Adaptive likelihood ratio approaches for the detection of space–time disease clusters

Max Sousa de Lima and Luiz Henrique Duczmal

Computational Statistics & Data Analysis, 2014, vol. 77, issue C, 352-370

Abstract: A methodology based on adaptive likelihood ratios (ALRs) for the detection of emerging disease clusters is presented. The martingale structure of the regular likelihood ratio is preserved by the ALR. The upper limit for the false alarm rate of the proposed method depends only on the quantity of evaluated cluster candidates. Thus Monte Carlo simulations are not required to validate the procedures’ statistical significance, allowing the construction of a fast computational algorithm to detect clusters. The number of evaluated clusters is also significantly reduced, through the use of an adaptive approach to prune many unpromising clusters. This further increases the computational speed. Performance is evaluated through simulations to measure the average detection delay and the probability of correct cluster detection. We present applications for thyroid cancer in New Mexico and hanseniasis in children in the Brazilian Amazon.

Keywords: Spatial analysis; Space–time clusters; Sequential analysis; Adaptive likelihood ratio; Simulation (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947314000929
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:77:y:2014:i:c:p:352-370

DOI: 10.1016/j.csda.2014.03.015

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().