TBS903 Autumn2009 Subject Outline Subject Outline (Final Version)
Outline
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Newton Institute, Infectious Diseases Dynamics, Aug 21, 2013 1 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
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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. Outline. - PowerPoint PPT Presentation
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Newton Institute, Infectious Diseases Dynamics, Aug 21, 2013 1
Inference of epidemiological dynamics from sequence data: application to influenza
Cécile Viboudwith Martha Nelson, Eddie Holmes, Julia Gog, Bryan Grenfell
Fogarty International Center National Institutes of Health
Bethesda, MD, USA
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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)
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>11,900 full genomes sequenced to date
• Majority are human influenza A virus
The NIAID/NIH Influenza Genome Sequencing Project
Evolutionary analysis using BEASTBayesian 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)
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Spatial dynamics
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• 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 1997-2005
Local influenza A Virus Evolution: New York State 1997-2005 (413 full-genome sequences)
Nelson et al, Plos Pathogen, 2006
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Spatial Diffusion of A/H1N1 in the United States284 full-genomes, 2006-07
• Multiple introductions, no cross-season persistence, no spatial structure
Nelson et al, Plos Pat 2007
• no. clades no. samples
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Temporal dynamics of A/H1N1across the US, 2006-07 season
Nelson et al, Plos Pathogens 2007
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Hierarchical spread of influenza in the US
R=1.35
R=1.35
R=1.89
Couplingi,j Popi
aiPopjaj/dij
g
Viboud et al, Science, 2006
Model fitted to long-term influenza epidemiological records
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Lemey et al., 2009 PLoS Curr
Phylogeographic analysis of 2009 spring pandemic wave
Epidemiological models of spring 2009 pandemic diffusion
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Balcan et al., Plos Currents 2009; Bajardi et al Plos One 2011; Hosseini et al Plos One 2010
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Tizzoni et al., BMC Med 2012
Epidemiologic models of fall wave of 2009 pandemic
Diffusion patterns at national scale (3 US locations)
Nelson et al, J Virol 2011
Seasonal fluH1N1pdmSpring 09
H1N1pdmFall 09
Houston
Milwaukee
NY State
Nelson et al., J Virol 2011
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!
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Epidemiologic patterns of fall 2009 pandemic wave
Schools
Pop sizeHumidity
Prior immunity
Distance
Gog et al., unpubl.
Fall pandemic outbreak at UC. San Diego
16Holmes et al., J Virol 2011
29,000 students55 full genome H1N1pdm
- 24-33 separate introductions
- 7 clusters- No clustering by time, age, gender or geography
In contrast, much clearer spatial patterns of the influenza virus in swine
0.1
Viral introductions between regions,based on Markov jump counts
Southern source populations
Midwestern sink populations
Nelson et al., PLoS Pathog 2011
3.3
0.4
9.413.1
Model testing
Model Log Marginal Likelihood
Bayes Factor Comparison
Rates fixed equally -87.08 --
Population of destination -83.24 3.8
Population of origin -108.32 -21.2
Destination x origin -85.45 1.6
Swineflows -80.99 6.1
Nelson et al. PLoS Pathog 2011
Best-fit swine flu model
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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
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Inference of temporal patterns
Inference of key epidemiological parameters early in a pandemic outbreak: R0, Tg
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Fraser et al, Science, 2009
Sequence data
TMRCA: Jan-12-09 (Nov-03 to Mar-2)
Epi data
Influenza seasonality
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Tracking population dynamics through time
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Captures differences in seasonality and viral diversity between regions
Rambaut et al, Nature, 2008; Bahl et al, PNAS, 2012
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Strain interactions
Rambaut et al. Nature 2008; Chen et al, J Mol Evol 2008
Sampling issues: region and viral subtype
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No. sequences available
Viboud et al. Phil Trans Roy Soc 2013
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Stack et al, Interface, 2010
Sampling issues: time
Cannot go back further than the last bottleneckSampling at the end of an epidemic best
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De Silva et al, Interface, 2012
Sampling issues: time
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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
