IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1004869.html
   My bibliography  Save this article

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

Author

Listed:
  • Eben Kenah
  • Tom Britton
  • M Elizabeth Halloran
  • Ira M Longini Jr

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.Author Summary: 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.

Suggested Citation

  • Eben Kenah & Tom Britton & M Elizabeth Halloran & Ira M Longini Jr, 2016. "Molecular Infectious Disease Epidemiology: Survival Analysis and Algorithms Linking Phylogenies to Transmission Trees," PLOS Computational Biology, Public Library of Science, vol. 12(4), pages 1-29, April.
    • Handle: RePEc:plo:pcbi00:1004869
    DOI: 10.1371/journal.pcbi.1004869
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004869
    Download Restriction: no
    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004869&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pcbi.1004869?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. P. D. O’Neill & G. O. Roberts, 1999. "Bayesian inference for partially observed stochastic epidemics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(1), pages 121-129.
    2. Trevor Bedford & Steven Riley & Ian G. Barr & Shobha Broor & Mandeep Chadha & Nancy J. Cox & Rodney S. Daniels & C. Palani Gunasekaran & Aeron C. Hurt & Anne Kelso & Alexander Klimov & Nicola S. Lewis, 2015. "Global circulation patterns of seasonal influenza viruses vary with antigenic drift," Nature, Nature, vol. 523(7559), pages 217-220, July.
    3. David A Rasmussen & Oliver Ratmann & Katia Koelle, 2011. "Inference for Nonlinear Epidemiological Models Using Genealogies and Time Series," PLOS Computational Biology, Public Library of Science, vol. 7(8), pages 1-11, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kypraios, Theodore, 2009. "A note on maximum likelihood estimation of the initial number of susceptibles in the general stochastic epidemic model," Statistics & Probability Letters, Elsevier, vol. 79(18), pages 1972-1976, September.
    2. McKinley, Trevelyan J. & Ross, Joshua V. & Deardon, Rob & Cook, Alex R., 2014. "Simulation-based Bayesian inference for epidemic models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 434-447.
    3. Deka, Aniruddha & Bhattacharyya, Samit, 2022. "The effect of human vaccination behaviour on strain competition in an infectious disease: An imitation dynamic approach," Theoretical Population Biology, Elsevier, vol. 143(C), pages 62-76.
    4. Patrick G T Walker & Simon Cauchemez & Raphaëlle Métras & Do Huu Dung & Dirk Pfeiffer & Azra C Ghani, 2010. "A Bayesian Approach to Quantifying the Effects of Mass Poultry Vaccination upon the Spatial and Temporal Dynamics of H5N1 in Northern Vietnam," PLOS Computational Biology, Public Library of Science, vol. 6(2), pages 1-9, February.
    5. Sissy Therese Sonnleitner & Martina Prelog & Stefanie Sonnleitner & Eva Hinterbichler & Hannah Halbfurter & Dominik B. C. Kopecky & Giovanni Almanzar & Stephan Koblmüller & Christian Sturmbauer & Leon, 2022. "Cumulative SARS-CoV-2 mutations and corresponding changes in immunity in an immunocompromised patient indicate viral evolution within the host," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    6. Feng, Jingxue & Wang, Liangliang, 2024. "A switching state-space transmission model for tracking epidemics and assessing interventions," Computational Statistics & Data Analysis, Elsevier, vol. 197(C).
    7. Ioannis Andrianakis & Ian R Vernon & Nicky McCreesh & Trevelyan J McKinley & Jeremy E Oakley & Rebecca N Nsubuga & Michael Goldstein & Richard G White, 2015. "Bayesian History Matching of Complex Infectious Disease Models Using Emulation: A Tutorial and a Case Study on HIV in Uganda," PLOS Computational Biology, Public Library of Science, vol. 11(1), pages 1-18, January.
    8. Jonas E. Arias & Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez & Minchul Shin, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," Working Papers 21-18, Federal Reserve Bank of Philadelphia.
    9. Franco M Neri & Alex R Cook & Gavin J Gibson & Tim R Gottwald & Christopher A Gilligan, 2014. "Bayesian Analysis for Inference of an Emerging Epidemic: Citrus Canker in Urban Landscapes," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-16, April.
    10. David A Rasmussen & Erik M Volz & Katia Koelle, 2014. "Phylodynamic Inference for Structured Epidemiological Models," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-16, April.
    11. Bagarello, F. & Gargano, F. & Roccati, F., 2020. "Modeling epidemics through ladder operators," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    12. Nik J Cunniffe & Francisco F Laranjeira & Franco M Neri & R Erik DeSimone & Christopher A Gilligan, 2014. "Cost-Effective Control of Plant Disease When Epidemiological Knowledge Is Incomplete: Modelling Bahia Bark Scaling of Citrus," PLOS Computational Biology, Public Library of Science, vol. 10(8), pages 1-14, August.
    13. Douc, Randal & Olsson, Jimmy & Roueff, François, 2020. "Posterior consistency for partially observed Markov models," Stochastic Processes and their Applications, Elsevier, vol. 130(2), pages 733-759.
    14. Jonathan U Harrison & Ruth E Baker, 2018. "The impact of temporal sampling resolution on parameter inference for biological transport models," PLOS Computational Biology, Public Library of Science, vol. 14(6), pages 1-30, June.
    15. Golightly, Andrew & Bradley, Emma & Lowe, Tom & Gillespie, Colin S., 2019. "Correlated pseudo-marginal schemes for time-discretised stochastic kinetic models," Computational Statistics & Data Analysis, Elsevier, vol. 136(C), pages 92-107.
    16. Yeongseon Park & Michael A. Martin & Katia Koelle, 2023. "Epidemiological inference for emerging viruses using segregating sites," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    17. Bonacina Francesco & Lopez Olivier & Thomas Maud, 2025. "Tree-based conditional copula estimation," Dependence Modeling, De Gruyter, vol. 13(1), pages 1-25.
    18. Wan Yang & Alicia Karspeck & Jeffrey Shaman, 2014. "Comparison of Filtering Methods for the Modeling and Retrospective Forecasting of Influenza Epidemics," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-15, April.
    19. Xiang, Fei & Neal, Peter, 2014. "Efficient MCMC for temporal epidemics via parameter reduction," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 240-250.
    20. Adam Kucharski, 2022. "Adam Kucharski's invited discussion contribution to the papers in Session 3 of the Royal Statistical Society's Special Topic Meeting on Covid‐19 Transmission: 11 June 2021," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 143-144, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1004869. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.