Respiratory syncytial virus (RSV) causes a large burden of infections in both infants and the elderly. While there is currently no vaccine, several candidates are undergoing clinical testing and are expected to become available in the coming years. A variety of approaches for vaccine delivery are being considered, including immunizing mothers to protect infants and directly immunizing children and high-risk adults. There is an urgent need to determine the delivery strategy that will maximize the direct and indirect benefits of RSV vaccines across the entire population and to monitor variations in impact from available surveillance data. The overall goals for this project are to use mathematical models to optimize vaccine delivery strategies for novel vaccines against RSV, to use models to guide the design of future clinical trials, and to develop statistical models that can monitor variations in vaccine impact at local-levels using routinely-collected healthcare data. Disease dynamics and the impact of vaccines are often characterized at aggregated state or national levels. However, this aggregation ignores important variability at the local level due to variations in vaccine uptake and/or spatially-structured contact networks. This type of variability can result in disparities in the impact of a vaccine between locales. By quantifying and understanding the drivers of heterogeneity in the dynamics of RSV, we can evaluate different delivery strategies that would maximize the impact of a vaccine against RSV across the entire population and design surveillance to monitor impact at the local level. To develop and validate these cutting-edge modeling approaches, we will use surveillance data and routinely-collected administrative hospitalization data from several locations in the United States that represent a broad range of epidemiological settings. We will test specific hypotheses about the spatial scale at which pathogen transmission occurs, the determinants of this spatial variation, and the implications of this variation for vaccine impact.
In Aim 1, we will use statistical models and dynamic models of transmission to quantify spatial variability in the dynamics of RSV epidemics and to test hypotheses about the mechanisms driving these patterns, including the role of local and regional contact patterns among different age groups. Understanding these mechanisms will help to inform predictions of the impact of vaccines against RSV.
In Aim 2, we will use transmission models to test alternative hypotheses about the strategy for vaccine delivery that would maximize benefits across age groups and across space. The information derived from these models can be used to guide the design of clinical trials for RSV. Finally, in Aim 3, we will develop an analysis framework that will allow us to design surveillance and monitor local variations in the impact of a vaccine once it is introduced using routinely-collected data on hospitalizations while controlling for spatiotemporally-varying confounders.
Vaccines against RSV are expected to become available in the near future. With a number of new vaccines and vaccine delivery strategies under consideration, models can play an important role at each stage in development, testing, and deployment. We will develop and validate a set of statistical and mathematical models that can be used to test hypotheses about the impact of alternative strategies on disease rates in different populations in the United States, to guide the design of clinical trials, and to monitor the impact of these vaccines on respiratory diseases once they are introduced.
