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Robust methods for generalized partial linear partial additive models with an application to detection of disease outbreaks

University of British Columbia

An essential function of the public health system is to detect and control disease outbreaks. The British Columbia Centre for Disease Control (BC CDC) monitors approximately 60 notifiable disease counts from 8 branch offices in 16 health service delivery areas. These disease counts exhibit a variety of characteristics, such as seasonality in meningococcal and a long-term trend in acute hepatitis A. As staff need to determine whether the reported counts are higher than expected, the detection process is both costly and fallible. To alleviate this problem, in the early 2000’s the BC CDC commissioned an automated statistical method to detect disease outbreaks. The method is based on a generalized additive partially linear model and appears to capture the characteristics of disease counts. However, it relies on certain ad-hoc criteria to flag counts for an outbreak. The BC CDC is interested in considering other alternatives. In this thesis, we discuss an outbreak detection method based on robust estimators. It builds on recently proposed robust estimators for additive, generalized additive, and generalized linear models. Using real and simulated data, we compare our method with that of the BC CDC and other natural competitors and present promising results.

Text

2019-08-29

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/71526

Statistics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/