Show simple item record

dc.contributor.authorKetcheson, David I.
dc.contributor.authorOmbao, Hernando
dc.contributor.authorMoraga, Paula
dc.contributor.authorBallal, Tarig
dc.contributor.authorDuarte, Carlos M.
dc.date.accessioned2020-06-10T09:03:12Z
dc.date.available2020-06-10T09:03:12Z
dc.date.issued2020-05-15
dc.identifier.citationKetcheson, 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
dc.identifier.doi10.1101/2020.05.11.20097972
dc.identifier.urihttp://hdl.handle.net/10754/663490
dc.description.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.
dc.publisherCold Spring Harbor Laboratory
dc.relation.urlhttp://medrxiv.org/lookup/doi/10.1101/2020.05.11.20097972
dc.rightsArchived with thanks to Cold Spring Harbor Laboratory
dc.titleEstimating and forecasting COVID-19 attack rates and mortality
dc.typePreprint
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentBiostatistics Group
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentMarine Science Program
dc.contributor.departmentNumerical Mathematics Group
dc.contributor.departmentRed Sea Research Center (RSRC)
dc.contributor.departmentStatistics Program
dc.eprint.versionPre-print
dc.contributor.institutionDepartment of Mathematical Sciences, University of Bath, Bath, BA2 7AY, UK.
kaust.personKetcheson, David I.
kaust.personOmbao, Hernando
kaust.personBallal, Tarig
kaust.personDuarte, Carlos M.
refterms.dateFOA2020-06-10T09:04:02Z


Files in this item

Thumbnail
Name:
2020.05.11.20097972v1.full.pdf
Size:
519.6Kb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record