Lee Hwang, Dae-JinDurbán Reguera, María LuzAyma Anza, Diego ArmandoVan De Kassteele, Jan2024-01-152024-01-152022-03-10Lee D-J, Durba´n M, Ayma D, Van de Kassteele J (2022) Modeling latent spatio-temporal disease incidence using penalized composite link models. PLoS ONE 17(3): e02637111932-6203https://hdl.handle.net/10016/39258Epidemiological 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., Durban M (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 computations that otherwise would be unfeasible. The model is then used to analyze data obtained during the largest outbreak of Q-fever in the Netherlands.eng© 2022 Lee et al.Atribución 3.0 Españaresearch articleEstadísticahttps://doi.org/10.1371/journal.pone.0263711open accesse02637113PLoS One17AR/0000030790