Characterizing Respiratory Syncytial Virus Burden and Prevention in the United States

Respiratory syncytial virus (RSV) is the most common viral cause of lower respiratory tract infection (LRTI) in infants globally, but also leads to severe outcomes among non-infant, high-risk adult populations. Mathematical models have been used to characterize RSV burden, but estimating the impact of interventions is often limited to a single target population (e.g., pediatric, maternal, or the elderly). Until recently, the only option for prevention of RSV was the monoclonal antibody, palivizumab, but there is now a substantial active pipeline of vaccine candidates and monoclonal antibodies targeting multiple risk groups, both licensed and in development. Due to the high morbidity and mortality from RSV, it is critical to assess population-level effectiveness of new interventions prior to introduction so that resources can be prioritized on interventions and delivery schedules that have the highest impact. In this research project, I will characterize RSV burden and prevention strategies in multiple risk groups in the United States. I propose to develop an agent-based modeling framework that enables timely incorporation of the most current understanding of RSV infection and calibration to epidemiologic data accounting for seasonality, social contact rates, immunity from prior infection, and maternal antibody transfer. This work will establish a detailed understanding of RSV risk and RSV natural history and produce updated estimates of RSV burden and cost-effectiveness of RSV prevention in the United States as new interventions are introduced and require assessment. This work will also demonstrate the value of both agent-based dynamic modeling and individual-level risk factors in characterizing and addressing optimal strategies for RSV prevention. I will first calibrate and apply the modeling framework to estimate health outcomes, including novel outcomes such as long-term sequelae and out-of-hospital mortality, in the United States, and subsequently extend the modeling framework to incorporate cost data and estimate the cost-effectiveness of RSV prevention in the United States. The analytic approach of agent-based, transmission dynamic modeling will provide flexibility in addressing prevention among different target populations with a combination of intervention strategies, incorporating the latest results of clinical trials. The overall goal of this work is to improve population health through characterizing RSV burden and potential prevention strategies. Through this award, I will learn crucial skills for taking the next step in my career as an independent researcher dedicated to improving vaccine access and delivery. Project Number: 1K01AI190051-01 | Fiscal Year: 2025 | NIH Institute/Center: National Institute of Allergy and Infectious Diseases (NIAID) | Principal Investigator: Allison Portnoy | Institution: BOSTON UNIVERSITY MEDICAL CAMPUS, BOSTON, MA | Award Amount: $189,276 | Activity Code: K01 | Study Section: Microbiology and Infectious Diseases Research Study Section[MID] View on NIH RePORTER: https://reporter.nih.gov/project-details/1K01AI19005101