From Research to Policy Impact: BU’s Allison Portnoy Models RSV Prevention From Newborns to the Elderly
With NIH support, Portnoy is building the first dynamic model to guide country-specific RSV prevention strategies across all ages
With flu season upon us, respiratory syncytial virus (RSV) cases are already rising. A leading cause of hospitalization in infants and older adults, RSV sends tens of thousands of people to the hospital each year. In response, the FDA fast-tracked three new prevention tools between 2023 and 2025: an infant monoclonal antibody, a maternal vaccine, and an adult vaccine. With multiple interventions targeting diverse age groups, public health officials now face critical decisions about who to target, when, and how to deploy these tools effectively.
Allison Portnoy, Assistant Professor of Global Health at the Boston University School of Public Health and Hariri Institute Junior Faculty Fellow, was awarded a five-year NIH Mentored Research Scientist Development Award (K01) from the National Institute of Allergy and Infectious Diseases (NIAID). Her project will provide a country-specific framework to dynamically track RSV risk, transmission, and prevention efficacy across all age groups.
The project builds on Portnoy’s previous research work: the first systematic review of RSV mathematical models in Low- and Middle-Income Countries (LMICs), where over 97% of RSV-related global pediatric deaths occur. That study highlighted the need for robust models incorporating local factors such as seasonal patterns, social contact rates, and immunity dynamics to generate accurate, policy-relevant assessments.
In the new NIH study, Portnoy will apply these insights to develop a sophisticated simulation framework examining the impact of multiple RSV prevention strategies on the burden of RSV-attributable illness in U.S. households. The framework is designed to be flexibly adapted for evaluating RSV risk in other countries and for future RSV prevention tools.
“RSV affects more than just infants—we need to understand transmission and prevention among pediatric, maternal, and older adult target age groups jointly,” says Portnoy. “Mathematical modeling can help policymakers deploy the most effective, cost-efficient interventions where they’re needed most, capturing diverse settings and scenarios that clinical trials alone cannot address.”
Innovating for Impact
Portnoy is recognized as an innovator in quantitative approaches that improve population health and the data supporting it. Connecting public health policy, health equity, and quantitative methods, her research examines the impact of vaccination on population health and economic outcomes to provide actionable insights for policy makers. Her prior work includes developing large-scale predictive models of infectious disease transmission and conducting policy analyses for measles, tuberculosis, and human papillomavirus (HPV).
“My goal has always been about the research to policy pathway, having my research impact policy decisions and helping people,” says Portnoy. “I use simulation modeling with an evaluative lens to ask: What are the health and economic consequences of choosing one policy over another?”
Her research collaborators include global organizations such as the Vaccine Impact Modeling Consortium (VIMC), the World Health Organization (WHO), UNICEF, and the Bill & Melinda Gates Foundation. With a background in economics, global policy, and public health, Portnoy began this research as a fellow at Harvard University’s Center for Health Decision Science, where she remains a faculty affiliate. In July 2023, she was appointed full-time faculty at Boston University School of Public Health, and in May 2025, she was awarded a Hariri Institute Junior Faculty Fellowship in recognition of her innovative, high-impact research.
“Dr. Portnoy’s work represents a significant advancement in the use of computational research to address global public health and social good,” said Patricia Hibberd, MD, PhD, Chair of Global Health at Boston University SPH. “By integrating computational and data science methods with public health priorities, her work provides a rigorous framework for optimizing strategies against some of the world’s most challenging diseases.”
Modeling Fair and Cost-Effective RSV Prevention Across All Ages
RSV is a complex public health challenge: it’s highly contagious, can cause severe illness and long-term consequences across multiple age groups, and involves a growing array of preventive interventions targeting diverse populations. Dynamic models are some of the most advanced tools available to study the population impact of RSV. However, current models often focus on a single population, such as infants or the elderly, missing the broader effects in how transmission takes place across ages, associated long-term consequences, and guidelines for multiple interventions.
“We currently have three RSV prevention strategies – a maternal vaccine, adult vaccine and pediatric monoclonal antibody – and other interventions in the pipeline,” says Portnoy. “The diverse interventions raise questions, such as, ‘If we get a pediatric vaccine, but we’ve already had the maternal and older adult vaccine for a number of years, how should we roll out the pediatric RSV vaccine? What should the recommendations be for everything together?’”
Addressing these challenges, Portnoy’s project will be the first dynamic modeling framework for RSV that is designed from the outset to consider all populations jointly. Portnoy’s approach uses Agent-Based Modeling (ABM), which defines “agents” as autonomous individuals with distinct characteristics, such as age, behavior, and social connections. It then tracks how these agents interact, providing a system-wide view of how vaccines and interventions work in action.
“The idea would be that the ABM-driven framework could answer current and future policy questions,” says Portnoy. “The focus is to close evidence gaps to improve the accuracy and relevance of RSV impact assessments and support better-informed public health decision-making.”
Methodological Innovation Advancing Global Health
Beyond guiding policy, Portnoy’s research has served to advance infectious disease research by adapting methods typically used in data-rich environments to address public health challenges in data-limited settings. Her prior work, using Bayesian meta-regression methods to produce country-specific estimates of vaccine delivery costs in LMICs, has been broadly referenced to support the work of other researchers globally.
“Decision science provides a transparent evaluative framework for the sorts of decisions that policy makers either are or are not making,” says Portnoy. “Because even doing nothing is still a decision that has an impact that we can quantify, and that we can estimate. By capturing these complexities, my goal is to provide policymakers with practical and actionable insights that not only show whether a vaccine is cost-effective, but also how distribution systems can be made more efficient, equitable, and resilient.”
In this NIH project, Portnoy will provide a powerful framework that integrates epidemiological, demographic, and economic data for all ages at risk for RSV – a framework that does not currently exist. This flexible, computationally powerful tool will guide policymakers in evaluating the impact of interventions while providing an open-access, reproducible format and modeling methods for global researchers.
“Dr. Portnoy demonstrates how rigorous computational approaches can drive real-world public health solutions,” says Yannis Paschalidis, Distinguished Professor of Engineering and Director of Hariri Institute for Computing. “Her research not only generates actionable insights to protect at-risk populations but also equips scientists with reusable tools to advance the field globally.”