BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Statistical inference of virus phylodynamics - Oliver Ratmann\, Im
 perial College London
DTSTART:20130312T143000Z
DTEND:20130312T153000Z
UID:TALK42671@talks.cam.ac.uk
CONTACT:Dr Jack Bowden
DESCRIPTION:The infectious disease dynamics of many viral pathogens like i
 nfluenza\, norovirus and coronavirus are inextricably tied to their evolut
 ion. This interaction between evolutionary and ecological processes compli
 cates our ability to understand the infectious disease behavior of rapidly
  evolving pathogens. Most statistical methods for the analysis of these 
 “phylodynamics” require that the likelihood of the data can be explici
 tly calculated. Currently\, this is not possible for many phylodynamic mod
 els\, so that questions on the interaction between viral variants cannot b
 e well-addressed within this framework. Simulation-based statistical metho
 ds circumvent likelihood calculations. Considering interpandemic human inf
 luenza A virus subtype H3N2\, we illustrate the effectiveness of these met
 hods to fit and assess complex phylodynamic models against both sequence a
 nd surveillance data. We find that combining molecular genetic and epidemi
 ological data is key to estimate phylodynamic parameters reliably. Moreove
 r\, the information in the available data taken together is enough to expo
 se quantitative model inconsistencies. Methods such as ABC which can combi
 ne sequence and surveillance data appear to be well-suited to fit and asse
 ss mechanistic hypotheses on the phylodynamics of RNA viruses.\n\nIntroduc
 tory reading: Grenfell et al. (2004) Unifying the epidemiological and evol
 utionary dynamics of pathogens Science 303\,327-332\,
LOCATION:Large  Seminar Room\, 1st Floor\, Institute of Public Health\, Un
 iversity Forvie Site\, Robinson Way\, Cambridge
END:VEVENT
END:VCALENDAR