Estimating pathogen spread using structured coalescent and birth–death models: A quantitative comparison
Elucidating disease spread between subpopulations is crucial in guiding effective disease control efforts. Genomic epidemiology and phylodynamics have emerged as key principles to estimate such spread from pathogen phylogenies derived from molecular data. Two well-established structured phylodynamic...
Saved in:
| Published in: | Epidemics Vol. 49; p. 100795 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Netherlands
Elsevier B.V
01.12.2024
Elsevier |
| Subjects: | |
| ISSN: | 1755-4365, 1878-0067, 1878-0067 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Elucidating disease spread between subpopulations is crucial in guiding effective disease control efforts. Genomic epidemiology and phylodynamics have emerged as key principles to estimate such spread from pathogen phylogenies derived from molecular data. Two well-established structured phylodynamic methodologies – based on the coalescent and the birth–death model – are frequently employed to estimate viral spread between populations. Nonetheless, these methodologies operate under distinct assumptions whose impact on the accuracy of migration rate inference is yet to be thoroughly investigated.
In this manuscript, we present a simulation study, contrasting the inferential outcomes of the structured coalescent model with constant population size and the multitype birth–death model with a constant rate. We explore this comparison across a range of migration rates in endemic diseases and epidemic outbreaks. The results of the epidemic outbreak analysis revealed that the birth–death model exhibits a superior ability to retrieve accurate migration rates compared to the coalescent model, regardless of the actual migration rate. Thus, to estimate accurate migration rates, the population dynamics have to be accounted for. On the other hand, for the endemic disease scenario, our investigation demonstrates that both models produce comparable coverage and accuracy of the migration rates, with the coalescent model generating more precise estimates. Regardless of the specific scenario, both models similarly estimated the source location of the disease.
This research offers tangible modelling advice for infectious disease analysts, suggesting the use of either model for endemic diseases. For epidemic outbreaks, or scenarios with varying population size, structured phylodynamic models relying on the Kingman coalescent with constant population size should be avoided as they can lead to inaccurate estimates of the migration rate. Instead, coalescent models accounting for varying population size or birth–death models should be favoured. Importantly, our study emphasises the value of directly capturing exponential growth dynamics which could be a useful enhancement for structured coalescent models.
•Comparison of structured phylodynamic models for estimating viral spread between populations.•Multi-type birth–death models excel in migration rate accuracy during epidemic outbreaks.•Both birth–death and coalescent models show comparable accuracy for endemic diseases; coalescent is more precise.•Estimating disease source location is more robust than migration rates. |
|---|---|
| AbstractList | AbstractElucidating disease spread between subpopulations is crucial in guiding effective disease control efforts. Genomic epidemiology and phylodynamics have emerged as key principles to estimate such spread from pathogen phylogenies derived from molecular data. Two well-established structured phylodynamic methodologies – based on the coalescent and the birth–death model – are frequently employed to estimate viral spread between populations. Nonetheless, these methodologies operate under distinct assumptions whose impact on the accuracy of migration rate inference is yet to be thoroughly investigated. In this manuscript, we present a simulation study, contrasting the inferential outcomes of the structured coalescent model with constant population size and the multitype birth–death model with a constant rate. We explore this comparison across a range of migration rates in endemic diseases and epidemic outbreaks. The results of the epidemic outbreak analysis revealed that the birth–death model exhibits a superior ability to retrieve accurate migration rates compared to the coalescent model, regardless of the actual migration rate. Thus, to estimate accurate migration rates, the population dynamics have to be accounted for. On the other hand, for the endemic disease scenario, our investigation demonstrates that both models produce comparable coverage and accuracy of the migration rates, with the coalescent model generating more precise estimates. Regardless of the specific scenario, both models similarly estimated the source location of the disease. This research offers tangible modelling advice for infectious disease analysts, suggesting the use of either model for endemic diseases. For epidemic outbreaks, or scenarios with varying population size, structured phylodynamic models relying on the Kingman coalescent with constant population size should be avoided as they can lead to inaccurate estimates of the migration rate. Instead, coalescent models accounting for varying population size or birth–death models should be favoured. Importantly, our study emphasises the value of directly capturing exponential growth dynamics which could be a useful enhancement for structured coalescent models. Elucidating disease spread between subpopulations is crucial in guiding effective disease control efforts. Genomic epidemiology and phylodynamics have emerged as key principles to estimate such spread from pathogen phylogenies derived from molecular data. Two well-established structured phylodynamic methodologies - based on the coalescent and the birth-death model - are frequently employed to estimate viral spread between populations. Nonetheless, these methodologies operate under distinct assumptions whose impact on the accuracy of migration rate inference is yet to be thoroughly investigated. In this manuscript, we present a simulation study, contrasting the inferential outcomes of the structured coalescent model with constant population size and the multitype birth-death model with a constant rate. We explore this comparison across a range of migration rates in endemic diseases and epidemic outbreaks. The results of the epidemic outbreak analysis revealed that the birth-death model exhibits a superior ability to retrieve accurate migration rates compared to the coalescent model, regardless of the actual migration rate. Thus, to estimate accurate migration rates, the population dynamics have to be accounted for. On the other hand, for the endemic disease scenario, our investigation demonstrates that both models produce comparable coverage and accuracy of the migration rates, with the coalescent model generating more precise estimates. Regardless of the specific scenario, both models similarly estimated the source location of the disease. This research offers tangible modelling advice for infectious disease analysts, suggesting the use of either model for endemic diseases. For epidemic outbreaks, or scenarios with varying population size, structured phylodynamic models relying on the Kingman coalescent with constant population size should be avoided as they can lead to inaccurate estimates of the migration rate. Instead, coalescent models accounting for varying population size or birth-death models should be favoured. Importantly, our study emphasises the value of directly capturing exponential growth dynamics which could be a useful enhancement for structured coalescent models.Elucidating disease spread between subpopulations is crucial in guiding effective disease control efforts. Genomic epidemiology and phylodynamics have emerged as key principles to estimate such spread from pathogen phylogenies derived from molecular data. Two well-established structured phylodynamic methodologies - based on the coalescent and the birth-death model - are frequently employed to estimate viral spread between populations. Nonetheless, these methodologies operate under distinct assumptions whose impact on the accuracy of migration rate inference is yet to be thoroughly investigated. In this manuscript, we present a simulation study, contrasting the inferential outcomes of the structured coalescent model with constant population size and the multitype birth-death model with a constant rate. We explore this comparison across a range of migration rates in endemic diseases and epidemic outbreaks. The results of the epidemic outbreak analysis revealed that the birth-death model exhibits a superior ability to retrieve accurate migration rates compared to the coalescent model, regardless of the actual migration rate. Thus, to estimate accurate migration rates, the population dynamics have to be accounted for. On the other hand, for the endemic disease scenario, our investigation demonstrates that both models produce comparable coverage and accuracy of the migration rates, with the coalescent model generating more precise estimates. Regardless of the specific scenario, both models similarly estimated the source location of the disease. This research offers tangible modelling advice for infectious disease analysts, suggesting the use of either model for endemic diseases. For epidemic outbreaks, or scenarios with varying population size, structured phylodynamic models relying on the Kingman coalescent with constant population size should be avoided as they can lead to inaccurate estimates of the migration rate. Instead, coalescent models accounting for varying population size or birth-death models should be favoured. Importantly, our study emphasises the value of directly capturing exponential growth dynamics which could be a useful enhancement for structured coalescent models. Elucidating disease spread between subpopulations is crucial in guiding effective disease control efforts. Genomic epidemiology and phylodynamics have emerged as key principles to estimate such spread from pathogen phylogenies derived from molecular data. Two well-established structured phylodynamic methodologies - based on the coalescent and the birth-death model - are frequently employed to estimate viral spread between populations. Nonetheless, these methodologies operate under distinct assumptions whose impact on the accuracy of migration rate inference is yet to be thoroughly investigated. In this manuscript, we present a simulation study, contrasting the inferential outcomes of the structured coalescent model with constant population size and the multitype birth-death model with a constant rate. We explore this comparison across a range of migration rates in endemic diseases and epidemic outbreaks. The results of the epidemic outbreak analysis revealed that the birth-death model exhibits a superior ability to retrieve accurate migration rates compared to the coalescent model, regardless of the actual migration rate. Thus, to estimate accurate migration rates, the population dynamics have to be accounted for. On the other hand, for the endemic disease scenario, our investigation demonstrates that both models produce comparable coverage and accuracy of the migration rates, with the coalescent model generating more precise estimates. Regardless of the specific scenario, both models similarly estimated the source location of the disease. This research offers tangible modelling advice for infectious disease analysts, suggesting the use of either model for endemic diseases. For epidemic outbreaks, or scenarios with varying population size, structured phylodynamic models relying on the Kingman coalescent with constant population size should be avoided as they can lead to inaccurate estimates of the migration rate. Instead, coalescent models accounting for varying population size or birth-death models should be favoured. Importantly, our study emphasises the value of directly capturing exponential growth dynamics which could be a useful enhancement for structured coalescent models. Elucidating disease spread between subpopulations is crucial in guiding effective disease control efforts. Genomic epidemiology and phylodynamics have emerged as key principles to estimate such spread from pathogen phylogenies derived from molecular data. Two well-established structured phylodynamic methodologies – based on the coalescent and the birth–death model – are frequently employed to estimate viral spread between populations. Nonetheless, these methodologies operate under distinct assumptions whose impact on the accuracy of migration rate inference is yet to be thoroughly investigated. In this manuscript, we present a simulation study, contrasting the inferential outcomes of the structured coalescent model with constant population size and the multitype birth–death model with a constant rate. We explore this comparison across a range of migration rates in endemic diseases and epidemic outbreaks. The results of the epidemic outbreak analysis revealed that the birth–death model exhibits a superior ability to retrieve accurate migration rates compared to the coalescent model, regardless of the actual migration rate. Thus, to estimate accurate migration rates, the population dynamics have to be accounted for. On the other hand, for the endemic disease scenario, our investigation demonstrates that both models produce comparable coverage and accuracy of the migration rates, with the coalescent model generating more precise estimates. Regardless of the specific scenario, both models similarly estimated the source location of the disease. This research offers tangible modelling advice for infectious disease analysts, suggesting the use of either model for endemic diseases. For epidemic outbreaks, or scenarios with varying population size, structured phylodynamic models relying on the Kingman coalescent with constant population size should be avoided as they can lead to inaccurate estimates of the migration rate. Instead, coalescent models accounting for varying population size or birth–death models should be favoured. Importantly, our study emphasises the value of directly capturing exponential growth dynamics which could be a useful enhancement for structured coalescent models. •Comparison of structured phylodynamic models for estimating viral spread between populations.•Multi-type birth–death models excel in migration rate accuracy during epidemic outbreaks.•Both birth–death and coalescent models show comparable accuracy for endemic diseases; coalescent is more precise.•Estimating disease source location is more robust than migration rates. |
| ArticleNumber | 100795 |
| Author | Vaughan, Timothy G. Stadler, Tanja Seidel, Sophie |
| Author_xml | – sequence: 1 givenname: Sophie orcidid: 0000-0002-4484-9888 surname: Seidel fullname: Seidel, Sophie email: sophie.seidel@bsse.ethz.ch – sequence: 2 givenname: Tanja orcidid: 0000-0001-6431-535X surname: Stadler fullname: Stadler, Tanja – sequence: 3 givenname: Timothy G. orcidid: 0000-0001-6220-2239 surname: Vaughan fullname: Vaughan, Timothy G. email: timothy.vaughan@bsse.ethz.ch |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39461051$$D View this record in MEDLINE/PubMed |
| BookMark | eNqVUstu1TAQjVARbS_8AUJZsrkXv32DEFJVFahUiQWwthx7cuuQ2KntVOqu_8Af8iU4pHSHECuPjs85o5kzp9WRDx6q6iVGO4yweNPvYHIWxh1BhBUIyYY_qU7wXu63CAl5VGrJ-ZZRwY-r05T6gjKM6bPqmDZMYMTxSdVfpOxGnZ0_1JPO1-EAvk5TBG3rOS1oynE2eY5gaxP0AMmAz7X2tm5dzNc_739YKMJ6DBaG9LY-q29m7bPLxfQWimacdHQp-OfV004PCV48vJvq24eLr-eftlefP16en11tDac4by1jhmrDpW6oJUK3HKQ0LeeWE97RBiPRgWGNMdw0YIG02iJBLZIG7XmpNtXl6muD7tUUy3jxTgXt1G8gxIPSMTszgNKsRR21lBIumMam3WOy11JYYjqhy9emer16TTHczJCyGl1ZwDBoD2FOimKCiZSMoUJ99UCd2xHsY-M_uy4EthJMDClF6B4pGKklUtWrNVK1RKrWSIvs_Sor24VbB1El48AbsC6CyWUo978GZnDeGT18hztIfZijL3korBJRSH1ZrmY5GsIQQlyQYvDu7wb_7v8LLHDWaw |
| Cites_doi | 10.1371/journal.pcbi.1002947 10.2307/3213548 10.1073/pnas.2012008118 10.1073/pnas.081068098 10.1016/j.jtbi.2010.09.010 10.1126/science.1090727 10.1093/bioinformatics/bty406 10.1534/genetics.111.134627 10.1098/rsif.2014.0945 10.1093/ve/veac073 10.3390/v14081648 10.1371/journal.pcbi.1002136 10.1093/ve/vez030 10.1098/rstb.2012.0198 10.1371/journal.pgen.1005421 10.1371/journal.pcbi.1003913 10.1093/molbev/msaa016 10.1371/journal.pcbi.1003570 10.1093/molbev/msr217 10.1016/j.jtbi.2019.110115 10.1016/j.jtbi.2020.110400 10.1534/genetics.108.092460 10.1371/journal.pcbi.1003537 10.1073/pnas.2211217119 10.1007/BF00173909 10.1093/molbev/mst057 10.1093/molbev/msw064 10.1016/j.epidem.2014.09.001 10.1093/molbev/msx186 10.1093/bioinformatics/btu201 10.1038/s41576-022-00483-8 10.1371/journal.pcbi.1004789 10.1371/journal.pcbi.1000520 10.1002/advs.202100707 10.1111/2041-210X.13620 10.1016/j.jtbi.2009.07.018 |
| ContentType | Journal Article |
| Copyright | 2024 The Authors The Authors Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved. |
| Copyright_xml | – notice: 2024 The Authors – notice: The Authors – notice: Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved. |
| DBID | 6I. AAFTH AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 DOA |
| DOI | 10.1016/j.epidem.2024.100795 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic DOAJ Open Access Full Text |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Public Health |
| EISSN | 1878-0067 |
| EndPage | 100795 |
| ExternalDocumentID | oai_doaj_org_article_a4b0f3d332564a1cb8128a76d2cf6a0f 39461051 10_1016_j_epidem_2024_100795 S1755436524000562 1_s2_0_S1755436524000562 |
| Genre | Journal Article Comparative Study |
| GroupedDBID | --- --K .1- .FO .~1 0R~ 1B1 1P~ 1~. 4.4 457 4G. 53G 5GY 5VS 7-5 71M 8P~ AAEDW AAIKJ AALRI AAQFI AARKO AAXUO AAYWO ABBQC ABGSF ABMAC ABWVN ABXDB ACGFS ACRPL ACVFH ADBBV ADCNI ADEZE ADMUD ADNMO ADQTV ADUVX ADVLN AEKER AENEX AEQOU AEUPX AEVXI AEXQZ AFJKZ AFPUW AFRHN AFTJW AGEKW AGHFR AGYEJ AIGII AITUG AJRQY AJUYK AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ APXCP BCNDV EBS EJD EP2 EP3 F5P FDB FEDTE FIRID FNPLU GBLVA GROUPED_DOAJ HVGLF HZ~ IPNFZ IXB J1W KQ8 LUGTX M41 M48 MO0 N9A O-L O9- OD- OK1 OO. OZT P-8 P-9 PC. Q38 RIG ROL SDF SES SSZ Z5R ~HD 0SF AACTN AFCTW AJOXV NCXOZ 6I. AAFTH 9DU AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 |
| ID | FETCH-LOGICAL-c531t-d44c3ac57a93d26ab5e77cb55d525f39106fec49cc5c9ede2bad063d07c085063 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001347061500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1755-4365 1878-0067 |
| IngestDate | Fri Oct 03 12:48:26 EDT 2025 Sun Sep 28 05:01:00 EDT 2025 Thu Jan 02 22:39:38 EST 2025 Sat Nov 29 01:54:22 EST 2025 Sat Feb 08 15:52:27 EST 2025 Mon Feb 24 20:33:34 EST 2025 Tue Oct 14 19:30:56 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Phylogenetics Coalescent Phylodynamics Pathogen spread Birth–death |
| Language | English |
| License | This is an open access article under the CC BY license. Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c531t-d44c3ac57a93d26ab5e77cb55d525f39106fec49cc5c9ede2bad063d07c085063 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-4484-9888 0000-0001-6220-2239 0000-0001-6431-535X |
| OpenAccessLink | https://doaj.org/article/a4b0f3d332564a1cb8128a76d2cf6a0f |
| PMID | 39461051 |
| PQID | 3121277440 |
| PQPubID | 23479 |
| PageCount | 1 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_a4b0f3d332564a1cb8128a76d2cf6a0f proquest_miscellaneous_3121277440 pubmed_primary_39461051 crossref_primary_10_1016_j_epidem_2024_100795 elsevier_sciencedirect_doi_10_1016_j_epidem_2024_100795 elsevier_clinicalkeyesjournals_1_s2_0_S1755436524000562 elsevier_clinicalkey_doi_10_1016_j_epidem_2024_100795 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-12-01 |
| PublicationDateYYYYMMDD | 2024-12-01 |
| PublicationDate_xml | – month: 12 year: 2024 text: 2024-12-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Netherlands |
| PublicationPlace_xml | – name: Netherlands |
| PublicationTitle | Epidemics |
| PublicationTitleAlternate | Epidemics |
| PublicationYear | 2024 |
| Publisher | Elsevier B.V Elsevier |
| Publisher_xml | – name: Elsevier B.V – name: Elsevier |
| References | Lannelongue, Grealey, Inouye (b18) 2021; 8 Volz, Koelle, Bedford (b42) 2013; 9 Keeling, Rohani (b15) 2011 Müller, Rasmussen, Stadler (b25) 2018; 34 Notohara (b27) 1990; 29 Vaughan, Kühnert, Popinga, Welch, Drummond (b38) 2014; 30 Müller, Bouckaert, Wu, Bedford, Francisco (b22) 2024 Stadler (b34) 2010; 267 Mueller, N., 0000. Taming the beast: MASCOT tutorial. Volz, Frost (b40) 2014; 11 Anderson, May (b1) 1992 Müller, Dudas, Stadler (b23) 2019; 5 Attwood, Hill, Aanensen, Connor, Pybus (b2) 2022; 23 Featherstone, Di Giallonardo, Holmes, Vaughan, Duchêne (b8) 2021; 12 Boskova, Bonhoeffer, Stadler (b4) 2014; 10 Dudas, Carvalho, Rambaut, Bedford (b7) 2018; 7 Müller, Rasmussen, Stadler (b24) 2017; 34 Manceau, Gupta, Vaughan, Stadler (b20) 2021; 509 Stadler, Bonhoeffer (b35) 2013; 368 Grenfell, Pybus, Gog, Wood, Daly, Mumford, Holmes (b10) 2004; 303 Gupta, Manceau, Vaughan, Khammash, Stadler (b12) 2020; 488 Rasmussen, Ratmann, Koelle (b30) 2011; 7 Rasmussen, Volz, Koelle (b31) 2014; 10 Frost, Pybus, Gog, Viboud, Bonhoeffer, Bedford (b9) 2015; 10 Bouckaert, Heled, Kühnert, Vaughan, Wu, Xie, Suchard, Rambaut, Drummond (b5) 2014; 10 Kühnert, Stadler, Vaughan, Drummond (b17) 2016; 33 Beerli, Felsenstein (b3) 2001; 98 Nadeau, Vaughan, Scire, Huisman, Stadler (b26) 2021; 118 Notohara (b28) 1990; 29 Stadler (b33) 2009; 261 Hudson (b13) 1990; 7 Vaughan, Drummond (b37) 2013; 30 Parag, du Plessis, Pybus (b29) 2020; 37 De Maio, Wu, O’Reilly, Wilson (b6) 2015; 11 Stadler, Kouyos, VonWy, Yerly, Böni, Bürgisser, Klimkait, Joos, Rieder, Xie, Günthard, Drummond, Bonhoeffer (b36) 2012; 29 Scire, Barido-Sottani, Kühnert, Vaughan, Stadler (b32) 2022; 14 Volz, Frost (b41) 2014; 11 Wakeley, Sargsyan (b43) 2009; 181 Guinat, Agüí, Vaughan, Scire, Pohlmann, Staubach, King, Świȩton, Dán, Černíková, Ducatez, Stadler (b11) 2022; 8 Lemey, Rambaut, Drummond, Suchard (b19) 2009; 5 Karcher, Palacios, Bedford, Suchard, Minin (b14) 2016; 12 Yebra, Harling-Lee, Lycett, Aarestrup, Larsen, Cavaco, Seo, Abraham, Norris, Schmidt, Ehlers, Sordelli, Buzzola, Gebreyes, Gonçalves, dos Santos, Zakaria, Rall, Keane, Niedziela, Paterson, Holmes, Freeman, Fitzgerald (b44) 2022; 119 Kingman (b16) 1982; 19 Volz (b39) 2012; 190 Attwood (10.1016/j.epidem.2024.100795_b2) 2022; 23 Lannelongue (10.1016/j.epidem.2024.100795_b18) 2021; 8 Müller (10.1016/j.epidem.2024.100795_b25) 2018; 34 Guinat (10.1016/j.epidem.2024.100795_b11) 2022; 8 Rasmussen (10.1016/j.epidem.2024.100795_b31) 2014; 10 Kingman (10.1016/j.epidem.2024.100795_b16) 1982; 19 Notohara (10.1016/j.epidem.2024.100795_b27) 1990; 29 Grenfell (10.1016/j.epidem.2024.100795_b10) 2004; 303 Beerli (10.1016/j.epidem.2024.100795_b3) 2001; 98 Wakeley (10.1016/j.epidem.2024.100795_b43) 2009; 181 Gupta (10.1016/j.epidem.2024.100795_b12) 2020; 488 Müller (10.1016/j.epidem.2024.100795_b22) 2024 Stadler (10.1016/j.epidem.2024.100795_b34) 2010; 267 Lemey (10.1016/j.epidem.2024.100795_b19) 2009; 5 Volz (10.1016/j.epidem.2024.100795_b39) 2012; 190 Frost (10.1016/j.epidem.2024.100795_b9) 2015; 10 Müller (10.1016/j.epidem.2024.100795_b23) 2019; 5 Volz (10.1016/j.epidem.2024.100795_b40) 2014; 11 Dudas (10.1016/j.epidem.2024.100795_b7) 2018; 7 Vaughan (10.1016/j.epidem.2024.100795_b38) 2014; 30 Hudson (10.1016/j.epidem.2024.100795_b13) 1990; 7 Stadler (10.1016/j.epidem.2024.100795_b33) 2009; 261 Volz (10.1016/j.epidem.2024.100795_b42) 2013; 9 Kühnert (10.1016/j.epidem.2024.100795_b17) 2016; 33 Featherstone (10.1016/j.epidem.2024.100795_b8) 2021; 12 Boskova (10.1016/j.epidem.2024.100795_b4) 2014; 10 Rasmussen (10.1016/j.epidem.2024.100795_b30) 2011; 7 Stadler (10.1016/j.epidem.2024.100795_b35) 2013; 368 De Maio (10.1016/j.epidem.2024.100795_b6) 2015; 11 Vaughan (10.1016/j.epidem.2024.100795_b37) 2013; 30 Anderson (10.1016/j.epidem.2024.100795_b1) 1992 Manceau (10.1016/j.epidem.2024.100795_b20) 2021; 509 Nadeau (10.1016/j.epidem.2024.100795_b26) 2021; 118 Müller (10.1016/j.epidem.2024.100795_b24) 2017; 34 Bouckaert (10.1016/j.epidem.2024.100795_b5) 2014; 10 Keeling (10.1016/j.epidem.2024.100795_b15) 2011 Volz (10.1016/j.epidem.2024.100795_b41) 2014; 11 Notohara (10.1016/j.epidem.2024.100795_b28) 1990; 29 10.1016/j.epidem.2024.100795_b21 Karcher (10.1016/j.epidem.2024.100795_b14) 2016; 12 Stadler (10.1016/j.epidem.2024.100795_b36) 2012; 29 Yebra (10.1016/j.epidem.2024.100795_b44) 2022; 119 Parag (10.1016/j.epidem.2024.100795_b29) 2020; 37 Scire (10.1016/j.epidem.2024.100795_b32) 2022; 14 |
| References_xml | – volume: 118 year: 2021 ident: b26 article-title: The origin and early spread of SARS-CoV-2 in Europe publication-title: Proc. Natl. Acad. Sci. USA – volume: 37 start-page: 2414 year: 2020 end-page: 2429 ident: b29 article-title: Jointly inferring the dynamics of population size and sampling intensity from molecular sequences publication-title: Mol. Biol. Evol. – volume: 190 start-page: 187 year: 2012 end-page: 201 ident: b39 article-title: Complex population dynamics and the coalescent under neutrality publication-title: Genetics – volume: 8 year: 2022 ident: b11 article-title: Disentangling the role of poultry farms and wild birds in the spread of highly pathogenic avian influenza virus in Europe publication-title: Virus Evol. – year: 2024 ident: b22 article-title: MASCOT-Skyline integrates population and migration dynamics to enhance phylogeographic reconstructions – volume: 10 start-page: 88 year: 2015 end-page: 92 ident: b9 article-title: Eight challenges in phylodynamic inference publication-title: Epidemics – volume: 267 start-page: 396 year: 2010 end-page: 404 ident: b34 article-title: Sampling-through-time in birth–death trees publication-title: J. Theoret. Biol. – volume: 181 start-page: 341 year: 2009 end-page: 345 ident: b43 article-title: Extensions of the coalescent effective population size publication-title: Genetics – volume: 98 start-page: 4563 year: 2001 end-page: 4568 ident: b3 article-title: Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach publication-title: Proc. Natl. Acad. Sci. – volume: 7 start-page: 1 year: 1990 end-page: 44 ident: b13 article-title: Gene genealogies and the coalescent process publication-title: Oxford Surv. Evol. Biol. – volume: 12 year: 2016 ident: b14 article-title: Quantifying and mitigating the effect of preferential sampling on phylodynamic inference publication-title: PLoS Comput. Biol. – volume: 368 year: 2013 ident: b35 article-title: Uncovering epidemiological dynamics in heterogeneous host populations using phylogenetic methods publication-title: Phil. Trans. R. Soc. B – volume: 509 year: 2021 ident: b20 article-title: The probability distribution of the ancestral population size conditioned on the reconstructed phylogenetic tree with occurrence data publication-title: J. Theoret. Biol. – year: 1992 ident: b1 article-title: Infectious Diseases of Humans: Dynamics and Control (Oxford Science Publications) – volume: 10 year: 2014 ident: b5 article-title: BEAST 2: A software platform for Bayesian evolutionary analysis publication-title: PLoS Comput. Biol. – volume: 11 start-page: 1 year: 2015 end-page: 22 ident: b6 article-title: New routes to phylogeography: A Bayesian structured coalescent approximation publication-title: PLoS Genet. – volume: 29 start-page: 59 year: 1990 end-page: 75 ident: b28 article-title: The coalescent and the genealogical process in geographically structured population publication-title: J. Math. Biol. – volume: 488 year: 2020 ident: b12 article-title: The probability distribution of the reconstructed phylogenetic tree with occurrence data publication-title: J. Theoret. Biol. – volume: 30 start-page: 1480 year: 2013 end-page: 1493 ident: b37 article-title: A stochastic simulator of birth-death master equations with application to phylodynamics publication-title: Mol. Biol. Evol. – volume: 11 year: 2014 ident: b40 article-title: Sampling through time and phylodynamic inference with coalescent and birth-death models publication-title: J. Royal Soc. Interface – year: 2011 ident: b15 article-title: Modeling Infectious Diseases in Humans and Animals – volume: 7 year: 2018 ident: b7 article-title: MERS-CoV spillover at the camel-human interface publication-title: eLife – volume: 7 year: 2011 ident: b30 article-title: Inference for nonlinear epidemiological models using genealogies and time series publication-title: PLoS Comput. Biol. – volume: 19 start-page: 27 year: 1982 end-page: 43 ident: b16 article-title: On the genealogy of large populations publication-title: J. Appl. Probab. – volume: 5 year: 2019 ident: b23 article-title: Inferring time-dependent migration and coalescence patterns from genetic sequence and predictor data in structured populations publication-title: Virus Evol. – volume: 8 year: 2021 ident: b18 article-title: Green algorithms: Quantifying the carbon footprint of computation publication-title: Adv. Sci. – volume: 30 start-page: 2272 year: 2014 end-page: 2279 ident: b38 article-title: Efficient Bayesian inference under the structured coalescent publication-title: Bioinformatics – volume: 303 start-page: 327 year: 2004 end-page: 332 ident: b10 article-title: Unifying the epidemiological and evolutionary dynamics of pathogens publication-title: Science – volume: 14 year: 2022 ident: b32 article-title: Robust phylodynamic analysis of genetic sequencing data from structured populations publication-title: Viruses – volume: 119 year: 2022 ident: b44 article-title: Multiclonal human origin and global expansion of an endemic bacterial pathogen of livestock publication-title: Proc. Natl. Acad. Sci. USA – volume: 34 start-page: 3843 year: 2018 end-page: 3848 ident: b25 article-title: MASCOT: parameter and state inference under the marginal structured coalescent approximation publication-title: Bioinformatics – volume: 10 year: 2014 ident: b4 article-title: Inference of epidemiological dynamics based on simulated phylogenies using birth-death and coalescent models publication-title: PLoS Comput. Biol. – volume: 33 start-page: 2102 year: 2016 end-page: 2116 ident: b17 article-title: Phylodynamics with migration: A computational framework to quantify population structure from genomic data publication-title: Mol. Biol. Evol. – volume: 12 start-page: 1498 year: 2021 end-page: 1507 ident: b8 article-title: Infectious disease phylodynamics with occurrence data publication-title: Methods Ecol. Evol. – volume: 5 year: 2009 ident: b19 article-title: Bayesian phylogeography finds its roots publication-title: PLoS Comput. Biol. – volume: 261 start-page: 58 year: 2009 end-page: 66 ident: b33 article-title: On incomplete sampling under birth-death models and connections to the sampling-based coalescent publication-title: J. Theoret. Biol. – reference: Mueller, N., 0000. Taming the beast: MASCOT tutorial. – volume: 9 year: 2013 ident: b42 article-title: Viral Phylodynamics publication-title: PLoS Comput. Biol. – volume: 34 start-page: 2970 year: 2017 end-page: 2981 ident: b24 article-title: The structured coalescent and its approximations publication-title: Mol. Biol. Evol. – volume: 29 start-page: 59 year: 1990 end-page: 75 ident: b27 article-title: The coalescent and the genealogical process in geographically structured population publication-title: J. Math. Biol. – volume: 23 start-page: 547 year: 2022 end-page: 562 ident: b2 article-title: Phylogenetic and phylodynamic approaches to understanding and combating the early SARS-CoV-2 pandemic publication-title: Nature Rev. Genet. – volume: 10 year: 2014 ident: b31 article-title: Phylodynamic inference for structured epidemiological models publication-title: PLoS Comput. Biol. – volume: 11 year: 2014 ident: b41 article-title: Sampling through time and phylodynamic inference with coalescent and birth-death models publication-title: J. R. Soc. Interface – volume: 29 start-page: 347 year: 2012 end-page: 357 ident: b36 article-title: Estimating the basic reproductive number from viral sequence data publication-title: Mol. Biol. Evol. – year: 2011 ident: 10.1016/j.epidem.2024.100795_b15 – volume: 9 issue: 3 year: 2013 ident: 10.1016/j.epidem.2024.100795_b42 article-title: Viral Phylodynamics publication-title: PLoS Comput. Biol. doi: 10.1371/journal.pcbi.1002947 – volume: 19 start-page: 27 issue: A year: 1982 ident: 10.1016/j.epidem.2024.100795_b16 article-title: On the genealogy of large populations publication-title: J. Appl. Probab. doi: 10.2307/3213548 – volume: 118 issue: 9 year: 2021 ident: 10.1016/j.epidem.2024.100795_b26 article-title: The origin and early spread of SARS-CoV-2 in Europe publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.2012008118 – volume: 7 year: 2018 ident: 10.1016/j.epidem.2024.100795_b7 article-title: MERS-CoV spillover at the camel-human interface publication-title: eLife – volume: 98 start-page: 4563 issue: 8 year: 2001 ident: 10.1016/j.epidem.2024.100795_b3 article-title: Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.081068098 – volume: 267 start-page: 396 issue: 3 year: 2010 ident: 10.1016/j.epidem.2024.100795_b34 article-title: Sampling-through-time in birth–death trees publication-title: J. Theoret. Biol. doi: 10.1016/j.jtbi.2010.09.010 – volume: 303 start-page: 327 issue: 5656 year: 2004 ident: 10.1016/j.epidem.2024.100795_b10 article-title: Unifying the epidemiological and evolutionary dynamics of pathogens publication-title: Science doi: 10.1126/science.1090727 – volume: 34 start-page: 3843 issue: 22 year: 2018 ident: 10.1016/j.epidem.2024.100795_b25 article-title: MASCOT: parameter and state inference under the marginal structured coalescent approximation publication-title: Bioinformatics doi: 10.1093/bioinformatics/bty406 – volume: 190 start-page: 187 issue: 1 year: 2012 ident: 10.1016/j.epidem.2024.100795_b39 article-title: Complex population dynamics and the coalescent under neutrality publication-title: Genetics doi: 10.1534/genetics.111.134627 – volume: 11 issue: 101 year: 2014 ident: 10.1016/j.epidem.2024.100795_b41 article-title: Sampling through time and phylodynamic inference with coalescent and birth-death models publication-title: J. R. Soc. Interface doi: 10.1098/rsif.2014.0945 – volume: 8 issue: 2 year: 2022 ident: 10.1016/j.epidem.2024.100795_b11 article-title: Disentangling the role of poultry farms and wild birds in the spread of highly pathogenic avian influenza virus in Europe publication-title: Virus Evol. doi: 10.1093/ve/veac073 – volume: 14 year: 2022 ident: 10.1016/j.epidem.2024.100795_b32 article-title: Robust phylodynamic analysis of genetic sequencing data from structured populations publication-title: Viruses doi: 10.3390/v14081648 – volume: 11 issue: 101 year: 2014 ident: 10.1016/j.epidem.2024.100795_b40 article-title: Sampling through time and phylodynamic inference with coalescent and birth-death models publication-title: J. Royal Soc. Interface doi: 10.1098/rsif.2014.0945 – volume: 7 issue: 8 year: 2011 ident: 10.1016/j.epidem.2024.100795_b30 article-title: Inference for nonlinear epidemiological models using genealogies and time series publication-title: PLoS Comput. Biol. doi: 10.1371/journal.pcbi.1002136 – volume: 5 issue: 2 year: 2019 ident: 10.1016/j.epidem.2024.100795_b23 article-title: Inferring time-dependent migration and coalescence patterns from genetic sequence and predictor data in structured populations publication-title: Virus Evol. doi: 10.1093/ve/vez030 – volume: 368 issue: 1614 year: 2013 ident: 10.1016/j.epidem.2024.100795_b35 article-title: Uncovering epidemiological dynamics in heterogeneous host populations using phylogenetic methods publication-title: Phil. Trans. R. Soc. B doi: 10.1098/rstb.2012.0198 – volume: 11 start-page: 1 issue: 8 year: 2015 ident: 10.1016/j.epidem.2024.100795_b6 article-title: New routes to phylogeography: A Bayesian structured coalescent approximation publication-title: PLoS Genet. doi: 10.1371/journal.pgen.1005421 – volume: 10 issue: 11 year: 2014 ident: 10.1016/j.epidem.2024.100795_b4 article-title: Inference of epidemiological dynamics based on simulated phylogenies using birth-death and coalescent models publication-title: PLoS Comput. Biol. doi: 10.1371/journal.pcbi.1003913 – volume: 37 start-page: 2414 issue: 8 year: 2020 ident: 10.1016/j.epidem.2024.100795_b29 article-title: Jointly inferring the dynamics of population size and sampling intensity from molecular sequences publication-title: Mol. Biol. Evol. doi: 10.1093/molbev/msaa016 – volume: 10 issue: 4 year: 2014 ident: 10.1016/j.epidem.2024.100795_b31 article-title: Phylodynamic inference for structured epidemiological models publication-title: PLoS Comput. Biol. doi: 10.1371/journal.pcbi.1003570 – volume: 29 start-page: 347 issue: 1 year: 2012 ident: 10.1016/j.epidem.2024.100795_b36 article-title: Estimating the basic reproductive number from viral sequence data publication-title: Mol. Biol. Evol. doi: 10.1093/molbev/msr217 – volume: 488 year: 2020 ident: 10.1016/j.epidem.2024.100795_b12 article-title: The probability distribution of the reconstructed phylogenetic tree with occurrence data publication-title: J. Theoret. Biol. doi: 10.1016/j.jtbi.2019.110115 – volume: 7 start-page: 1 year: 1990 ident: 10.1016/j.epidem.2024.100795_b13 article-title: Gene genealogies and the coalescent process publication-title: Oxford Surv. Evol. Biol. – volume: 509 year: 2021 ident: 10.1016/j.epidem.2024.100795_b20 article-title: The probability distribution of the ancestral population size conditioned on the reconstructed phylogenetic tree with occurrence data publication-title: J. Theoret. Biol. doi: 10.1016/j.jtbi.2020.110400 – volume: 181 start-page: 341 year: 2009 ident: 10.1016/j.epidem.2024.100795_b43 article-title: Extensions of the coalescent effective population size publication-title: Genetics doi: 10.1534/genetics.108.092460 – volume: 10 issue: 4 year: 2014 ident: 10.1016/j.epidem.2024.100795_b5 article-title: BEAST 2: A software platform for Bayesian evolutionary analysis publication-title: PLoS Comput. Biol. doi: 10.1371/journal.pcbi.1003537 – ident: 10.1016/j.epidem.2024.100795_b21 – volume: 119 issue: 50 year: 2022 ident: 10.1016/j.epidem.2024.100795_b44 article-title: Multiclonal human origin and global expansion of an endemic bacterial pathogen of livestock publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.2211217119 – volume: 29 start-page: 59 issue: 1 year: 1990 ident: 10.1016/j.epidem.2024.100795_b27 article-title: The coalescent and the genealogical process in geographically structured population publication-title: J. Math. Biol. doi: 10.1007/BF00173909 – year: 2024 ident: 10.1016/j.epidem.2024.100795_b22 – volume: 30 start-page: 1480 issue: 6 year: 2013 ident: 10.1016/j.epidem.2024.100795_b37 article-title: A stochastic simulator of birth-death master equations with application to phylodynamics publication-title: Mol. Biol. Evol. doi: 10.1093/molbev/mst057 – volume: 33 start-page: 2102 issue: 8 year: 2016 ident: 10.1016/j.epidem.2024.100795_b17 article-title: Phylodynamics with migration: A computational framework to quantify population structure from genomic data publication-title: Mol. Biol. Evol. doi: 10.1093/molbev/msw064 – year: 1992 ident: 10.1016/j.epidem.2024.100795_b1 – volume: 10 start-page: 88 year: 2015 ident: 10.1016/j.epidem.2024.100795_b9 article-title: Eight challenges in phylodynamic inference publication-title: Epidemics doi: 10.1016/j.epidem.2014.09.001 – volume: 34 start-page: 2970 issue: 11 year: 2017 ident: 10.1016/j.epidem.2024.100795_b24 article-title: The structured coalescent and its approximations publication-title: Mol. Biol. Evol. doi: 10.1093/molbev/msx186 – volume: 30 start-page: 2272 issue: 16 year: 2014 ident: 10.1016/j.epidem.2024.100795_b38 article-title: Efficient Bayesian inference under the structured coalescent publication-title: Bioinformatics doi: 10.1093/bioinformatics/btu201 – volume: 23 start-page: 547 issue: 9 year: 2022 ident: 10.1016/j.epidem.2024.100795_b2 article-title: Phylogenetic and phylodynamic approaches to understanding and combating the early SARS-CoV-2 pandemic publication-title: Nature Rev. Genet. doi: 10.1038/s41576-022-00483-8 – volume: 12 issue: 3 year: 2016 ident: 10.1016/j.epidem.2024.100795_b14 article-title: Quantifying and mitigating the effect of preferential sampling on phylodynamic inference publication-title: PLoS Comput. Biol. doi: 10.1371/journal.pcbi.1004789 – volume: 5 issue: 9 year: 2009 ident: 10.1016/j.epidem.2024.100795_b19 article-title: Bayesian phylogeography finds its roots publication-title: PLoS Comput. Biol. doi: 10.1371/journal.pcbi.1000520 – volume: 8 year: 2021 ident: 10.1016/j.epidem.2024.100795_b18 article-title: Green algorithms: Quantifying the carbon footprint of computation publication-title: Adv. Sci. doi: 10.1002/advs.202100707 – volume: 12 start-page: 1498 issue: 8 year: 2021 ident: 10.1016/j.epidem.2024.100795_b8 article-title: Infectious disease phylodynamics with occurrence data publication-title: Methods Ecol. Evol. doi: 10.1111/2041-210X.13620 – volume: 29 start-page: 59 issue: 1 year: 1990 ident: 10.1016/j.epidem.2024.100795_b28 article-title: The coalescent and the genealogical process in geographically structured population publication-title: J. Math. Biol. doi: 10.1007/BF00173909 – volume: 261 start-page: 58 issue: 1 year: 2009 ident: 10.1016/j.epidem.2024.100795_b33 article-title: On incomplete sampling under birth-death models and connections to the sampling-based coalescent publication-title: J. Theoret. Biol. doi: 10.1016/j.jtbi.2009.07.018 |
| SSID | ssj0064113 |
| Score | 2.327582 |
| Snippet | Elucidating disease spread between subpopulations is crucial in guiding effective disease control efforts. Genomic epidemiology and phylodynamics have emerged... AbstractElucidating disease spread between subpopulations is crucial in guiding effective disease control efforts. Genomic epidemiology and phylodynamics have... |
| SourceID | doaj proquest pubmed crossref elsevier |
| SourceType | Open Website Aggregation Database Index Database Publisher |
| StartPage | 100795 |
| SubjectTerms | Birth–death Coalescent Communicable Diseases - epidemiology Communicable Diseases - transmission Computer Simulation Disease Outbreaks - statistics & numerical data Epidemics - statistics & numerical data Epidemiological Models Humans Infectious Disease Internal Medicine Pathogen spread Phylodynamics Phylogenetics Phylogeny Population Density Population Dynamics |
| Title | Estimating pathogen spread using structured coalescent and birth–death models: A quantitative comparison |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S1755436524000562 https://www.clinicalkey.es/playcontent/1-s2.0-S1755436524000562 https://dx.doi.org/10.1016/j.epidem.2024.100795 https://www.ncbi.nlm.nih.gov/pubmed/39461051 https://www.proquest.com/docview/3121277440 https://doaj.org/article/a4b0f3d332564a1cb8128a76d2cf6a0f |
| Volume | 49 |
| WOSCitedRecordID | wos001347061500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1878-0067 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0064113 issn: 1755-4365 databaseCode: DOA dateStart: 20140101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nb9QwELWg4oCEEN9sKZWRuEY4sR0n3ErVigOqkPjQ3izb45TtIdtudjnzH_iH_JLO2MlqkUDlwDVKYmv8ZuY5Gb9h7DWUAbSKphBOx0LpCuNgK0ShfCPqtqvAJ53trx_M2Vkzn7cfd1p9UU1YlgfOhnvjlBedBCkxNytXBo8ZqXGmhip0tRMdRV9h2mkzlWNwrcrUGBlzoy6UrPV0aC5VdsXUexX3hpVKRQLUW2InKSXt_t9y09-4Z8pBpw_Y_ZE88qM86YfsVuwfsXv5yxvPB4oes4sT9Friof05p37DS4QIHy6RHAKnKvdznjVjN6sIPCzdKOjEXQ_cL1brb79-_AQihjx1yRne8iN-tXF9Oo2GsZGHbevCJ-zL6cnn4_fF2FGhCOhr6wKUCtIFbVwroaqd19GY4LUGXelOInWouxhUG4IObYRYeQfIYUCYkKTt5FO21y_7-JzxRoLvgqyld7jFdMo1uoLSdCrGxskGZqyYTGovs3CGnSrKLmxeAktLYPMSzNg7svv2XpK9ThcQDHYEg70JDDOmp1Wz08lSjIX4osUNg5s_PReH0aEHW9qhssJ-IkQRoKj0lrjj7pMjZ8lc5B_GfDXByqJL038a18flZrCyJNl9Um6csWcZb1uzyJYE8nW5_z_M9YLdpQnl6pwDtofoiy_ZnfB9vRhWh-y2mTeHya-uATe0JvE |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Estimating+pathogen+spread+using+structured+coalescent+and+birth%E2%80%93death+models%3A+A+quantitative+comparison&rft.jtitle=Epidemics&rft.au=Seidel%2C+Sophie&rft.au=Stadler%2C+Tanja&rft.au=Vaughan%2C+Timothy+G&rft.date=2024-12-01&rft.issn=1755-4365&rft.volume=49&rft.spage=100795&rft.epage=100795&rft_id=info:doi/10.1016%2Fj.epidem.2024.100795&rft.externalDBID=ECK1-s2.0-S1755436524000562&rft.externalDocID=1_s2_0_S1755436524000562 |
| thumbnail_m | http://cvtisr.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fcdn.clinicalkey.com%2Fck-thumbnails%2F17554365%2FS1755436524X00043%2Fcov150h.gif |