Towards Precision Prevention: Using Agent-based Modeling in Population Health

Ross Hammond, PhD
Washington University St. Louis

ABSTRACT:
In this presentation, Dr. Hammond will give an overview of agent-based modeling (ABM), an important systems science method, and its uses in population health. The presentation will focus on the applications of ABM to inform design and delivery of disease-prevention interventions. He will draw on his current and recent research including work on obesity prevention, tobacco control, and pandemic containment.

High-Fidelity Agent-Based Modeling to Support Prevention Decision-making

Wouter Vermeer, PhD
Northwestern University

ABSTRACT:
Preventing adverse health outcomes is complex due to the multilevel contexts and social systems in which these phenomena occur. To capture both the systemic effects, local determinants, and individual-level risks and protective factors simultaneously, the prevention field has called for adoption of system science methods in general, and agent-based models (ABMs) specifically. While these models can provide unique and timely insight into the potential of prevention strategies, an ABM’s ability to do so depends strongly on its accuracy in capturing the phenomenon. What is more, to support what we call model-based decision-making, these models they need to be accepted by and available to decision-makers and other stakeholders. In this presentation we will present a set of recommendations for adopting and using this novel method. We recommend ways to include stakeholders throughout the modeling process, as well as ways to conduct model verification, validation, and replication.

Agent-based Modeling of COVID-19 to Support Public Health Decision Making

Jonathan Ozik, Ph.D.
The University of Chicago

Anna Hotton, Ph.D., MPH, BS
The University of Chicago

ABSTRACT:
The COVID-19 pandemic has highlighted the need for detailed modeling approaches that can capture the myriad complexities of emerging infectious diseases. In response, our group has developed CityCOVID, an agent-based model capable of tracking COVID-19 transmission in large, urban areas. Through partnerships between Argonne National Laboratory, the University of Chicago, the Chicago Department of Public Health, and the Illinois COVID-19 Modeling Task Force we combined multiple data sources to develop a locally informed, realistic, and statistically representative synthetic agent population, with attributes and processes that reflect real-world social and biomedical aspects of transmission. We model all 2.7 million individual residents of Chicago, as they go to and from 1.2 million different places according to their individual hourly schedules. The places include locations such as households, workplaces, schools, and hospitals, and, as individuals congregate with other individuals in these places over the course of their daily routines, they are exposed to potential infection from other infectious people who are also at those places. Transitions between disease states depend on agent attributes and exposure to infected individuals, placed-based risks, and protective behaviors. This detailed modeling approach allows us to implement very specific and realistic mitigation strategies that are being considered by stakeholders, and which have been evolving over the course of the pandemic. We continue to apply CityCOVID to examine the impact of non-pharmaceutical interventions, SARS-CoV-2 variants of concern, vaccination deployment strategies, and to understand the impacts of social determinants of health on disease outcomes. In this presentation we will describe CityCOVID, including how the synthetic population was developed, what agent-based modeling and high-performance computing technologies were required, and our efforts in supporting local public health stakeholders in understanding, responding to and planning for the current and future population health emergencies.

Ending the HIV epidemic in Chicago: Evidence from high-fidelity local agent-based model

Wouter Vermeer, PhD
Northwestern University

Nanette Benbow, M.A.S.
Northwestern University

ABSTRACT:
Agent-based models have enormous potential for modeling complex social phenomena. The ability to model complex individual-level social dynamics and project system-wide behaviors can help inform decision makers in their effort to curb a phenomenon like the spread of HIV. To produce actionable results, models need to accurately capture the dynamics and behaviors observed by decision makers. As such, models aimed at supporting decision making need to be tailored to the local context by incorporating behaviors based on local data. In this presentation we describe our model for HIV-spread in Chicago and highlight how we used Chicago-level data and input from local public health experts to inform this model. We will illustrate how our model can be used to perform predictive scenario analysis and discuss how these results can be used to inform decision makers as they plan their HIV care and prevention strategies to end the HIV epidemic.

Localized Economic Modeling to Support Implementation of the “Ending the HIV Epidemic in America” Initiative

Bohdan Nosyk, PhD, MA
Simon Fraser University

ABSTRACT:
Rather than a homogeneous national epidemic, the HIV epidemic in the US is a collection of diverse local microepidemics concentrated primarily in the South, hotspot counties, and large urban centers, with fundamental differences in health system infrastructure, funding and HIV-related laws and policies. Recognizing these facts, the US launched the ambitious ‘Ending the HIV Epidemic (EHE) initiative in February 2019. The plan called for an initial focus on 48 of the most-affected counties plus Washington, DC, San Juan, Puerto Rico and seven southern states to reduce new infections by 75% within 5 years and by 90% within 10 years. These goals are now challenged by the onset of the global COVID-19 pandemic, which may have severe consequences for people living with HIV and on HIV microepidemics across the US. A value-based approach, accounting for the pervasive racial/ethnic inequities in healthcare access and explicating key elements of the implementation process are now more critical than ever in reaching the ambitious targets of the EHE initiative.

In 2016 our investigative team began a project aiming to identify optimal combination implementation strategies to reduce the public health burden of HIV/AIDS in six US cities (NIH-DA-041747). These six cities, all subsequently included in the EHE initiative, comprised 12 of 48 EHE-targeted counties and 24.1% of people living with HIV/AIDS in the nation. Considering the impact of 16 evidence-based interventions to Diagnose, Treat and Protect against HIV/AIDS, we found unique combination implementation strategies provided the greatest health benefits in each city; no two cities featured the same mix of interventions in their ‘optimal’ strategy. Moreover, we found the EHE goals were attainable in three of six cities. The biomedical interventions we considered would however have to be delivered at ideal levels of implementation, which would require additional efforts to reduce barriers in access to care and explicitly focus on reducing disparities in healthcare access among Black and Hispanic communities. We argue that promoting health equity is key to bridging this implementation gap and propose an approach to establish an equitable distribution of resources to maximize the impact of the EHE initiative.

Agent-based models for understanding the impact of transitions between community and criminal justice settings on HIV transmission and opioid mortality: implications for intervention development

Anna Hotton, PhD, MPH
John Schneider, MD, MPH
University of Chicago Medicine

ABSTRACT:
Criminal justice involvement (CJI) has important public health and social consequences, affecting social and sexual network stability, employment and housing opportunities, and access to medical care, all of which can lead to cycles of socioeconomic marginalization and adverse health outcomes. CJI populations are disproportionately impacted by HIV and substance use disorders, which can be exacerbated by frequent cycling between communities and criminal justice settings. However, such settings also offer opportunities for delivery of treatment and prevention interventions, such as PrEP, ART, and medication assisted therapy to populations who may not otherwise access these services. Guidance is needed to determine how interventions for CJI populations can be most effectively deployed, but logistical and ethnical challenges make empirical research difficult in contexts that often include marginalized communities that are highly mobile, have significant loss to follow-up, and cycle frequently between criminal justice and community settings. Agent-based models (ABMs) can generate insights about the processes that drive HIV transmission and opioid related mortality and provide a platform for virtually evaluating potential candidate interventions, thus facilitating more efficient and focused intervention development. By illuminating mechanisms associated with intervention success and providing the ability to parameterize the relevant individual-level heterogeneity via detailed, local data, ABMs allow for exploration of complex interventions, enabling the investigation of specific intervention ingredients and mechanisms likely to have the most impact on the HIV and opioid epidemics in the US. We present early applications of ABMs for evaluating interventions for CJI populations with nascent examples in HIV and opioid mortality, and discuss implications for structural, policy, and network-based interventions.