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COMBINING COMMUNITY AND CLINICAL DATA FOR AUGMENTING INFLUENZA MODELING

Abstract

This EAGER represents timely and essential exploratory work assessing the value of community-sourced data in infectious disease modeling efforts. Community-generated data can suffer from lack of information about the reference population, which hinders prevalence estimates. In theory, real-time and near real-time community-sourced data has been recognized to offer important opportunity to improve timeliness and scope of infectious disease modeling efforts, but there are still fundamental questions regarding the value of community infection data for understanding, monitoring and forecasting. Towards this, work here will study how community and clinically generated data compare regarding measures of disease incidence, contributing population demographics, and spatio-temporal coverage in influenza dynamics. Public dissemination of our research and findings will help expose and educate the community in data generation and forecasting efforts. This project involves a rigorous and systematic comparison between contemporaneous community and clinical data on acute respiratory infections. The goal of this work will be to first generate a diverse community-sourced data set with a defined reference population. We will then assess significance of outcomes between groups in community and clinical data, accounting for demographic and epidemiological factors. Dynamical modeling and Bayesian inference methods will be used to develop and augment disease forecasts. Normalized and municipal scale estimates from the community samples will be integrated and the data generation and modeling efforts will together be used to assess the impact of community data on real-time and near-real time simulations and forecasts. The high-risk work can potentially be paradigm shifting regarding how we collect and use data in forecasting methods for disease as well as a broader range of societal issues.

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Funding Source

Project Period

2016-2018