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Newton Institute, Infectious Diseases Dynamics, Aug 21, 2013 1 Inference of epidemiological dynamics from sequence data: application to influenza Cécile.

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Presentation on theme: "Newton Institute, Infectious Diseases Dynamics, Aug 21, 2013 1 Inference of epidemiological dynamics from sequence data: application to influenza Cécile."— Presentation transcript:

1 Newton Institute, Infectious Diseases Dynamics, Aug 21, Inference of epidemiological dynamics from sequence data: application to influenza Cécile Viboud with Martha Nelson, Eddie Holmes, Julia Gog, Bryan Grenfell Fogarty International Center National Institutes of Health Bethesda, MD, USA

2 2 Outline Influenza has a long-history of fitting epidemiologic models to data Recent explosion of sequence data makes epidemiological inference possible Contrast insights from both types of analyses Spatial patterns (Pandemic, epidemic) Temporal patterns (Growth rate, R0, and else)

3 3 >11,900 full genomes sequenced to date Majority are human influenza A virus The NIAID/NIH Influenza Genome Sequencing Project

4 Evolutionary analysis using BEAST Bayesian evolutionary analysis sampling trees Platform for integrating sequence, time, spatial data for –Estimating evolutionary rates –Inferring population dynamics (coalescent) –Phylogeography Exact date of influenza virus sampling is available (allows fine- scale temporal resolution)

5 5 Spatial dynamics

6 6 Multiple introductions of virus into New York state in each season Little persistence of viral lineages between seasons ( No spatial structure within New York State Antigenic drift is an episodic process and does not seen to occur in New York State Global NA phylogeny Local influenza A Virus Evolution: New York State (413 full-genome sequences) Nelson et al, Plos Pathogen, 2006

7 7 Spatial Diffusion of A/H1N1 in the United States 284 full-genomes, Multiple introductions, no cross- season persistence, no spatial structure Nelson et al, Plos Pat 2007 no. clades no. samples

8 8 Temporal dynamics of A/H1N1 across the US, season Nelson et al, Plos Pathogens 2007

9 9 Hierarchical spread of influenza in the US R=1.35 R=1.89 Coupling i,j Pop i i Pop j j /d ij Viboud et al, Science, 2006 Model fitted to long-term influenza epidemiological records

10 10 Lemey et al., 2009 PLoS Curr Phylogeographic analysis of 2009 spring pandemic wave

11 Epidemiological models of spring 2009 pandemic diffusion 11 Balcan et al., Plos Currents 2009; Bajardi et al Plos One 2011; Hosseini et al Plos One 2010

12 12 Tizzoni et al., BMC Med 2012 Epidemiologic models of fall wave of 2009 pandemic

13 Diffusion patterns at national scale (3 US locations) Nelson et al, J Virol 2011 Seasonal flu H1N1pdm Spring 09 H1N1pdm Fall 09 Houston Milwaukee NY State Nelson et al., J Virol 2011

14 Different spatial structure in spring and fall 2009 Spatially structured co-circulating lineages One predominant lineage, no spatial structure Spring 2009 Fall 2009 Nelson et al., J Virol 2011; but Baillie et al, J Virol 2012!

15 15 Epidemiologic patterns of fall 2009 pandemic wave Schools Pop size Humidity Prior immunity Distance Gog et al., unpubl.

16 Fall pandemic outbreak at UC. San Diego 16 Holmes et al., J Virol ,000 students 55 full genome H1N1pdm separate introductions - 7 clusters - No clustering by time, age, gender or geography

17 In contrast, much clearer spatial patterns of the influenza virus in swine Viral introductions between regions, based on Markov jump counts Southern source populations Midwestern sink populations Nelson et al., PLoS Pathog

18 Model testing Model Log Marginal Likelihood Bayes Factor Comparison Rates fixed equally Population of destination Population of origin Destination x origin Swineflows Nelson et al. PLoS Pathog 2011 Best-fit swine flu model

19 19 Influenza spatial spread: insights from sequence data Seasonal flu (national): –No persistence over summer –Lots of co-circulating lineages –Hierarchical patterns of spread observed in epidemiologic data but not in sequence data sampling ? role of mixed infections ? Pandemic flu: –International pandemic arrival explained by travel patterns –Conflicting fall wave patterns nationally

20 20 Inference of temporal patterns

21 Inference of key epidemiological parameters early in a pandemic outbreak: R0, Tg 21 Fraser et al, Science, 2009 Sequence data TMRCA: Jan (Nov-03 to Mar-2) Epi data

22 Influenza seasonality 22

23 Tracking population dynamics through time 23 Captures differences in seasonality and viral diversity between regions Rambaut et al, Nature, 2008; Bahl et al, PNAS, 2012

24 24 Strain interactions Rambaut et al. Nature 2008; Chen et al, J Mol Evol 2008

25 Sampling issues: region and viral subtype 25 No. sequences available Viboud et al. Phil Trans Roy Soc 2013

26 26 Stack et al, Interface, 2010 Sampling issues: time Cannot go back further than the last bottleneck Sampling at the end of an epidemic best

27 27 De Silva et al, Interface, 2012 Sampling issues: time

28 28 Areas for future research –Sampling –Estimate R from influenza sequence data for « typical » epidemic season –Explore seasonal drivers and subtype interactions in viral population size estimates –Other disease systems have clearer spatial diffusion patterns (swine influenza, West Nile, rabies) –Movements of hosts vs mutation rate

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