome Data Commons: A scalable and sustainable platform for investigating the full social context and determinants of health.

Background/Objective: Non-clinical aspects of life, such as social, environmental, behavioral, psychological, and economic factors, what we call the sociome, play significant roles in shaping patient health and health outcomes. This paper introduces the Sociome Data Commons (SDC), a new research platform that enables large-scale data analysis for investigating such factors. Methods: This platform focuses on "hyper-local" data, i.e., at the neighborhood or point level, a geospatial scale of data not adequately considered in existing tools and projects. We enumerate key insights gained regarding data quality standards, data governance, and organizational structure for long-term project sustainability. A pilot use case investigating sociome factors associated with asthma exacerbations in children residing on the South Side of Chicago used machine learning and six SDC datasets. Results: The pilot use case reveals one dominant spatial cluster for asthma exacerbations and important roles of housing conditions and cost, proximity to Superfund pollution sites, urban flooding, violent crime, lack of insurance, and a poverty index. Conclusion: The SDC has been purposefully designed to support and encourage extension of the platform into new data sets as well as the continued development, refinement, and adoption of standards for dataset quality, dataset inclusion, metadata annotation, and data access/governance. The asthma pilot has served as the first driver use case and demonstrates promise for future investigation into the sociome and clinical outcomes. Additional projects will be selected, in part for their ability to exercise and grow the capacity of the SDC to meet its ambitious goals.

Reopening Under Uncertainty: Stress-Testing California's COVID-19 Exit Strategy.

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.

Impact of changes in protective behaviors and out-of-household activities by age on COVID-19 transmission and hospitalization in Chicago, Illinois.

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.

Characterization and Valuation of the Uncertainty of Calibrated Parameters in Microsimulation Decision Models.

Background: We evaluated the implications of different approaches to characterize the uncertainty of calibrated parameters of microsimulation decision models (DMs) and quantified the value of such uncertainty in decision making. Methods: We calibrated the natural history model of CRC to simulated epidemiological data with different degrees of uncertainty and obtained the joint posterior distribution of the parameters using a Bayesian approach. We conducted a probabilistic sensitivity analysis (PSA) on all the model parameters with different characterizations of the uncertainty of the calibrated parameters. We estimated the value of uncertainty of the various characterizations with a value of information analysis. We conducted all analyses using high-performance computing resources running the Extreme-scale Model Exploration with Swift (EMEWS) framework. Results: The posterior distribution had a high correlation among some parameters. The parameters of the Weibull hazard function for the age of onset of adenomas had the highest posterior correlation of -0.958. When comparing full posterior distributions and the maximum-a-posteriori estimate of the calibrated parameters, there is little difference in the spread of the distribution of the CEA outcomes with a similar expected value of perfect information (EVPI) of $653 and $685, respectively, at a willingness-to-pay (WTP) threshold of $66,000 per quality-adjusted life year (QALY). Ignoring correlation on the calibrated parameters' posterior distribution produced the broadest distribution of CEA outcomes and the highest EVPI of $809 at the same WTP threshold. Conclusion: Different characterizations of the uncertainty of calibrated parameters affect the expected value of eliminating parametric uncertainty on the CEA. Ignoring inherent correlation among calibrated parameters on a PSA overestimates the value of uncertainty.

Modeling hepatitis C micro-elimination among people who inject drugs with direct-acting antivirals in metropolitan Chicago.

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.

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.

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.

Sequentially calibrating a Bayesian microsimulation model to incorporate new information and assumptions.

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.

Unequal Recovery in Colorectal Cancer Screening Following the COVID-19 Pandemic: A Comparative Microsimulation Analysis.

The aftermath of the initial phase of the COVID-19 pandemic may contribute to the widening of disparities in access to colorectal cancer (CRC) screening due to differential disruptions to CRC screening. This comparative microsimulation analysis uses two CISNET CRC models to simulate the impact of ongoing screening disruptions induced by the COVID-19 pandemic on long-term CRC outcomes. We evaluate three channels through which screening was disrupted: delays in screening, regimen switching, and screening discontinuation. The impact of these disruptions on long-term colorectal cancer (CRC) outcomes was measured by the number of Life-years lost due to CRC screening disruptions compared to a scenario without any disruptions. While short-term delays in screening of 3-18 months are predicted to result in minor life-years loss, discontinuing screening could result in much more significant reductions in the expected benefits of screening. These results demonstrate that unequal recovery of screening following the pandemic can widen disparities in colorectal cancer outcomes and emphasize the importance of ensuring equitable recovery to screening following the pandemic.

Too Good to Be True? Evaluation of Colonoscopy Sensitivity Assumptions Used in Policy Models.

BACKGROUND: Models can help guide colorectal cancer screening policy. Although models are carefully calibrated and validated, there is less scrutiny of assumptions about test performance. METHODS: We examined the validity of the CRC-SPIN model and colonoscopy sensitivity assumptions. Standard sensitivity assumptions, consistent with published decision analyses, assume sensitivity equal to 0.75 for diminutive adenomas (<6 mm), 0.85 for small adenomas (6-10 mm), 0.95 for large adenomas (≥10 mm), and 0.95 for preclinical cancer. We also selected adenoma sensitivity that resulted in more accurate predictions. Targets were drawn from the Wheat Bran Fiber study. We examined how well the model predicted outcomes measured over a three-year follow-up period, including the number of adenomas detected, the size of the largest adenoma detected, and incident colorectal cancer. RESULTS: Using standard sensitivity assumptions, the model predicted adenoma prevalence that was too low (42.5% versus 48.9% observed, with 95% confidence interval 45.3%-50.7%) and detection of too few large adenomas (5.1% versus 14.% observed, with 95% confidence interval 11.8%-17.4%). Predictions were close to targets when we set sensitivities to 0.20 for diminutive adenomas, 0.60 for small adenomas, 0.80 for 10- to 20-mm adenomas, and 0.98 for adenomas 20 mm and larger. CONCLUSIONS: Colonoscopy may be less accurate than currently assumed, especially for diminutive adenomas. Alternatively, the CRC-SPIN model may not accurately simulate onset and progression of adenomas in higher-risk populations. IMPACT: Misspecification of either colonoscopy sensitivity or disease progression in high-risk populations may affect the predicted effectiveness of colorectal cancer screening. When possible, decision analyses used to inform policy should address these uncertainties.See related commentary by Etzioni and Lange, p. 702.

Do partner services linked to molecular clusters yield people with viremia or new HIV?

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.

DISTRIBUTED AGENT-BASED SIMULATION WITH REPAST4PY.

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.