Modeling latent spatio-temporal disease incidence using penalized composite link models Articles uri icon

publication date

  • March 2022

start page

  • e0263711

issue

  • 3

volume

  • 17

International Standard Serial Number (ISSN)

  • 1932-6203

abstract

  • 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., 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.

subjects

  • Statistics