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Activity Number: 133 - Statistical Issues in Environmental Epidemiology and Pharmacoepidemiology
Type: Contributed
Date/Time: Monday, August 9, 2021 : 1:30 PM to 3:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #317994
Title: Effective Disease Surveillance by Using Covariate Information
Author(s): Kai Yang*
Companies: University of Florida
Keywords: Covariate information; Data correlation; Disease surveillance; Kernel smoothing; Spatio-temporal data; Statistical process control

Effective surveillance of infectious diseases, cancers and other deadly diseases is critically important for public health and safety of our society. Incidence data of such diseases are often collected spatially from different clinics and hospitals through a regional, national or global disease reporting system. In such a system, new batches of data keep being collected over time, and a decision needs to be made immediately after new data are collected regarding whether there is a disease outbreak at the current time point. This is the disease surveillance problem that will be focused in this talk. There are some existing methods for solving this problem, most of which use the disease incidence data only. In practice, however, disease incidence is often associated with some covariates, including the air temperature, humidity, and other weather or environmental conditions. In this talk, we will introduce a new methodology for disease surveillance which can make use of the helpful covariate information to improve its effectiveness.

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

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