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A computationally tractable birth-death model that combines phylogenetic and epidemiological data
Inferring the dynamics of pathogen transmission during an outbreak is an important problem in infectious disease epidemiology. In mathematical epidemiology, estimates are often informed by time series of confirmed cases, while in phylodynamics genetic sequences of the pathogen, sampled through time,...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8903285/ https://www.ncbi.nlm.nih.gov/pubmed/35148311 http://dx.doi.org/10.1371/journal.pcbi.1009805 |
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author | Zarebski, Alexander Eugene du Plessis, Louis Parag, Kris Varun Pybus, Oliver George |
author_facet | Zarebski, Alexander Eugene du Plessis, Louis Parag, Kris Varun Pybus, Oliver George |
author_sort | Zarebski, Alexander Eugene |
collection | PubMed |
description | Inferring the dynamics of pathogen transmission during an outbreak is an important problem in infectious disease epidemiology. In mathematical epidemiology, estimates are often informed by time series of confirmed cases, while in phylodynamics genetic sequences of the pathogen, sampled through time, are the primary data source. Each type of data provides different, and potentially complementary, insight. Recent studies have recognised that combining data sources can improve estimates of the transmission rate and the number of infected individuals. However, inference methods are typically highly specialised and field-specific and are either computationally prohibitive or require intensive simulation, limiting their real-time utility. We present a novel birth-death phylogenetic model and derive a tractable analytic approximation of its likelihood, the computational complexity of which is linear in the size of the dataset. This approach combines epidemiological and phylodynamic data to produce estimates of key parameters of transmission dynamics and the unobserved prevalence. Using simulated data, we show (a) that the approximation agrees well with existing methods, (b) validate the claim of linear complexity and (c) explore robustness to model misspecification. This approximation facilitates inference on large datasets, which is increasingly important as large genomic sequence datasets become commonplace. |
format | Online Article Text |
id | pubmed-8903285 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-89032852022-03-09 A computationally tractable birth-death model that combines phylogenetic and epidemiological data Zarebski, Alexander Eugene du Plessis, Louis Parag, Kris Varun Pybus, Oliver George PLoS Comput Biol Research Article Inferring the dynamics of pathogen transmission during an outbreak is an important problem in infectious disease epidemiology. In mathematical epidemiology, estimates are often informed by time series of confirmed cases, while in phylodynamics genetic sequences of the pathogen, sampled through time, are the primary data source. Each type of data provides different, and potentially complementary, insight. Recent studies have recognised that combining data sources can improve estimates of the transmission rate and the number of infected individuals. However, inference methods are typically highly specialised and field-specific and are either computationally prohibitive or require intensive simulation, limiting their real-time utility. We present a novel birth-death phylogenetic model and derive a tractable analytic approximation of its likelihood, the computational complexity of which is linear in the size of the dataset. This approach combines epidemiological and phylodynamic data to produce estimates of key parameters of transmission dynamics and the unobserved prevalence. Using simulated data, we show (a) that the approximation agrees well with existing methods, (b) validate the claim of linear complexity and (c) explore robustness to model misspecification. This approximation facilitates inference on large datasets, which is increasingly important as large genomic sequence datasets become commonplace. Public Library of Science 2022-02-11 /pmc/articles/PMC8903285/ /pubmed/35148311 http://dx.doi.org/10.1371/journal.pcbi.1009805 Text en © 2022 Zarebski et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Zarebski, Alexander Eugene du Plessis, Louis Parag, Kris Varun Pybus, Oliver George A computationally tractable birth-death model that combines phylogenetic and epidemiological data |
title | A computationally tractable birth-death model that combines phylogenetic and epidemiological data |
title_full | A computationally tractable birth-death model that combines phylogenetic and epidemiological data |
title_fullStr | A computationally tractable birth-death model that combines phylogenetic and epidemiological data |
title_full_unstemmed | A computationally tractable birth-death model that combines phylogenetic and epidemiological data |
title_short | A computationally tractable birth-death model that combines phylogenetic and epidemiological data |
title_sort | computationally tractable birth-death model that combines phylogenetic and epidemiological data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8903285/ https://www.ncbi.nlm.nih.gov/pubmed/35148311 http://dx.doi.org/10.1371/journal.pcbi.1009805 |
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