Statistical modelling of COVID-19 data: Putting generalized additive models to work

  title={Statistical modelling of COVID-19 data: Putting generalized additive models to work},
  author={Cornelius Fritz and Giacomo De Nicola and Martje Rave and Maximilian Weigert and Yegane Khazaei and Ursula Berger and Helmut Kuchenhoff and G{\"o}ran Kauermann},
  journal={Statistical Modelling},
Over the course of the COVID-19 pandemic, Generalized Additive Models (GAMs) have been successfully employed on numerous occasions to obtain vital data-driven insights. In this article we further substantiate the success story of GAMs, demonstrating their flexibility by focusing on three relevant pandemic-related issues. First, we examine the interdepency among infections in different age groups, concentrating on school children. In this context, we derive the setting under which parameter… 



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  • N. Perra
  • Computer Science
    Physics Reports
  • 2021