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INTEGRATING DATA STREAMS WITH MULTI-SCALE MODELING TO GUIDE NOROVIRUS VACCINE DECISION-MAKING

Abstract

Noroviruses cause over 20 million diarrhea cases annually in the United States, one-fifth of all diarrhea cases globally, and over 200,000 deaths worldwide. Norovirus vaccines are currently in development. Our goal is to develop a comprehensive detailed, user-friendly norovirus transmission and vaccination simulator that will help guide and inform norovirus vaccine design and implementation. To achieve this goal, a number of specific insights and parameter estimates are required, such as: At what rate does immunity to norovirus wane in individuals? How does prior infection (or vaccination) affect the course of illness and the duration/quantity of viral shedding? What is the degree of cross-protecting immunity at the genogroup, genotype and strain-level? With such results in hand, we can answer the following vaccine strategy questions: What frequency of re-vaccination is required for control? How often will a norovirus vaccine need to be updated to keep up with viral evolution? What are the population-level health impacts of age-targeted (or other) vaccination strategies? To answer these questions, we will integrate detailed data from multiple sources including volunteer infection studies, national surveillance, and viral sequence datasets. We will fit mathematical and statistical models to these datasets to answer the questions posed above. Our analyses will provide a thorough understanding of the epidemiological and evolutionary dynamics of the virus. This information will be used to develop and implement a detailed norovirus transmission and vaccination simulator. This simulator will be packaged as a user-friendly application for vaccine designers and policy implementers to analyze and forecast possible impacts of vaccine formulations and distribution strategies.

People

Funding

2018-2023