Newton Institute, Infectious Diseases Dynamics, Aug 21, 2013 1 Inference of epidemiological dynamics...

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