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Epilocal: a real-time tool for local epidemic monitoring

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  • Marco Bonetti
  • Ugofilippo Basellini

Abstract

We describe Epilocal, a simple R program designed to automatically download the most recent data on reported infected SARS-CoV-2 cases for all Italian provinces and regions, and to provide a simple descriptive analysis. For each province the cumulative number of reported infected cases is available each day. In addition, the current numbers of hospitalized patients (separately for intensive care or not) and the cumulative number of deceased individuals are available at the region level. The data are analyzed through Poisson generalized linear models with logarithmic link function and polynomial regression on time. For cumulative data, we also consider a logistic parameterisation of the hazard function. Automatic model selection is performed to choose among the different model specifications, based on the statistical significance of the corresponding estimated parameters and on goodness-of-fit assessment. The chosen model is used to produce up-to-today estimates of the growth rate of the counts. Results are plotted on a map of the country to allow for a visual assessment of the geographic distribution of the areas with differential prevalence and rates of growth.

Suggested Citation

  • Marco Bonetti & Ugofilippo Basellini, 2020. "Epilocal: a real-time tool for local epidemic monitoring," Working Papers axhbndayuclqnee2wf7y, French Institute for Demographic Studies.
    • Handle: RePEc:idg:wpaper:axhbndayuclqnee2wf7y
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    References listed on IDEAS

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    1. Monica Chiogna and Carlo Gaetan & Carlo Gaetan, 2002. "Dynamic generalized linear models with application to environmental epidemiology," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(4), pages 453-468, October.
    2. Arne Henningsen & Ott Toomet, 2011. "maxLik: A package for maximum likelihood estimation in R," Computational Statistics, Springer, vol. 26(3), pages 443-458, September.
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    Cited by:

    1. Marco Bonetti & Ugofilippo Basellini, 2021. "Epilocal: A real-time tool for local epidemic monitoring," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 44(12), pages 307-332.
    2. Ugofilippo Basellini & Carlo Giovanni Camarda, 2020. "Modelling COVID-19 mortality at the regional level in Italy," Working Papers axq0sudakgkzhr-blecv, French Institute for Demographic Studies.

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