RT Journal Article
T1 Modeling latent spatio-temporal disease incidence using penalized composite link models
A1 Lee Hwang, Dae-Jin
A1 Durbán Reguera, María Luz
A1 Ayma Anza, Diego Armando
A1 Van De Kassteele, Jan
AB Epidemiological data are frequently recorded at coarse spatio-temporal resolutions to protect confidential information or to summarize it in a compact manner. However, the detailed patterns followed by the source data, which may be of interest to researchers and public health officials, are overlooked. We propose to use the penalized composite link model (Eilers PCH (2007)), combined with spatio-temporal P-splines methodology (Lee D.-J., DurbanM (2011)) to estimate the underlying trend within data that have been aggregated not only in space, but also in time. Model estimation is carried out within a generalized linear mixed model framework, and sophisticated algorithms are used to speed up computationsthat otherwise would be unfeasible. The model is then used to analyze data obtained during the largest outbreak of Q-fever in the Netherlands.
PB Indiana University
SN 1932-6203
YR 2022
FD 2022-03-10
LK https://hdl.handle.net/10016/39258
UL https://hdl.handle.net/10016/39258
LA eng
NO This study was supported in the form of funding by Ministerio de Ciencia, Innovación y Universidades (Grant Nos. SEV 2017-0718 andMTM2017-82379-R) awarded to DJL, (Grant No. PID2019-10490RB-100) awarded to MD, by Ministerio de Economía, Industria yCompetitividad, Gobierno de España (Grant No. MTM2014-52184-P) awarded to DJL, MD, and DA, and by Agencia Estatal de Investigación(Grant No. PID2020-115882RB-I00) awarded to DJL.
DS e-Archivo
RD 1 sept. 2024