Jonathan Ozik

Mechanisms that lead to viral chronicity are poorly understood, but cell-to-cell spread has been implicated in the establishment of chronic infections. We previously developed mathematical models to explore the nature of hepatitis C virus (HCV) cell-to-cell spread in vitro and quantified the effect of inhibiting individual host factors involved. However, the previous models were not designed to (i) address cell proliferation, (ii) account for differences in cell size, and (iii) did not include possible foci merging. Herein we have developed an agent-based model (ABM) to simulate HCV cell-to-cell spread in vitro by modeling individual cell behaviors. This model recapitulates the natural increase of cell confluence that occurs in vitro accompanied by a concomitant decrease in cell size by allowing for independent proliferation cycles of individual cells within a restricted space. The model fits the experimental foci expansion data well and allows assessment of foci merging while reproducing the irregular HCV foci shape observed in cell culture. Altogether, the new more inclusive model has the potential to help elucidate the dynamics of HCV cell-to-cell spread and provide accurate predictions regarding the efficacy of antiviral drugs. Author Summary: Despite remarkable progress in treatments for hepatitis C virus (HCV), HCV infection remains a global public health burden with over 50 million chronic HCV infections and about 1 million new infections occurring annually. Once infection occurs, HCV can spread in the liver multiple ways. One mechanism is cell-to-cell (CTC) spread where the virus moves directly from one infected cell to an adjacent cell without moving through the extracellular space. Previous mathematical models of HCV CTC spread were not designed to incorporate cell proliferation, cell size, or foci merging. Therefore, to better mimic cells in culture, we developed a novel agent-based model (ABM) that allows the simulated cells to proliferate resulting in an increased number of cells with concomitant decrease in cell/agent size analogous to what happens as cells become tightly packed in culture. This new ABM not only can be used to estimate efficacy values of HCV cell-to-cell spread inhibitors (e.g., when different factors involved in cell-to-cell spread are blocked), but also should enable modeling of HCV CTC spread under a wider variety of cell culture conditions and thus help elucidate the impact of different viral-host dynamics on HCV CTC spread.

Background: People who inject drugs (PWIDs) are at high risk for acquiring and transmitting hepatitis C virus (HCV). Direct-acting antiviral (DAA) therapy leads to high cure rates. However, lack of protective immunity after cure and high re-infection rates in PWID necessitates access to multiple DAA treatments per PWID to reach the World Health Organization HCV elimination goal of 90% incidence reduction. A major public health challenge is determining the proportion of PWID to treat to reach the goal and the continued treatment levels required to maintain decreased incidence. Method: We used an agent-based model to study varying levels of DAA treatment among HCV-infected PWID from Chicago, IL, and the effects of reducing DAA treatment after elimination is reached. The model uses individual temporal viral load profiles to determine transmission probabilities relative to the HCV RNA titers of receptive syringe-sharing PWID. Results: Modeling predicts that insufficient DAA treatment can result in a continuous increase of HCV incidence over a 25-year period, with no significant decrease. Elimination can be achieved within 10 years with sufficient treatment levels. However, when DAA treatment is stopped, rates of new chronic HCV infections rapidly increase, returning to preelimination levels within 5 years. Annual treatment of ≥0.5% would maintain the elimination goal, but identifying and treating small numbers of infected people annually in PWID populations would be resource intensive. Conclusions: The challenge to achieve and maintain the elimination goal underscores the importance of augmenting DAA treatment with intervention strategies such as syringe service programs and vaccines.

Detailed agent-based simulations are increasingly used to support policy decisions, but their computational cost and complex uncertainty structure make systematic scenario analysis challenging. We present a data-driven, uncertainty-aware decision support (DDUADS) workflow for using stochastic simulation models as decision-support tools under limited computational budgets. The approach combines several established techniques—sensitivity screening, Bayesian calibration using simulation-based inference, and multi-surrogate model integration for translational efficiency—into a coherent pipeline that enables uncertainty-aware policy analysis. Rather than producing a single baseline, the calibration stage yields a posterior distribution over plausible model parameterizations, allowing flexible, uncertainty-aware forward projections. We demonstrate the DDUADS workflow on the INFORM-HIV agent-based model of HIV transmission in Chicago to evaluate potential disruptions in antiretroviral therapy (ART) and pre-exposure prophylaxis (PrEP) use. While the specific application is HIV modeling, the challenges and techniques described here arise in other simulation studies and can be applied to decision support in other domains.

Background: Pediatric asthma exacerbations remain a critical public health concern, particularly in historically underserved urban settings. Objective: This study investigates sociome factors—the social context of disease—associated with asthma exacerbations among children living in Chicago's South Side, leveraging clinical and publicly available generalizable census tract‐level datasets from agencies including ChiVes, the City of Chicago Data Portal, EPA, Census Bureau, HUD, NOAA, and more. The aim is to uncover novel hypotheses for potential new interventions. Methods: A generalized linear model assessed associations with the outcome of asthma exacerbations while accounting for clustering at the patient level. Predictors included all variables from the Sociome Data Commons, including social, environmental, behavioral, economic, housing, and school variables. Results: Predictors of decreased risk included patient age (+4.8 years, −22%), tree crown density (+6% coverage, −17%), parks per acre (+0.41, −8%), and labor market engagement (+0.8 points, −9%). Conversely, predictors of increased risk included increased distance to the nearest pharmacy (+0.28 miles, +12%), limited English skills (+2.3%, +10%), higher inequality (+0.08 points, +8%), and visits in the Spring (+11%) and Fall (+20%). Conclusion: The results suggest that tree crown density, a novel finding in the context of asthma exacerbations, may play a protective role. Limited access to health care facilities such as pharmacies continues to complicate care. Clinical Implications: These findings provide hypotheses for future interventions for long‐standing asthma disparities.

Background: Fecal immunochemical test (FIT) performance for colorectal cancer screening varies by age and sex, yet most FIT-based screening programs use uniform positivity thresholds. This study assessed the potential benefits of stratifying FIT thresholds based on age and sex. Methods: We conducted a meta-analysis of FIT sensitivity and specificity at various positivity thresholds by age and sex. We then used these estimates in 2 microsimulation models of colorectal cancer and projected lifetime clinical outcomes, incremental costs, and quality-adjusted life-years (QALYs) gained from age- and sex-stratified FIT strategies. FIT thresholds ranged from 10 to 50 µg hemoglobin per gram of feces. Results: For current uniform FIT screening (20 µg hemoglobin/gram of feces), models projected 85.67 to 122.15 QALYs gained at incremental costs of ‒$982 to $504 per 1000 individuals compared with no screening. At equivalent costs to current uniform screening, only 1 model found stratified FIT approaches cost-effective, yielding a marginal increase of 1.04 and 1.10 QALYs gained/1000 female and male individuals, respectively. At a willingness-to-pay threshold of $100 000/QALYs gained, both models found stratified FIT cutoffs to be the best strategy, with cutoffs being equal to or higher for males and lowest at older ages (70-75 years). Uniform strategies showed comparable effectiveness, falling within 1 quality-adjusted life-day per person of efficient strategies at up to $112 more per person. Results were sensitive to FIT test performance characteristics and 1-time setup costs. Conclusion: Stratifying FIT thresholds by age and sex may be cost-effective compared to current screening. The gain in expected health benefits with stratified FIT screening, however, is likely small.

Public health experts studying infectious disease spread often seek granular insights into population health outcomes. Metapopulation models offer an effective framework for analyzing disease transmission through subpopulation mixing. These models strike a balance between traditional, homogeneous mixing compartmental models and granular but computationally intensive agent-based models. In collaboration with the Chicago Department of Public Health (CDPH), we developed MetaRVM , an open-source R package for modeling the spread of infectious diseases in subpop-ulations, which can be flexibly defined by geography, demographics, or other stratifications. MetaRVM is designed to support real-time public health decision-making and through its co-creation with CDPH, we have ensured that it is re-sponsive to real-world needs. We demonstrate its flexible capabilities by tracking influenza dynamics within different age groups in Chicago, by integrating an efficient Bayesian optimization-based calibration approach.

Purpose: The 2023 American College of Physicians (ACP) guidelines for colorectal cancer (CRC) screening are at odds with the United States Preventive Task Force (USPSTF) guidelines, with the former recommending screening starting at age 50 y and the latter at age 45 y. This article “stress tests” CRC colonoscopy screening strategies to investigate their robustness to uncertainties stemming from the natural history of disease and sensitivity of colonoscopy. Methods: This study uses the CRC-SPIN microsimulation model to project the life-years gained (LYG) under several colonoscopy CRC screening strategies. The model was extended to include birth cohort effects on adenoma risk. We estimated natural history parameters under 2 different assumptions about the youngest age of adenoma initiation. For each, we generated 500 parameter sets to reflect uncertainty in the natural history parameters. We simulated 26 colonoscopy screening strategies and examined 4 different colonoscopy sensitivity assumptions, encompassing the range of sensitivities consistent with prior tandem colonoscopy studies. Across this set of scenarios, we identify efficient screening strategies and report posterior credible intervals for benefits of screening (LYG), burden (number of colonoscopies), and incremental burden-effectiveness ratios. Results: Projected absolute screening benefits varied widely based on assumptions, but strategies starting at age 45 y were consistently in the efficiency frontier. Strategies in which screening starts at age 50 y with 10-y intervals were never efficient, saving fewer life-years than starting screening at age 45 y and performing colonoscopies every 15 y while requiring more colonoscopies per person. Conclusions: Decennial colonoscopy screening initiation at age 45 y remained a robust recommendation. Colonoscopy screening with a 10-y interval starting at age 50 y did not result in an efficient use of colonoscopies in any of the scenarios evaluated. Highlights: Colorectal cancer colonoscopy screening strategies initiated at age 45 y were projected to yield more life-years gained while requiring the least number of colonoscopies across different model assumptions about disease natural history and colonoscopy sensitivity. Colonoscopy screening starting at age 50 y with a 10-y interval consistently underperformed strategies that started at age 45 y.

One way to reduce the time of conducting optimization studies is to evaluate designs in parallel rather than just one-at-a-time. For expensive-to-evaluate black-boxes, batch versions of Bayesian optimization have been proposed. They work by building a surrogate model of the black-box to simultaneously select multiple designs via an infill criterion. Still, despite the increased availability of computing resources that enable large-scale parallelism, the strategies that work for selecting a few tens of parallel designs for evaluations become limiting due to the complexity of selecting more designs. It is even more crucial when the black-box is noisy, necessitating more evaluations as well as repeating experiments. Here we propose a scalable strategy that can keep up with massive batching natively, focused on the exploration/exploitation trade-off and a portfolio allocation. We compare the approach with related methods on noisy functions, for mono and multi-objective optimization tasks. These experiments show orders of magnitude speed improvements over existing methods with similar or better performance.