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Identifying likely transmissions in Mycobacterium bovis infected populations of cattle and badgers using the Kolmogorov Forward Equations

  • 1.

    Barrat, A., Barthélemy, M., Pastor-Satorras, R. & Vespignani, A. The architecture of complex weighted networks. Proc. Natl. Acad. Sci. USA 101, 3747–3752 (2004).

    ADS  CAS  PubMed  MATH  Article  Google Scholar 

  • 2.

    Colizza, V., Barthélemy, M., Barrat, A. & Vespignani, A. Epidemic modeling in complex realities. Comptes Rendus Biol. 330, 364–374 (2007).

    Article  Google Scholar 

  • 3.

    Craft, M. E., Volz, E., Packer, C. & Meyers, L. A. Distinguishing epidemic waves from disease spillover in a wildlife population. Proc. R. Soc. B Biol. Sci. 276, 1777–1785 (2009).

    Article  Google Scholar 

  • 4.

    Vernon, M. C. & Keeling, M. J. Representing the UK’s cattle herd as static and dynamic networks. Proc. Biol. Sci. 276, 469–476 (2009).

    PubMed  Google Scholar 

  • 5.

    Kao, R. R., Green, D. M., Johnson, J. & Kiss, I. Z. Disease dynamics over very different time-scales: foot-and-mouth disease and scrapie on the network of livestock movements in the UK. J. R. Soc. Interface 4, 907–916 (2007).

    PubMed  PubMed Central  Article  Google Scholar 

  • 6.

    Craft, M. E. Infectious disease transmission and contact networks in wildlife and livestock. Philos. Trans. R. Soc. B Biol. Sci. 370, 20140107–20140107 (2015).

    Article  Google Scholar 

  • 7.

    Chiner-Oms, Á. & Comas, I. Large genomics datasets shed light on the evolution of the Mycobacterium tuberculosis complex. Infect. Genet. Evol. https://doi.org/10.1016/j.meegid.2019.02.028 (2019).

    Article  PubMed  Google Scholar 

  • 8.

    Köser, C. U. et al. Rapid whole-genome sequencing for investigation of a neonatal MRSA outbreak. N. Engl. J. Med. 366, 2267–2275 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  • 9.

    Roetzer, A. et al. Whole genome sequencing versus traditional genotyping for investigation of a Mycobacterium tuberculosis outbreak: a longitudinal molecular epidemiological study. PLoS Med. 10, e1001387 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  • 10.

    Kao, R. R., Haydon, D. T., Lycett, S. J. & Murcia, P. R. Supersize me: how whole-genome sequencing and big data are transforming epidemiology. Trends Microbiol. 22, 282–291 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 11.

    Wymant, C. et al. PHYLOSCANNER: Inferring transmission from within- and between-host pathogen genetic diversity. Mol. Biol. Evol. 35, 719–733 (2018).

    CAS  PubMed  Article  Google Scholar 

  • 12.

    Gutiérrez, S., Michalakis, Y. & Blanc, S. Virus population bottlenecks during within-host progression and host-to-host transmission. Curr. Opin. Virol. 2, 546–555 (2012).

    PubMed  Article  CAS  Google Scholar 

  • 13.

    Buckee, C. O. F., Koelle, K., Mustard, M. J. & Gupta, S. The effects of host contact network structure on pathogen diversity and strain structure. Proc. Natl. Acad. Sci. USA 101, 10839–10844 (2004).

    ADS  CAS  PubMed  Article  Google Scholar 

  • 14.

    Rasmussen, D. A., Ratmann, O. & Koelle, K. Inference for nonlinear epidemiological models using genealogies and time series. PLoS Comput. Biol. 7, e1002136 (2011).

    ADS  MathSciNet  CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 15.

    Rasmussen, D. A., Volz, E. M. & Koelle, K. Phylodynamic inference for structured epidemiological models. PLoS Comput. Biol. 10, e1003570 (2014).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  • 16.

    Rasmussen, D. A., Kouyos, R., Günthard, H. F. & Stadler, T. Phylodynamics on local sexual contact networks. PLoS Comput. Biol. 13, 1–23 (2017).

    Article  CAS  Google Scholar 

  • 17.

    Cottam, E. M. et al. Integrating genetic and epidemiological data to determine transmission pathways of foot-and-mouth disease virus. Proc. R. Soc. B Biol. Sci. 275, 887–895 (2008).

    Article  Google Scholar 

  • 18.

    Ypma, R. J. F. et al. Unravelling transmission trees of infectious diseases by combining genetic and epidemiological data. Proc. R. Soc. B Biol. Sci. 279, 444–450 (2012).

    CAS  Article  Google Scholar 

  • 19.

    Ypma, R. J. F., van Ballegooijen, W. M. & Wallinga, J. Relating phylogenetic trees to transmission trees of infectious disease outbreaks. Genetics 195, 1055–1062 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  • 20.

    Morelli, M. J. et al. A Bayesian inference framework to reconstruct transmission trees using epidemiological and genetic data. PLoS Comput. Biol. 8, e1002768 (2012).

    MathSciNet  CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 21.

    Lau, M. S. Y., Marion, G., Streftaris, G. & Gibson, G. A systematic Bayesian integration of epidemiological and genetic data. PLoS Comput. Biol. 11, 1–27 (2015).

    Article  CAS  Google Scholar 

  • 22.

    De Maio, N., Wu, C. H. & Wilson, D. J. SCOTTI: efficient reconstruction of transmission within outbreaks with the structured coalescent. PLoS Comput. Biol. 12, 1–23 (2016).

    Google Scholar 

  • 23.

    Li, L. M., Grassly, N. C. & Fraser, C. Quantifying transmission heterogeneity using both pathogen phylogenies and incidence time series. Mol. Biol. Evol. 34, 2982–2995 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 24.

    Romero-Severson, E. O., Bulla, I. & Leitner, T. Phylogenetically resolving epidemiologic linkage. Proc. Natl. Acad. Sci. USA 113, 2690–2695 (2016).

    ADS  CAS  PubMed  Article  Google Scholar 

  • 25.

    Firestone, S. M. et al. Reconstructing foot-and-mouth disease outbreaks: a methods comparison of transmission network models. Sci. Rep. 9, 4809 (2019).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  • 26.

    Campbell, F., Strang, C., Ferguson, N., Cori, A. & Jombart, T. When are pathogen genome sequences informative of transmission events?. PLoS Pathog. 14, 1–21 (2018).

    Google Scholar 

  • 27.

    Allen, A. R. One bacillus to rule them all? Investigating broad range host adaptation in Mycobacterium bovis. Infect. Genet. Evol. 53, 68–76 (2017).

    PubMed  Article  Google Scholar 

  • 28.

    Biek, R. et al. Whole genome sequencing reveals local transmission patterns of Mycobacterium bovis in sympatric cattle and badger populations. PLoS Pathog. 8, e1003008 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 29.

    Glaser, L. et al. Descriptive epidemiology and whole genome sequencing analysis for an outbreak of bovine tuberculosis in beef cattle and white-tailed deer in northwestern Minnesota. PLoS ONE 11, e0145735 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  • 30.

    Orloski, K., Robbe-Austerman, S., Stuber, T., Hench, B. & Schoenbaum, M. Whole genome sequencing of Mycobacterium bovis isolated from livestock in the United States, 1989–2018. Front. Vet. Sci. 5, 1–10 (2018).

    Article  Google Scholar 

  • 31.

    Palmer, M. V. Mycobacterium bovis: characteristics of wildlife reservoir hosts. Transbound. Emerg. Dis. 60, 1–13 (2013).

    PubMed  Article  Google Scholar 

  • 32.

    Keeling, M. J. & Ross, J. V. On methods for studying stochastic disease dynamics. J. R. Soc. Interface 5, 171–181 (2008).

    CAS  PubMed  Article  Google Scholar 

  • 33.

    Sharkey, K. J. Deterministic epidemiological models at the individual level. J. Math. Biol. 57, 311–331 (2008).

    MathSciNet  PubMed  MATH  Article  Google Scholar 

  • 34.

    Stollenwerk, N. & Jansen, V. A. A. Meningitis, pathogenicity near criticality: the epidemiology of meningococcal disease as a model for accidental pathogens. J. Theor. Biol. 222, 347–359 (2003).

    PubMed  MATH  Article  Google Scholar 

  • 35.

    Delahay, R. J. et al. Long-term temporal trends and estimated transmission rates for Mycobacterium bovis infection in an undisturbed high-density badger (Meles meles) population. Epidemiol. Infect. 141, 1445–1456 (2013).

    CAS  PubMed  Article  Google Scholar 

  • 36.

    Crispell, J. et al. Combining genomics and epidemiology to analyse bi-directional transmission of Mycobacterium bovis in a multi-host system. Elife https://doi.org/10.7554/eLife.45833.001 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  • 37.

    Ford, C. B. et al. Use of whole genome sequencing to estimate the mutation rate of Mycobacterium tuberculosis during latent infection. Nat. Genet. 43, 482–488 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 38.

    Colangeli, R. et al. Whole genome sequencing of Mycobacterium tuberculosis reveals slow growth and low mutation rates during latent infections in humans. PLoS ONE 9, 1–9 (2014).

    MathSciNet  Article  CAS  Google Scholar 

  • 39.

    Didelot, X., Fraser, C., Gardy, J., Colijn, C. & Malik, H. Genomic infectious disease epidemiology in partially sampled and ongoing outbreaks. Mol. Biol. Evol. 34, 997–1007 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 40.

    Lycett, S. J. et al. Role for migratory wild birds in the global spread of avian influenza H5N8. Science 354, 213–217 (2016).

    Article  CAS  Google Scholar 

  • 41.

    Saulnier, E., Gascuel, O. & Alizon, S. Inferring epidemiological parameters from phylogenies using regression-ABC: a comparative study. PLoS Comput. Biol. 13, 1–31 (2017).

    Article  CAS  Google Scholar 

  • 42.

    De Maio, N., Worby, C. J., Wilson, D. J. & Stoesser, N. Bayesian reconstruction of transmission within outbreaks using genomic variants. PLoS Comput. Biol. 14, 1–23 (2018).

    Google Scholar 

  • 43.

    Pybus, O. G. & Rambaut, A. Evolutionary analysis of the dynamics of viral infectious disease. Nat. Rev. Genet. 10, 540–550 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 44.

    Biek, R., Pybus, O. G., Lloyd-Smith, J. O. & Didelot, X. Measurably evolving pathogens in the genomic era. Trends Ecol. Evol. 30, 306–313 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  • 45.

    Brooks-Pollock, E., Roberts, G. O. & Keeling, M. J. A dynamic model of bovine tuberculosis spread and control in Great Britain. Nature 511, 228–231 (2014).

    ADS  CAS  PubMed  Article  Google Scholar 

  • 46.

    Campbell, F., Cori, A., Ferguson, N. & Jombart, T. Bayesian inference of transmission chains using timing of symptoms, pathogen genomes and contact data. PLOS Comput. Biol. 15, e1006930 (2019).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  • 47.

    Robertson, A., Palphramand, K. L., Carter, S. P. & Delahay, R. J. Group size correlates with territory size in European badgers: implications for the resource dispersion hypothesis?. Oikos 124, 507–514 (2015).

    Article  Google Scholar 

  • 48.

    Roper, T. Badger (Collins, London, 2010).

    Google Scholar 

  • 49.

    Drewe, J. A., Tomlinson, A. J., Walker, N. J. & Delahay, R. J. Diagnostic accuracy and optimal use of three tests for tuberculosis in live badgers. PLoS ONE 5, e11196 (2010).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  • 50.

    Walker, T. M. et al. Whole-genome sequencing to delineate Mycobacterium tuberculosis outbreaks: a retrospective observational study. Lancet Infect. Dis. 13, 137–146 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 51.

    Álvarez, J. et al. Research in Veterinary Science Bovine tuberculosis : within-herd transmission models to support and direct the decision-making process. Res. Vet. Sci. 97, S61–S68 (2014).

    PubMed  Article  Google Scholar 

  • 52.

    Rossi, G. et al. Epidemiological modelling for the assessment of bovine tuberculosis surveillance in the dairy farm network in Emilia-Romagna (Italy). Epidemics 11, 62–70 (2015).

    PubMed  Article  Google Scholar 

  • 53.

    Kao, R. R., Roberts, M. G. & Ryan, T. J. A model of bovine tuberculosis control in domesticated cattle herds. Proc. R. Soc. B Biol. Sci. 264, 1069–1076 (1997).

    ADS  CAS  Article  Google Scholar 

  • 54.

    Conlan, A. J. K. et al. Estimating the hidden burden of bovine tuberculosis in Great Britain. PLoS Comput. Biol. 8, e1002730 (2012).

    MathSciNet  CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 55.

    O’Hare, A., Orton, R. J., Bessell, P. R. & Kao, R. R. Estimating epidemiological parameters for bovine tuberculosis in British cattle using a Bayesian partial-likelihood approach. Proc. R. Soc. B Biol. Sci. 281, 20140248 (2014).

    Article  Google Scholar 

  • 56.

    Rossi, G., Aubry, P., Dubé, C. & Smith, R. L. The spread of bovine tuberculosis in Canadian shared pastures: data, model, and simulations. Transbound. Emerg. Dis. 66, 562–577 (2019).

    PubMed  Article  Google Scholar 

  • 57.

    R Core Team. R: A Language and Environment for Statistical Computing. (2018).

  • 58.

    Soetaert, K., Petzoldt, T. & Setzer, R. W. Solving differential equations in R: package deSolve. J. Stat. Softw. 33, 1–25 (2010).

    Google Scholar 

  • 59.

    Lawes, J. R. et al. Bovine TB surveillance in Great Britain in 2014. Vet. Rec. 178, 310–315 (2016).

    CAS  PubMed  Article  Google Scholar 

  • 60.

    Trewby, H. et al. Use of bacterial whole-genome sequencing to investigate local persistence and spread in bovine tuberculosis. Epidemics 14, 26–35 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  • 61.

    Crispell, J. et al. Using whole genome sequencing to investigate transmission in a multi-host system: bovine tuberculosis in New Zealand. BMC Genom. 18, 180 (2017).

    Article  CAS  Google Scholar 

  • 62.

    Salvador, L. C. M. et al. Disease management at the wildlife-livestock interface: using whole-genome sequencing to study the role of elk in Mycobacterium bovis transmission in Michigan. USA. Mol. Ecol. https://doi.org/10.1111/mec.15061 (2019).

    Article  PubMed  Google Scholar 

  • 63.

    Brooks-Pollock, E. et al. Age-dependent patterns of bovine tuberculosis in cattle. Vet. Res. 44, 1 (2013).

    Article  Google Scholar 


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