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<p>The COVID-19 pandemic in France induced the development of a national, high spatiotemporal resolution confirmed infection cases database. We aimed to estimate the predictive ability of census-based indicators on the infection risk to assess their potential usefulness in future pandemic response. We collected and aggregated all counts of biologically confirmed cases of SARS-CoV-2 infection in the Auvergne-Rhône-Alpes region in France at small-area statistical units between May 2020 and February 2021 (second wave). Ten census-based ecological covariates were evaluated as predictors of case incidence using a Poisson regression with conditional autoregressive (CAR) spatial effects. Benefits of CAR effects and covariates on model predictive ability was assessed comparing posterior predictive distribution of case incidence with the observed value for each statistical unit. Among 7,917,997 inhabitants, 438,992 infection cases over 5410 neighbourhoods were analysed. Spatial correlation was high for the periods before and after the epidemic peak, and illustrated with cartography. The addition of covariates to the null model led to an increase in satisfying prediction of + 5% from 14%, with a maximum of 21% across all periods. The ecological covariates assessed were insufficient to provide a satisfying prediction of infection risk without explicitly accounting for the spatial organization of the epidemic.</p>

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Predictive quality of census-based socio-economic indicators on Covid-19 infection risk at a fine spatial scale in France

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月1日

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  • Nicolas Romain-Scelle,
  • Benjamin Riche,
  • Thomas Benet,
  • Muriel Rabilloud

摘要

The COVID-19 pandemic in France induced the development of a national, high spatiotemporal resolution confirmed infection cases database. We aimed to estimate the predictive ability of census-based indicators on the infection risk to assess their potential usefulness in future pandemic response. We collected and aggregated all counts of biologically confirmed cases of SARS-CoV-2 infection in the Auvergne-Rhône-Alpes region in France at small-area statistical units between May 2020 and February 2021 (second wave). Ten census-based ecological covariates were evaluated as predictors of case incidence using a Poisson regression with conditional autoregressive (CAR) spatial effects. Benefits of CAR effects and covariates on model predictive ability was assessed comparing posterior predictive distribution of case incidence with the observed value for each statistical unit. Among 7,917,997 inhabitants, 438,992 infection cases over 5410 neighbourhoods were analysed. Spatial correlation was high for the periods before and after the epidemic peak, and illustrated with cartography. The addition of covariates to the null model led to an increase in satisfying prediction of + 5% from 14%, with a maximum of 21% across all periods. The ecological covariates assessed were insufficient to provide a satisfying prediction of infection risk without explicitly accounting for the spatial organization of the epidemic.