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    Estimating and forecasting COVID-19 attack rates and mortality

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    2020.05.11.20097972v1.full.pdf
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    Type
    Preprint
    Authors
    Ketcheson, David I. cc
    Ombao, Hernando cc
    Moraga, Paula
    Ballal, Tarig
    Duarte, Carlos M. cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Biological and Environmental Sciences and Engineering (BESE) Division
    Biostatistics Group
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Marine Science Program
    Numerical Mathematics Group
    Red Sea Research Center (RSRC)
    Statistics Program
    Date
    2020-05-15
    Permanent link to this record
    http://hdl.handle.net/10754/663490
    
    Metadata
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    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.
    Citation
    Ketcheson, D. I., Ombao, H. C., Moraga, P., Ballal, T., & Duarte, C. M. (2020). Estimating and forecasting COVID-19 attack rates and mortality. doi:10.1101/2020.05.11.20097972
    Publisher
    Cold Spring Harbor Laboratory
    DOI
    10.1101/2020.05.11.20097972
    Additional Links
    http://medrxiv.org/lookup/doi/10.1101/2020.05.11.20097972
    ae974a485f413a2113503eed53cd6c53
    10.1101/2020.05.11.20097972
    Scopus Count
    Collections
    Biological and Environmental Sciences and Engineering (BESE) Division; Red Sea Research Center (RSRC); Preprints; Marine Science Program; Applied Mathematics and Computational Science Program; Statistics Program; Numerical Mathematics Group; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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