Spatial autoregressive modelling of epidemiological data: geometric mean model proposal

• Mabel Morales-Otero ^[1] ; Christel Faes ^[2] ; Vicente Núñez-Antón ^[3]
  1. [1] Universidad de Navarra

     Universidad de Navarra

     Pamplona, España

     
  2. [2] University of Hasselt

     University of Hasselt

     Arrondissement Hasselt, Bélgica

     
  3. [3] Universidad del País Vasco/Euskal Herriko Unibertsitatea

     Universidad del País Vasco/Euskal Herriko Unibertsitatea

     Leioa, España

     

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• Localización: Sort: Statistics and Operations Research Transactions, ISSN 1696-2281, Vol. 49, Nº. 1, 2025, págs. 93-120
• Idioma: inglés
• DOI: 10.57645/20.8080.02.24
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• Resumen

  • We propose the geometric mean spatial conditional model for fitting spatial public Health data, assuming that the disease incidence in one region depends on that of neighbouring regions, and incorporating an autoregressive spatial term based on their geometric mean. We explore alternative spatial weights matrices, including those based on contiguity, distance, covariate differences and individuals’ mobility. A simulation study assesses the model’s performance with mobility-based spatial correlation. We illustrate our proposals by analysing the COVID-19 spread in Flanders, Belgium, and comparing the proposed model with other commonly used spatial models. Our approach demonstrates advantages in interpretability, computational efficiency, and flexibility over the commonly used and previously existing methods.