Statistical vhallenges in tracking the evolution of SARS-CoV-2

Genomic surveillance of SARS-CoV-2 has been instrumental in tracking the spread and evolution of the virus during the pandemic. The availability of SARS-CoV-2 molecular sequences isolated from infected individuals, coupled with phylodynamic methods, have provided insights into the origin of the viru...

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Detalles Bibliográficos
Autores: Cappello, Lorenzo, Kim, Jaehee, Liu, Sifan, Palacios, Julia A.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/59279
Acceso en línea:http://hdl.handle.net/10230/59279
http://dx.doi.org/10.1214/22-sts853
Access Level:acceso abierto
Palabra clave:Bayesian nonparametrics
birth-death processes
Coalescent
genetic epidemiology
phylodynamics
SIR models
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spelling Statistical vhallenges in tracking the evolution of SARS-CoV-2Cappello, LorenzoKim, JaeheeLiu, SifanPalacios, Julia A.Bayesian nonparametricsbirth-death processesCoalescentgenetic epidemiologyphylodynamicsSIR modelsGenomic surveillance of SARS-CoV-2 has been instrumental in tracking the spread and evolution of the virus during the pandemic. The availability of SARS-CoV-2 molecular sequences isolated from infected individuals, coupled with phylodynamic methods, have provided insights into the origin of the virus, its evolutionary rate, the timing of introductions, the patterns of transmission, and the rise of novel variants that have spread through populations. Despite enormous global efforts of governments, laboratories, and researchers to collect and sequence molecular data, many challenges remain in analyzing and interpreting the data collected. Here, we describe the models and methods currently used to monitor the spread of SARS-CoV-2, discuss long-standing and new statistical challenges, and propose a method for tracking the rise of novel variants during the epidemic.Institute of Mathematical Statistics202420242022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/59279http://dx.doi.org/10.1214/22-sts853reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésStatistical Science. 2022;37(2):162-82.© Institute of Mathematical Statistics, 2022info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/592792026-06-12T07:21:37Z
dc.title.none.fl_str_mv Statistical vhallenges in tracking the evolution of SARS-CoV-2
title Statistical vhallenges in tracking the evolution of SARS-CoV-2
spellingShingle Statistical vhallenges in tracking the evolution of SARS-CoV-2
Cappello, Lorenzo
Bayesian nonparametrics
birth-death processes
Coalescent
genetic epidemiology
phylodynamics
SIR models
title_short Statistical vhallenges in tracking the evolution of SARS-CoV-2
title_full Statistical vhallenges in tracking the evolution of SARS-CoV-2
title_fullStr Statistical vhallenges in tracking the evolution of SARS-CoV-2
title_full_unstemmed Statistical vhallenges in tracking the evolution of SARS-CoV-2
title_sort Statistical vhallenges in tracking the evolution of SARS-CoV-2
dc.creator.none.fl_str_mv Cappello, Lorenzo
Kim, Jaehee
Liu, Sifan
Palacios, Julia A.
author Cappello, Lorenzo
author_facet Cappello, Lorenzo
Kim, Jaehee
Liu, Sifan
Palacios, Julia A.
author_role author
author2 Kim, Jaehee
Liu, Sifan
Palacios, Julia A.
author2_role author
author
author
dc.subject.none.fl_str_mv Bayesian nonparametrics
birth-death processes
Coalescent
genetic epidemiology
phylodynamics
SIR models
topic Bayesian nonparametrics
birth-death processes
Coalescent
genetic epidemiology
phylodynamics
SIR models
description Genomic surveillance of SARS-CoV-2 has been instrumental in tracking the spread and evolution of the virus during the pandemic. The availability of SARS-CoV-2 molecular sequences isolated from infected individuals, coupled with phylodynamic methods, have provided insights into the origin of the virus, its evolutionary rate, the timing of introductions, the patterns of transmission, and the rise of novel variants that have spread through populations. Despite enormous global efforts of governments, laboratories, and researchers to collect and sequence molecular data, many challenges remain in analyzing and interpreting the data collected. Here, we describe the models and methods currently used to monitor the spread of SARS-CoV-2, discuss long-standing and new statistical challenges, and propose a method for tracking the rise of novel variants during the epidemic.
publishDate 2022
dc.date.none.fl_str_mv 2022
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/59279
http://dx.doi.org/10.1214/22-sts853
url http://hdl.handle.net/10230/59279
http://dx.doi.org/10.1214/22-sts853
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Statistical Science. 2022;37(2):162-82.
dc.rights.none.fl_str_mv © Institute of Mathematical Statistics, 2022
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © Institute of Mathematical Statistics, 2022
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Mathematical Statistics
publisher.none.fl_str_mv Institute of Mathematical Statistics
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
repository.name.fl_str_mv
repository.mail.fl_str_mv
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