Spatiotemporal surveillance methods in the presence of spatial correlation.

Abstract:

:Health surveillance involves collecting public health data on chronic and infectious diseases to detect changes in disease incidence rates in order to improve public health. Timely detection of disease clusters is essential in prospective public health surveillance. Most existing health surveillance research is based on the assumption that observations from different regions are independent. This paper proposes a set of multivariate surveillance schemes generalized from well-known detection methods in multivariate statistical process control based on likelihood ratio tests. We use Monte Carlo simulations to compare these methods for health surveillance in the presence of spatial correlations. By taking advantage of correlations among regions,the proposed schemes are able to perform better than existing surveillance methods and provide faster and more accurate detection of outbreaks. An example of breast cancer in New Hampshire is presented to demonstrate the application of these methods when observations are spatially correlated counts.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Jiang W,Han SW,Tsui KL,Woodall WH

doi

10.1002/sim.3877

subject

Has Abstract

pub_date

2011-02-28 00:00:00

pages

569-83

issue

5

eissn

0277-6715

issn

1097-0258

journal_volume

30

pub_type

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