@article{fdi:010080771,
  title = {{R}egression-based ranking of pathogen strains with respect to their contribution to natural epidemics},
  author = {{S}oubeyrand, {S}. and {T}ollenaere, {C}harlotte and {H}aon-{L}asportes, {E}. and {L}aine, {A}.{L}.},
  editor = {},
  language = {{ENG}},
  abstract = {{G}enetic variation in pathogen populations may be an important factor driving heterogeneity in disease dynamics within their host populations. {H}owever, to date, we understand poorly how genetic diversity in diseases impact on epidemiological dynamics because data and tools required to answer this questions are lacking. {H}ere, we combine pathogen genetic data with epidemiological monitoring of disease progression, and introduce a statistical exploratory method to investigate differences among pathogen strains in their performance in the field. {T}he method exploits epidemiological data providing a measure of disease progress in time and space, and genetic data indicating the relative spatial patterns of the sampled pathogen strains. {A}pplying this method allows to assign ranks to the pathogen strains with respect to their contributions to natural epidemics and to assess the significance of the ranking. {T}his method was first tested on simulated data, including data obtained from an original, stochastic, multi-strain epidemic model. {I}t was then applied to epidemiological and genetic data collected during one natural epidemic of powdery mildew occurring in its wild host population. {B}ased on the simulation study, we conclude that the method can achieve its aim of ranking pathogen strains if the sampling effort is sufficient. {F}or powdery mildew data, the method indicated that one of the sampled strains tends to have a higher fitness than the four other sampled strains, highlighting the importance of strain diversity for disease dynamics. {O}ur approach allowing the comparison of pathogen strains in natural epidemic is complementary to the classical practice of using experimental infections in controlled conditions to estimate fitness of different pathogen strains. {O}ur statistical tool, implemented in the {R} package {S}train{R}anking, is mainly based on regression and does not rely on mechanistic assumptions on the pathogen dynamics. {T}hus, the method can be applied to a wide range of pathogens},
  keywords = {{FINLANDE} ; {ALAND}},
  booktitle = {},
  journal = {{P}los {O}ne},
  volume = {9},
  numero = {1},
  pages = {e86591 [8 ]},
  ISSN = {1932-6203},
  year = {2014},
  DOI = {10.1371/journal.pone.0086591},
  URL = {https://www.documentation.ird.fr/hor/fdi:010080771},
}