Methods for Estimating Disease Burden of Seasonal Influenza

Influenza is a common respiratory infection with substantial disease and economic burdens. Due to the threat of another global pandemic, significant resources have been devoted to increase influenza surveillance, laboratory capacity, and pandemic preparedness worldwide since 2009. Disease burden estimates are critical for evaluating vaccine benefits, for communicating prevention and control messages, and for developing evidence-based policies for resource allocations. There are several major analytical challenges in estimating influenza disease burden and a pressing need to develop methods and tools to support burden estimation that will increase accuracy, improve precision, enhance multi-partner collaboration, and quantify uncertainty appropriately.

In this 2-year exploratory project, we will examine the use of state-of-the-art approaches from epidemiology and evidence synthesis to influenza burden estimation. In Aim 1, we will develop single-site time-series models for attributing counts of adverse respiratory health outcomes to influenza. In Aim 2, we will develop data integration models for combining information across multiple sites and perform predictions to sites without burden estimates. Anticipated outcomes from this project include (1) feasibility and performance evaluations of the proposed time-series and data integration models; and (2) substantive findings on influenza-associated morbidity as measured by ED visits and hospitalization for respiratory disease. Moreover, models developed in this project are also widely applicable to other respiratory pathogens.

Study Locations: Arizona, California, Georgia, Maryland, Missouri, Nevada, New Jersey, New York, North Carolina, and Utah

Investigators: Howard Chang, Stefanie Ebelt

Current or Recent Funding Sources: National Institutes of Health (1R21AI167418-01A1)

Previous Funding Sources: N/A

Category: Epidemiologic & Biostatistical Methods