Inference of epidemiological dynamics based on simulated phylogenies using birth-death and coalescent models

Quantifying epidemiological dynamics is crucial for understanding and forecasting the spread of an epidemic. The coalescent and the birth-death model are used interchangeably to infer epidemiological parameters from the genealogical relationships of the pathogen population under study, which in turn...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:PLoS computational biology 2014-11, Vol.10 (11), p.e1003913-e1003913
Hauptverfasser: Boskova, Veronika, Bonhoeffer, Sebastian, Stadler, Tanja
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page e1003913
container_issue 11
container_start_page e1003913
container_title PLoS computational biology
container_volume 10
creator Boskova, Veronika
Bonhoeffer, Sebastian
Stadler, Tanja
description Quantifying epidemiological dynamics is crucial for understanding and forecasting the spread of an epidemic. The coalescent and the birth-death model are used interchangeably to infer epidemiological parameters from the genealogical relationships of the pathogen population under study, which in turn are inferred from the pathogen genetic sequencing data. To compare the performance of these widely applied models, we performed a simulation study. We simulated phylogenetic trees under the constant rate birth-death model and the coalescent model with a deterministic exponentially growing infected population. For each tree, we re-estimated the epidemiological parameters using both a birth-death and a coalescent based method, implemented as an MCMC procedure in BEAST v2.0. In our analyses that estimate the growth rate of an epidemic based on simulated birth-death trees, the point estimates such as the maximum a posteriori/maximum likelihood estimates are not very different. However, the estimates of uncertainty are very different. The birth-death model had a higher coverage than the coalescent model, i.e. contained the true value in the highest posterior density (HPD) interval more often (2-13% vs. 31-75% error). The coverage of the coalescent decreases with decreasing basic reproductive ratio and increasing sampling probability of infecteds. We hypothesize that the biases in the coalescent are due to the assumption of deterministic rather than stochastic population size changes. Both methods performed reasonably well when analyzing trees simulated under the coalescent. The methods can also identify other key epidemiological parameters as long as one of the parameters is fixed to its true value. In summary, when using genetic data to estimate epidemic dynamics, our results suggest that the birth-death method will be less sensitive to population fluctuations of early outbreaks than the coalescent method that assumes a deterministic exponentially growing infected population.
doi_str_mv 10.1371/journal.pcbi.1003913
format Article
fullrecord <record><control><sourceid>proquest_plos_</sourceid><recordid>TN_cdi_plos_journals_1685033153</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_28e54ddc671047c9b11249a03f2a2f6f</doaj_id><sourcerecordid>1622063449</sourcerecordid><originalsourceid>FETCH-LOGICAL-c498t-57c0f24b9bc4259b6446f72e4b3917c36ab3041669d67bf1d345ad36327717013</originalsourceid><addsrcrecordid>eNpVUstu1DAUjRCItgN_gMBLNhn8drJBQhXQkSqxgbXlV2Y8cuxgJ0jz9_UwadWu_Drn3HuuT9N8QHCLiEBfjmnJUYXtZLTfIghJj8ir5hoxRlpBWPf62f6quSnlWDGs6_nb5gozIljlXDdhFweXXTQOpAG4yVs3-hTS3hsVgD1FNXpTgFbFWZAiKH5cgprrYTqcKsxF7wpYio97oH2eD611aj4AFS0wSQVXjIszGJN1obxr3gwqFPd-XTfNnx_ff9_etfe_fu5uv923hvbd3DJh4ICp7rWhmPWaU8oHgR3V1aMwhCtNIEWc95YLPSBLKFOWcIKFQAIismk-XXSnkIpcB1Uk4h2DhCBGKmJ3QdikjnLKflT5JJPy8v9Fynup8uxNcBJ3jlFrDRcIUmF6jRCmvYJkwAoPfKhaX9dqix6dPfvNKrwQffkS_UHu0z9JMca8ftGm-bwK5PR3cWWWo69jC0FFl5Zz3xhDTijtK5ReoCanUrIbnsogKM-xeHQrz7GQaywq7ePzFp9IjzkgD7y9tz4</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1622063449</pqid></control><display><type>article</type><title>Inference of epidemiological dynamics based on simulated phylogenies using birth-death and coalescent models</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Public Library of Science (PLoS) Journals Open Access</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><creator>Boskova, Veronika ; Bonhoeffer, Sebastian ; Stadler, Tanja</creator><contributor>Koelle, Katia</contributor><creatorcontrib>Boskova, Veronika ; Bonhoeffer, Sebastian ; Stadler, Tanja ; Koelle, Katia</creatorcontrib><description>Quantifying epidemiological dynamics is crucial for understanding and forecasting the spread of an epidemic. The coalescent and the birth-death model are used interchangeably to infer epidemiological parameters from the genealogical relationships of the pathogen population under study, which in turn are inferred from the pathogen genetic sequencing data. To compare the performance of these widely applied models, we performed a simulation study. We simulated phylogenetic trees under the constant rate birth-death model and the coalescent model with a deterministic exponentially growing infected population. For each tree, we re-estimated the epidemiological parameters using both a birth-death and a coalescent based method, implemented as an MCMC procedure in BEAST v2.0. In our analyses that estimate the growth rate of an epidemic based on simulated birth-death trees, the point estimates such as the maximum a posteriori/maximum likelihood estimates are not very different. However, the estimates of uncertainty are very different. The birth-death model had a higher coverage than the coalescent model, i.e. contained the true value in the highest posterior density (HPD) interval more often (2-13% vs. 31-75% error). The coverage of the coalescent decreases with decreasing basic reproductive ratio and increasing sampling probability of infecteds. We hypothesize that the biases in the coalescent are due to the assumption of deterministic rather than stochastic population size changes. Both methods performed reasonably well when analyzing trees simulated under the coalescent. The methods can also identify other key epidemiological parameters as long as one of the parameters is fixed to its true value. In summary, when using genetic data to estimate epidemic dynamics, our results suggest that the birth-death method will be less sensitive to population fluctuations of early outbreaks than the coalescent method that assumes a deterministic exponentially growing infected population.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1003913</identifier><identifier>PMID: 25375100</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Biology and Life Sciences ; Birth Rate ; Computational Biology ; Computer Simulation ; Epidemiologic Methods ; Epidemiology ; Estimates ; Growth rate ; Humans ; Mathematical models ; Medicine and Health Sciences ; Methods ; Models, Biological ; Mortality ; Phylogenetics ; Phylogeny ; Population ; Population Dynamics ; Probability ; Trees</subject><ispartof>PLoS computational biology, 2014-11, Vol.10 (11), p.e1003913-e1003913</ispartof><rights>2014 Boskova et al 2014 Boskova et al</rights><rights>2014 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Boskova V, Bonhoeffer S, Stadler T (2014) Inference of Epidemiological Dynamics Based on Simulated Phylogenies Using Birth-Death and Coalescent Models. PLoS Comput Biol 10(11): e1003913. doi:10.1371/journal.pcbi.1003913</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c498t-57c0f24b9bc4259b6446f72e4b3917c36ab3041669d67bf1d345ad36327717013</citedby><cites>FETCH-LOGICAL-c498t-57c0f24b9bc4259b6446f72e4b3917c36ab3041669d67bf1d345ad36327717013</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4222655/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4222655/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25375100$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Koelle, Katia</contributor><creatorcontrib>Boskova, Veronika</creatorcontrib><creatorcontrib>Bonhoeffer, Sebastian</creatorcontrib><creatorcontrib>Stadler, Tanja</creatorcontrib><title>Inference of epidemiological dynamics based on simulated phylogenies using birth-death and coalescent models</title><title>PLoS computational biology</title><addtitle>PLoS Comput Biol</addtitle><description>Quantifying epidemiological dynamics is crucial for understanding and forecasting the spread of an epidemic. The coalescent and the birth-death model are used interchangeably to infer epidemiological parameters from the genealogical relationships of the pathogen population under study, which in turn are inferred from the pathogen genetic sequencing data. To compare the performance of these widely applied models, we performed a simulation study. We simulated phylogenetic trees under the constant rate birth-death model and the coalescent model with a deterministic exponentially growing infected population. For each tree, we re-estimated the epidemiological parameters using both a birth-death and a coalescent based method, implemented as an MCMC procedure in BEAST v2.0. In our analyses that estimate the growth rate of an epidemic based on simulated birth-death trees, the point estimates such as the maximum a posteriori/maximum likelihood estimates are not very different. However, the estimates of uncertainty are very different. The birth-death model had a higher coverage than the coalescent model, i.e. contained the true value in the highest posterior density (HPD) interval more often (2-13% vs. 31-75% error). The coverage of the coalescent decreases with decreasing basic reproductive ratio and increasing sampling probability of infecteds. We hypothesize that the biases in the coalescent are due to the assumption of deterministic rather than stochastic population size changes. Both methods performed reasonably well when analyzing trees simulated under the coalescent. The methods can also identify other key epidemiological parameters as long as one of the parameters is fixed to its true value. In summary, when using genetic data to estimate epidemic dynamics, our results suggest that the birth-death method will be less sensitive to population fluctuations of early outbreaks than the coalescent method that assumes a deterministic exponentially growing infected population.</description><subject>Biology and Life Sciences</subject><subject>Birth Rate</subject><subject>Computational Biology</subject><subject>Computer Simulation</subject><subject>Epidemiologic Methods</subject><subject>Epidemiology</subject><subject>Estimates</subject><subject>Growth rate</subject><subject>Humans</subject><subject>Mathematical models</subject><subject>Medicine and Health Sciences</subject><subject>Methods</subject><subject>Models, Biological</subject><subject>Mortality</subject><subject>Phylogenetics</subject><subject>Phylogeny</subject><subject>Population</subject><subject>Population Dynamics</subject><subject>Probability</subject><subject>Trees</subject><issn>1553-7358</issn><issn>1553-734X</issn><issn>1553-7358</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>DOA</sourceid><recordid>eNpVUstu1DAUjRCItgN_gMBLNhn8drJBQhXQkSqxgbXlV2Y8cuxgJ0jz9_UwadWu_Drn3HuuT9N8QHCLiEBfjmnJUYXtZLTfIghJj8ir5hoxRlpBWPf62f6quSnlWDGs6_nb5gozIljlXDdhFweXXTQOpAG4yVs3-hTS3hsVgD1FNXpTgFbFWZAiKH5cgprrYTqcKsxF7wpYio97oH2eD611aj4AFS0wSQVXjIszGJN1obxr3gwqFPd-XTfNnx_ff9_etfe_fu5uv923hvbd3DJh4ICp7rWhmPWaU8oHgR3V1aMwhCtNIEWc95YLPSBLKFOWcIKFQAIismk-XXSnkIpcB1Uk4h2DhCBGKmJ3QdikjnLKflT5JJPy8v9Fynup8uxNcBJ3jlFrDRcIUmF6jRCmvYJkwAoPfKhaX9dqix6dPfvNKrwQffkS_UHu0z9JMca8ftGm-bwK5PR3cWWWo69jC0FFl5Zz3xhDTijtK5ReoCanUrIbnsogKM-xeHQrz7GQaywq7ePzFp9IjzkgD7y9tz4</recordid><startdate>20141101</startdate><enddate>20141101</enddate><creator>Boskova, Veronika</creator><creator>Bonhoeffer, Sebastian</creator><creator>Stadler, Tanja</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20141101</creationdate><title>Inference of epidemiological dynamics based on simulated phylogenies using birth-death and coalescent models</title><author>Boskova, Veronika ; Bonhoeffer, Sebastian ; Stadler, Tanja</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c498t-57c0f24b9bc4259b6446f72e4b3917c36ab3041669d67bf1d345ad36327717013</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Biology and Life Sciences</topic><topic>Birth Rate</topic><topic>Computational Biology</topic><topic>Computer Simulation</topic><topic>Epidemiologic Methods</topic><topic>Epidemiology</topic><topic>Estimates</topic><topic>Growth rate</topic><topic>Humans</topic><topic>Mathematical models</topic><topic>Medicine and Health Sciences</topic><topic>Methods</topic><topic>Models, Biological</topic><topic>Mortality</topic><topic>Phylogenetics</topic><topic>Phylogeny</topic><topic>Population</topic><topic>Population Dynamics</topic><topic>Probability</topic><topic>Trees</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Boskova, Veronika</creatorcontrib><creatorcontrib>Bonhoeffer, Sebastian</creatorcontrib><creatorcontrib>Stadler, Tanja</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PLoS computational biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Boskova, Veronika</au><au>Bonhoeffer, Sebastian</au><au>Stadler, Tanja</au><au>Koelle, Katia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Inference of epidemiological dynamics based on simulated phylogenies using birth-death and coalescent models</atitle><jtitle>PLoS computational biology</jtitle><addtitle>PLoS Comput Biol</addtitle><date>2014-11-01</date><risdate>2014</risdate><volume>10</volume><issue>11</issue><spage>e1003913</spage><epage>e1003913</epage><pages>e1003913-e1003913</pages><issn>1553-7358</issn><issn>1553-734X</issn><eissn>1553-7358</eissn><abstract>Quantifying epidemiological dynamics is crucial for understanding and forecasting the spread of an epidemic. The coalescent and the birth-death model are used interchangeably to infer epidemiological parameters from the genealogical relationships of the pathogen population under study, which in turn are inferred from the pathogen genetic sequencing data. To compare the performance of these widely applied models, we performed a simulation study. We simulated phylogenetic trees under the constant rate birth-death model and the coalescent model with a deterministic exponentially growing infected population. For each tree, we re-estimated the epidemiological parameters using both a birth-death and a coalescent based method, implemented as an MCMC procedure in BEAST v2.0. In our analyses that estimate the growth rate of an epidemic based on simulated birth-death trees, the point estimates such as the maximum a posteriori/maximum likelihood estimates are not very different. However, the estimates of uncertainty are very different. The birth-death model had a higher coverage than the coalescent model, i.e. contained the true value in the highest posterior density (HPD) interval more often (2-13% vs. 31-75% error). The coverage of the coalescent decreases with decreasing basic reproductive ratio and increasing sampling probability of infecteds. We hypothesize that the biases in the coalescent are due to the assumption of deterministic rather than stochastic population size changes. Both methods performed reasonably well when analyzing trees simulated under the coalescent. The methods can also identify other key epidemiological parameters as long as one of the parameters is fixed to its true value. In summary, when using genetic data to estimate epidemic dynamics, our results suggest that the birth-death method will be less sensitive to population fluctuations of early outbreaks than the coalescent method that assumes a deterministic exponentially growing infected population.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>25375100</pmid><doi>10.1371/journal.pcbi.1003913</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1553-7358
ispartof PLoS computational biology, 2014-11, Vol.10 (11), p.e1003913-e1003913
issn 1553-7358
1553-734X
1553-7358
language eng
recordid cdi_plos_journals_1685033153
source MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS) Journals Open Access; EZB-FREE-00999 freely available EZB journals; PubMed Central
subjects Biology and Life Sciences
Birth Rate
Computational Biology
Computer Simulation
Epidemiologic Methods
Epidemiology
Estimates
Growth rate
Humans
Mathematical models
Medicine and Health Sciences
Methods
Models, Biological
Mortality
Phylogenetics
Phylogeny
Population
Population Dynamics
Probability
Trees
title Inference of epidemiological dynamics based on simulated phylogenies using birth-death and coalescent models
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T19%3A41%3A09IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Inference%20of%20epidemiological%20dynamics%20based%20on%20simulated%20phylogenies%20using%20birth-death%20and%20coalescent%20models&rft.jtitle=PLoS%20computational%20biology&rft.au=Boskova,%20Veronika&rft.date=2014-11-01&rft.volume=10&rft.issue=11&rft.spage=e1003913&rft.epage=e1003913&rft.pages=e1003913-e1003913&rft.issn=1553-7358&rft.eissn=1553-7358&rft_id=info:doi/10.1371/journal.pcbi.1003913&rft_dat=%3Cproquest_plos_%3E1622063449%3C/proquest_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1622063449&rft_id=info:pmid/25375100&rft_doaj_id=oai_doaj_org_article_28e54ddc671047c9b11249a03f2a2f6f&rfr_iscdi=true