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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.

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  1. Inference of epidemiological dynamics from sequence data: application to influenzaCécileViboudwith Martha Nelson, Eddie Holmes, Julia Gog, Bryan GrenfellFogarty International Center National Institutes of HealthBethesda, MD, USA Newton Institute, Infectious Diseases Dynamics, Aug 21, 2013

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

  4. 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)

  5. Spatial dynamics

  6. Local influenza A Virus Evolution: New York State 1997-2005 (413 full-genome sequences) Global NA phylogeny 1997-2005 • 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 Nelson et al, Plos Pathogen, 2006

  7. Spatial Diffusion of A/H1N1 in the United States 284 full-genomes, 2006-07 • Multiple introductions, no cross-season persistence, no spatial structure • no. clades  no. samples Nelson et al, PlosPat 2007

  8. Temporal dynamics of A/H1N1 across the US, 2006-07 season Nelson et al, Plos Pathogens 2007

  9. Hierarchicalspread of influenza in the US R=1.35 Couplingi,jPopiaiPopjaj/dijg R=1.89 R=1.35 Model fitted to long-term influenza epidemiological records Viboudet al, Science, 2006

  10. Phylogeographicanalysisof 2009 springpandemicwave Lemeyet al., 2009 PLoSCurr

  11. Epidemiologicalmodels of spring 2009 pandemic diffusion Balcanet al., Plos Currents 2009; Bajardi et al Plos One 2011; Hosseini et al Plos One 2010

  12. Epidemiologicmodels of fallwave of 2009 pandemic Tizzoniet al., BMC Med 2012

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

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

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

  16. Fallpandemicoutbreakat UC. San Diego • 24-33 separate introductions • 7 clusters • - No clustering by time, age, gender or geography 29,000 students 55 full genome H1N1pdm Holmes et al., J Virol 2011

  17. In contrast, much clearer spatial patterns of the influenza virus in swine Viral introductions between regions, based on Markov jump counts 9.4 13.1 0.4 Southern source populations 3.3 0.1 Midwestern sink populations Nelson et al., PLoSPathog 2011

  18. Model testing Best-fit swine flu model Nelson et al. PLoSPathog2011

  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. Inference of temporal patterns

  21. Inference of keyepidemiologicalparametersearly in a pandemicoutbreak: R0, Tg Sequence data TMRCA: Jan-12-09 (Nov-03 to Mar-2) Epidata Fraser et al, Science, 2009

  22. Influenza seasonality

  23. Tracking population dynamicsthrough time Captures differences in seasonality and viral diversitybetweenregions Rambaut et al, Nature, 2008; Bahl et al, PNAS, 2012

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

  25. Sampling issues: region and viral subtype No. sequencesavailable Viboudet al. Phil Trans Roy Soc 2013

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

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

  28. Areas for future research • Sampling • EstimateRfrom influenza sequence data for « typical » epidemicseason • 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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