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

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  • Dae-Jin Lee
  • María Durbán
  • Diego Ayma
  • Jan Van de Kassteele

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.

Suggested Citation

  • Dae-Jin Lee & María Durbán & Diego Ayma & Jan Van de Kassteele, 2022. "Modeling latent spatio-temporal disease incidence using penalized composite link models," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-22, March.
    • Handle: RePEc:plo:pone00:0263711
    DOI: 10.1371/journal.pone.0263711
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    References listed on IDEAS

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    1. Ying C. MacNab & C. B. Dean, 2001. "Autoregressive Spatial Smoothing and Temporal Spline Smoothing for Mapping Rates," Biometrics, The International Biometric Society, vol. 57(3), pages 949-956, September.
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