Back to Search
Start Over
Regularized spatial and spatio-temporal cluster detection.
- Source :
-
Spatial and spatio-temporal epidemiology [Spat Spatiotemporal Epidemiol] 2022 Jun; Vol. 41, pp. 100462. Date of Electronic Publication: 2021 Nov 01. - Publication Year :
- 2022
-
Abstract
- Spatial and spatio-temporal cluster detection are important tools in public health and many other areas of application. Cluster detection can be approached as a multiple testing problem, typically using a space and time scan statistic. We recast the spatial and spatio-temporal cluster detection problem in a high-dimensional data analytical framework with Poisson or quasi-Poisson regression with the Lasso penalty. We adopt a fast and computationally-efficient method using a novel sparse matrix representation of the effects of potential clusters. The number of clusters and tuning parameters are selected based on (quasi-)information criteria. We evaluate the performance of our proposed method including the false positive detection rate and power using a simulation study. Application of the method is illustrated using breast cancer incidence data from three prefectures in Japan.<br /> (Copyright © 2021. Published by Elsevier Ltd.)
Details
- Language :
- English
- ISSN :
- 1877-5853
- Volume :
- 41
- Database :
- MEDLINE
- Journal :
- Spatial and spatio-temporal epidemiology
- Publication Type :
- Academic Journal
- Accession number :
- 35691644
- Full Text :
- https://doi.org/10.1016/j.sste.2021.100462