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Estimating and forecasting COVID-19 attack rates and mortality
David I. Ketcheson; Hernando C. Ombao; Paula Moraga; Tarig Ballal; Carlos M. Duarte.
Afiliação
  • David I. Ketcheson; King Abdullah University of Science & Technology
  • Hernando C. Ombao; King Abdullah University of Science & Technology
  • Paula Moraga; University of Bath
  • Tarig Ballal; King Abdullah University of Science & Technology
  • Carlos M. Duarte; King Abdullah University of Science & Technology
Preprint em En | PREPRINT-MEDRXIV | ID: ppmedrxiv-20097972
ABSTRACT
We describe a model for estimating past and current infections as well as future deaths due to the ongoing COVID-19 pandemic. The model does not use confirmed case numbers and is based instead on recorded numbers of deaths and on the age-specific population distribution. A regularized deconvolution technique is used to infer past infections from recorded deaths. Forecasting is based on a compartmental SIR-type model, combined with a probability distribution for the time from infection to death. The effect of non-pharmaceutical interventions (NPIs) is modelled empirically, based on recent trends in the death rate. The model can also be used to study counterfactual scenarios based on hypothetical NPI policies.
Licença
cc_no
Texto completo: 1 Coleções: 09-preprints Base de dados: PREPRINT-MEDRXIV Idioma: En Ano de publicação: 2020 Tipo de documento: Preprint
Texto completo: 1 Coleções: 09-preprints Base de dados: PREPRINT-MEDRXIV Idioma: En Ano de publicação: 2020 Tipo de documento: Preprint