Molecular Infectious Disease Epidemiology: Survival Analysis and Algorithms Linking Phylogenies to Transmission Trees

Recent work has attempted to use whole-genome sequence data from pathogens to reconstruct the transmission trees linking infectors and infectees in outbreaks. However, transmission trees from one outbreak do not generalize to future outbreaks. Reconstruction of transmission trees is most useful to p...

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Vydané v:	PLoS computational biology Ročník 12; číslo 4; s. e1004869
Hlavní autori:	Kenah, Eben, Britton, Tom, Halloran, M. Elizabeth, Longini, Ira M.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Public Library of Science 01.04.2016 Public Library of Science (PLoS)
Predmet:	Algorithms Animals Biology and Life Sciences Communicable Diseases - epidemiology Communicable Diseases - transmission Computational Biology Computer and Information Sciences Computer Simulation Data analysis Disease transmission Disease Transmission, Infectious - statistics & numerical data Epidemiology Estimates Foot & mouth disease Foot-and-Mouth Disease - epidemiology Foot-and-Mouth Disease - transmission Foot-and-Mouth Disease - virology Foot-and-Mouth Disease Virus - genetics Genomes HIV Human immunodeficiency virus Humans Infections Infectious diseases Medicine and Health Sciences Models, Statistical Molecular Epidemiology Outbreaks Pathogens Phylogenetics Phylogeny Picornaviridae Population Public health Research and Analysis Methods Simulation Stochastic Processes Studies Survival Analysis Trees United Kingdom > UK
ISSN:	1553-7358, 1553-734X, 1553-7358
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Abstract	Recent work has attempted to use whole-genome sequence data from pathogens to reconstruct the transmission trees linking infectors and infectees in outbreaks. However, transmission trees from one outbreak do not generalize to future outbreaks. Reconstruction of transmission trees is most useful to public health if it leads to generalizable scientific insights about disease transmission. In a survival analysis framework, estimation of transmission parameters is based on sums or averages over the possible transmission trees. A phylogeny can increase the precision of these estimates by providing partial information about who infected whom. The leaves of the phylogeny represent sampled pathogens, which have known hosts. The interior nodes represent common ancestors of sampled pathogens, which have unknown hosts. Starting from assumptions about disease biology and epidemiologic study design, we prove that there is a one-to-one correspondence between the possible assignments of interior node hosts and the transmission trees simultaneously consistent with the phylogeny and the epidemiologic data on person, place, and time. We develop algorithms to enumerate these transmission trees and show these can be used to calculate likelihoods that incorporate both epidemiologic data and a phylogeny. A simulation study confirms that this leads to more efficient estimates of hazard ratios for infectiousness and baseline hazards of infectious contact, and we use these methods to analyze data from a foot-and-mouth disease virus outbreak in the United Kingdom in 2001. These results demonstrate the importance of data on individuals who escape infection, which is often overlooked. The combination of survival analysis and algorithms linking phylogenies to transmission trees is a rigorous but flexible statistical foundation for molecular infectious disease epidemiology.
AbstractList	Recent work has attempted to use whole-genome sequence data from pathogens to reconstruct the transmission trees linking infectors and infectees in outbreaks. However, transmission trees from one outbreak do not generalize to future outbreaks. Reconstruction of transmission trees is most useful to public health if it leads to generalizable scientific insights about disease transmission. In a survival analysis framework, estimation of transmission parameters is based on sums or averages over the possible transmission trees. A phylogeny can increase the precision of these estimates by providing partial information about who infected whom. The leaves of the phylogeny represent sampled pathogens, which have known hosts. The interior nodes represent common ancestors of sampled pathogens, which have unknown hosts. Starting from assumptions about disease biology and epidemiologic study design, we prove that there is a one-to-one correspondence between the possible assignments of interior node hosts and the transmission trees simultaneously consistent with the phylogeny and the epidemiologic data on person, place, and time. We develop algorithms to enumerate these transmission trees and show these can be used to calculate likelihoods that incorporate both epidemiologic data and a phylogeny. A simulation study confirms that this leads to more efficient estimates of hazard ratios for infectiousness and baseline hazards of infectious contact, and we use these methods to analyze data from a foot-and-mouth disease virus outbreak in the United Kingdom in 2001. These results demonstrate the importance of data on individuals who escape infection, which is often overlooked. The combination of survival analysis and algorithms linking phylogenies to transmission trees is a rigorous but flexible statistical foundation for molecular infectious disease epidemiology. Recent work has attempted to use whole-genome sequence data from pathogens to reconstruct the transmission trees linking infectors and infectees in outbreaks. However, transmission trees from one outbreak do not generalize to future outbreaks. Reconstruction of transmission trees is most useful to public health if it leads to generalizable scientific insights about disease transmission. In a survival analysis framework, estimation of transmission parameters is based on sums or averages over the possible transmission trees. A phylogeny can increase the precision of these estimates by providing partial information about who infected whom. The leaves of the phylogeny represent sampled pathogens, which have known hosts. The interior nodes represent common ancestors of sampled pathogens, which have unknown hosts. Starting from assumptions about disease biology and epidemiologic study design, we prove that there is a one-to-one correspondence between the possible assignments of interior node hosts and the transmission trees simultaneously consistent with the phylogeny and the epidemiologic data on person, place, and time. We develop algorithms to enumerate these transmission trees and show these can be used to calculate likelihoods that incorporate both epidemiologic data and a phylogeny. A simulation study confirms that this leads to more efficient estimates of hazard ratios for infectiousness and baseline hazards of infectious contact, and we use these methods to analyze data from a foot-and-mouth disease virus outbreak in the United Kingdom in 2001. These results demonstrate the importance of data on individuals who escape infection, which is often overlooked. The combination of survival analysis and algorithms linking phylogenies to transmission trees is a rigorous but flexible statistical foundation for molecular infectious disease epidemiology. Recent work has attempted to use whole-genome sequence data from pathogens to reconstruct the transmission trees linking infectors and infectees in outbreaks. However, transmission trees from one outbreak do not generalize to future outbreaks. Reconstruction of transmission trees is most useful to public health if it leads to generalizable scientific insights about disease transmission. In a survival analysis framework, estimation of transmission parameters is based on sums or averages over the possible transmission trees. A phylogeny can increase the precision of these estimates by providing partial information about who infected whom. The leaves of the phylogeny represent sampled pathogens, which have known hosts. The interior nodes represent common ancestors of sampled pathogens, which have unknown hosts. Starting from assumptions about disease biology and epidemiologic study design, we prove that there is a one-to-one correspondence between the possible assignments of interior node hosts and the transmission trees simultaneously consistent with the phylogeny and the epidemiologic data on person, place, and time. We develop algorithms to enumerate these transmission trees and show these can be used to calculate likelihoods that incorporate both epidemiologic data and a phylogeny. A simulation study confirms that this leads to more efficient estimates of hazard ratios for infectiousness and baseline hazards of infectious contact, and we use these methods to analyze data from a foot-and-mouth disease virus outbreak in the United Kingdom in 2001. These results demonstrate the importance of data on individuals who escape infection, which is often overlooked. The combination of survival analysis and algorithms linking phylogenies to transmission trees is a rigorous but flexible statistical foundation for molecular infectious disease epidemiology. Recent work has attempted to use whole-genome sequence data from pathogens to reconstruct the transmission trees linking infectors and infectees in outbreaks. However, transmission trees from one outbreak do not generalize to future outbreaks. Reconstruction of transmission trees is most useful to public health if it leads to generalizable scientific insights about disease transmission. Accurate estimates of transmission parameters can help identify risk factors for transmission and aid the design and evaluation of public health interventions for emerging infections. Using statistical methods for time-to-event data (survival analysis), estimation of transmission parameters is based on sums or averages over the possible transmission trees. By providing partial information about who infected whom, a pathogen phylogeny can reduce the set of possible transmission trees and increase the precision of transmission parameter estimates. We derive algorithms that enumerate the transmission trees consistent with a pathogen phylogeny and epidemiologic data, show how to calculate likelihoods for transmission data with a phylogeny, and apply these methods to a foot and mouth disease outbreak in the United Kingdom in 2001. These methods will allow pathogen genetic sequences to be incorporated into the analysis of outbreak investigations, vaccine trials, and other studies of infectious disease transmission.
Author	Kenah, Eben Britton, Tom Longini, Ira M. Halloran, M. Elizabeth
AuthorAffiliation	Temple University, UNITED STATES 5 Department of Biostatistics, University of Washington, Seattle, Washington, United States of America 3 Department of Mathematics, Stockholm University, Stockholm, Sweden 2 Center for Inference and Dynamics of Infectious Diseases, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America 1 Biostatistics Department and Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America 4 Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
AuthorAffiliation_xml	– name: Temple University, UNITED STATES – name: 3 Department of Mathematics, Stockholm University, Stockholm, Sweden – name: 2 Center for Inference and Dynamics of Infectious Diseases, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America – name: 4 Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America – name: 5 Department of Biostatistics, University of Washington, Seattle, Washington, United States of America – name: 1 Biostatistics Department and Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
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Copyright	2016 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: Kenah E, Britton T, Halloran ME, Longini IM Jr (2016) Molecular Infectious Disease Epidemiology: Survival Analysis and Algorithms Linking Phylogenies to Transmission Trees. PLoS Comput Biol 12(4): e1004869. doi:10.1371/journal.pcbi.1004869 2016 Kenah et al 2016 Kenah et al 2016 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: Kenah E, Britton T, Halloran ME, Longini IM Jr (2016) Molecular Infectious Disease Epidemiology: Survival Analysis and Algorithms Linking Phylogenies to Transmission Trees. PLoS Comput Biol 12(4): e1004869. doi:10.1371/journal.pcbi.1004869
Copyright_xml	– notice: 2016 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: Kenah E, Britton T, Halloran ME, Longini IM Jr (2016) Molecular Infectious Disease Epidemiology: Survival Analysis and Algorithms Linking Phylogenies to Transmission Trees. PLoS Comput Biol 12(4): e1004869. doi:10.1371/journal.pcbi.1004869 – notice: 2016 Kenah et al 2016 Kenah et al – notice: 2016 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: Kenah E, Britton T, Halloran ME, Longini IM Jr (2016) Molecular Infectious Disease Epidemiology: Survival Analysis and Algorithms Linking Phylogenies to Transmission Trees. PLoS Comput Biol 12(4): e1004869. doi:10.1371/journal.pcbi.1004869
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Current Address: University of Florida, Gainesville, Florida, United States of America Conceived and designed the experiments: EK TB MEH IML. Performed the experiments: EK. Analyzed the data: EK. Wrote the paper: EK TB MEH IML. The authors have declared that no competing interests exist.
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Snippet	Recent work has attempted to use whole-genome sequence data from pathogens to reconstruct the transmission trees linking infectors and infectees in outbreaks.... Recent work has attempted to use whole-genome sequence data from pathogens to reconstruct the transmission trees linking infectors and infectees in...
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SubjectTerms	Algorithms Animals Biology and Life Sciences Communicable Diseases - epidemiology Communicable Diseases - transmission Computational Biology Computer and Information Sciences Computer Simulation Data analysis Disease transmission Disease Transmission, Infectious - statistics & numerical data Epidemiology Estimates Foot & mouth disease Foot-and-Mouth Disease - epidemiology Foot-and-Mouth Disease - transmission Foot-and-Mouth Disease - virology Foot-and-Mouth Disease Virus - genetics Genomes HIV Human immunodeficiency virus Humans Infections Infectious diseases Medicine and Health Sciences Models, Statistical Molecular Epidemiology Outbreaks Pathogens Phylogenetics Phylogeny Picornaviridae Population Public health Research and Analysis Methods Simulation Stochastic Processes Studies Survival Analysis Trees
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Title	Molecular Infectious Disease Epidemiology: Survival Analysis and Algorithms Linking Phylogenies to Transmission Trees
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