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Activity Number: 474 - SPEED: Infectious Disease, Environmental Epidemiology, and Diet
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract #327052 Presentation
Title: Online Sequential Monitoring of Disease Incidence Rates with an Application to the Florida Influenza-Like Illness Data
Author(s): Kai Yang* and Peihua Qiu
Companies: University of Florida and University of Florida
Keywords: Disease surveillance; Dynamic systems; Nonparametric methods; Process control; Spatio-temporal correlation; Sequential monitoring

Online sequential monitoring of the disease incidence rates is critically important for public health and stability of our society. Governments around the world have invested a great amount of resource in building efficient disease reporting and surveillance systems. In these systems, conventional control charts, such as the cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) charts, are usually included for disease surveillance purpose. However, these charts require many assumptions on the observed data, including the ones of independent and identically normally distributed data when no disease outbreaks are present. These assumptions can hardly be valid in practice, making the results from the conventional control charts unreliable. Motivated by an application to monitor the Florida influenza-like illness data, we develop a new sequential monitoring approach, which can accommodate the dynamic nature of the disease incidence rates, spatio-temporal data correlation, and non-normality. It is shown that our proposed method is much more reliable to use in practice than the commonly used conventional charts for sequential monitoring of disease incidence rates.

Authors who are presenting talks have a * after their name.

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