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SUMMARY:Modelling the growth and transmission of infectious disease by lin
 king epidemiology and population genetics - Daniel Wilson\, University of 
 Oxford
DTSTART:20120918T133000Z
DTEND:20120918T143000Z
UID:TALK39603@talks.cam.ac.uk
CONTACT:Dr Jack Bowden
DESCRIPTION:Understanding the transmission of infectious disease is import
 ant for monitoring outbreaks\, informing public health policy\, and improv
 ing intervention strategies. Traditionally the fields of population geneti
 cs and epidemiology have been studied separately\; however it is clear tha
 t using genetic information alongside epidemiological models has great pot
 ential for understanding the dynamics of infectious disease.  Directly est
 imating epidemiological parameters such as transmission rates can be diffi
 cult\, as it relies on comprehensive monitoring during an outbreak where r
 elevant processes may be hidden or undetectable. However\, genetic informa
 tion provides an alternative window into the past. I will talk about a com
 bined coalescent-based meta-population model for estimating the parameters
  of standard SI\, SIS and SIR epidemiological models from genetic data. I 
 will apply these models to a meta-analysis of Hepatitis C virus (HCV)\, wi
 th the aim of explaining differences in patterns of genetic diversity betw
 een populations in terms of the underlying epidemiological dynamics. I wil
 l look at differences between datasets in the growth rate of HCV and wheth
 er they are explained by subtype\, host population size or prevalence of d
 isease to understand the factors that drive global variation in Hepatitis 
 C diversity.
LOCATION:Large  Seminar Room\, 1st Floor\, Institute of Public Health\, Un
 iversity Forvie Site\, Robinson Way\, Cambridge
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