Noise is not error : detecting parametric heterogeneity between epidemiologic time series

Detalhes bibliográficos
Autor(a) principal: Romero-Severson, Ethan O.
Data de Publicação: 2018
Outros Autores: Ribeiro, Ruy M., Castro, Mário
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10451/45075
Resumo: © Copyright © 2018 Romero-Severson, Ribeiro and Castro. This is an open-accessarticle distributed under the terms of the Creative Commons Attribution License (CCBY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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spelling Noise is not error : detecting parametric heterogeneity between epidemiologic time seriesDeterministicEpidemiologyFixed effectsPanel dataRandom effectsStochastic deterministic© Copyright © 2018 Romero-Severson, Ribeiro and Castro. This is an open-accessarticle distributed under the terms of the Creative Commons Attribution License (CCBY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.Mathematical models play a central role in epidemiology. For example, models unify heterogeneous data into a single framework, suggest experimental designs, and generate hypotheses. Traditional methods based on deterministic assumptions, such as ordinary differential equations (ODE), have been successful in those scenarios. However, noise caused by random variations rather than true differences is an intrinsic feature of the cellular/molecular/social world. Time series data from patients (in the case of clinical science) or number of infections (in the case of epidemics) can vary due to both intrinsic differences or incidental fluctuations. The use of traditional fitting methods for ODEs applied to noisy problems implies that deviation from some trend can only be due to error or parametric heterogeneity, that is noise can be wrongly classified as parametric heterogeneity. This leads to unstable predictions and potentially misguided policies or research programs. In this paper, we quantify the ability of ODEs under different hypotheses (fixed or random effects) to capture individual differences in the underlying data. We explore a simple (exactly solvable) example displaying an initial exponential growth by comparing state-of-the-art stochastic fitting and traditional least squares approximations. We also provide a potential approach for determining the limitations and risks of traditional fitting methodologies. Finally, we discuss the implications of our results for the interpretation of data from the 2014-2015 Ebola epidemic in Africa.This work was funded by NIH grants R01-AI087520 and R01-AI104373; grants FIS2013-47949-C2-2-P and FIS2016-78883-C2-2-P and PRX 16/00287 (Spain); and PIRSES-GA-2012-317893 (7th FP, EU).FrontiersRepositório da Universidade de LisboaRomero-Severson, Ethan O.Ribeiro, Ruy M.Castro, Mário2020-12-02T14:38:20Z20182018-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10451/45075engFrontiers in Microbiology 1 July 2018 | Volume 9 | Article 152910.3389/fmicb.2018.01529.1664-302Xinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-08T16:46:34Zoai:repositorio.ul.pt:10451/45075Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:57:34.069158Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Noise is not error : detecting parametric heterogeneity between epidemiologic time series
title Noise is not error : detecting parametric heterogeneity between epidemiologic time series
spellingShingle Noise is not error : detecting parametric heterogeneity between epidemiologic time series
Romero-Severson, Ethan O.
Deterministic
Epidemiology
Fixed effects
Panel data
Random effects
Stochastic deterministic
title_short Noise is not error : detecting parametric heterogeneity between epidemiologic time series
title_full Noise is not error : detecting parametric heterogeneity between epidemiologic time series
title_fullStr Noise is not error : detecting parametric heterogeneity between epidemiologic time series
title_full_unstemmed Noise is not error : detecting parametric heterogeneity between epidemiologic time series
title_sort Noise is not error : detecting parametric heterogeneity between epidemiologic time series
author Romero-Severson, Ethan O.
author_facet Romero-Severson, Ethan O.
Ribeiro, Ruy M.
Castro, Mário
author_role author
author2 Ribeiro, Ruy M.
Castro, Mário
author2_role author
author
dc.contributor.none.fl_str_mv Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Romero-Severson, Ethan O.
Ribeiro, Ruy M.
Castro, Mário
dc.subject.por.fl_str_mv Deterministic
Epidemiology
Fixed effects
Panel data
Random effects
Stochastic deterministic
topic Deterministic
Epidemiology
Fixed effects
Panel data
Random effects
Stochastic deterministic
description © Copyright © 2018 Romero-Severson, Ribeiro and Castro. This is an open-accessarticle distributed under the terms of the Creative Commons Attribution License (CCBY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
publishDate 2018
dc.date.none.fl_str_mv 2018
2018-01-01T00:00:00Z
2020-12-02T14:38:20Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10451/45075
url http://hdl.handle.net/10451/45075
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Frontiers in Microbiology 1 July 2018 | Volume 9 | Article 1529
10.3389/fmicb.2018.01529.
1664-302X
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Frontiers
publisher.none.fl_str_mv Frontiers
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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