This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 160
Type: Topic Contributed
Date/Time: Monday, August 2, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Defense and National Security
Abstract - #308699
Title: Using Disease Surveillance Data to Forecast Disease Risk
Author(s): Al Ozonoff*+ and Yorghos Tripodis and John Brownstein
Companies: Boston University School of Public Health and Boston University and Children's Hospital Boston
Address: 801 Massachusetts Avenue, 3rd floor, Boston, MA, 02118,
Keywords: disease surveillance ; time series ; ARIMA ; forecasting ; outbreaks ; influenza

Events such as extreme weather events and natural disasters such as floods, or earthquakes, occur with unpredictable timing and magnitude with obvious risks to human health. Similarly, epidemics or pandemics of infectious disease are unpredictable and can lead to morbidity and mortality on massive scales. To address this concern we propose a methodology, inspired by similar work in econometrics and the physical sciences, to forecast future risk of a disease outbreak. We first fit linear time series models to surveillance data, then implement a resampling procedure to produce synthetic time series with similar dynamic characteristics. We use the resamples to generate an empirical forecast of future disease events. In this paper, we will discuss implementation issues related to seasonality, model selection, and resampling; then discuss an application to influenza mortality data.

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