Unifying Viral Genetics and Human Transportation Data to Predict the Global Transmission Dynamics of Human Influenza H3N2

Information on global human movement patterns is central to spatial epidemiological models used to predict the behavior of influenza and other infectious diseases. Yet it remains difficult to test which modes of dispersal drive pathogen spread at various geographic scales using standard epidemiological data alone. Evolutionary analyses of pathogen genome sequences increasingly provide insights into the spatial dynamics of influenza viruses, but to date they have largely neglected the wealth of information on human mobility, mainly because no statistical framework exists within which viral gene sequences and empirical data on host movement can be combined. Here, we address this problem by applying a phylogeographic approach to elucidate the global spread of human influenza subtype H3N2 and assess its ability to predict the spatial spread of human influenza A viruses worldwide. Using a framework that estimates the migration history of human influenza while simultaneously testing and quantifying a range of potential predictive variables of spatial spread, we show that the global dynamics of influenza H3N2 are driven by air passenger flows, whereas at more local scales spread is also determined by processes that correlate with geographic distance. Our analyses further confirm a central role for mainland China and Southeast Asia in maintaining a source population for global influenza diversity. By comparing model output with the known pandemic expansion of H1N1 during 2009, we demonstrate that predictions of influenza spatial spread are most accurate when data on human mobility and viral evolution are integrated. In conclusion, the global dynamics of influenza viruses are best explained by combining human mobility data with the spatial information inherent in sampled viral genomes. The integrated approach introduced here offers great potential for epidemiological surveillance through phylogeographic reconstructions and for improving predictive models of disease control.

[1]  L. A. Rvachev,et al.  A mathematical model for the global spread of influenza , 1985 .

[2]  I M Longini,et al.  Predicting the global spread of new infectious agents. , 1986, American journal of epidemiology.

[3]  A. Flahault,et al.  Modelling the 1985 influenza epidemic in France. , 1988, Statistics in medicine.

[4]  Z. Yang,et al.  A space-time process model for the evolution of DNA sequences. , 1995, Genetics.

[5]  W. Fitch,et al.  Predicting the evolution of human influenza A. , 1999, Science.

[6]  Edward I. George,et al.  The Practical Implementation of Bayesian Model Selection , 2001 .

[7]  D. Gillespie.  Approximate accelerated stochastic simulation of chemically reacting systems , 2001 .

[8]  M. Suchard,et al.  Bayesian selection of continuous-time Markov chain evolutionary models. , 2001, Molecular biology and evolution.

[9]  R. Webster,et al.  Are We Ready for Pandemic Influenza? , 2003, Science.

[10]  G. Glass,et al.  Assessing the impact of airline travel on the geographic spread of pandemic influenza , 2003 .

[11]  O. Pybus,et al.  Unifying the Epidemiological and Evolutionary Dynamics of Pathogens , 2004, Science.

[12]  J. H. Ellis,et al.  Modeling the Spread of Annual Influenza Epidemics in the U.S.: The Potential Role of Air Travel , 2004, Health care management science.

[13]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  M. Pagel,et al.  Bayesian estimation of ancestral character states on phylogenies. , 2004, Systematic biology.

[15]  R. Guimerà,et al.  The worldwide air transportation network: Anomalous centrality, community structure, and cities' global roles , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[16]  H. Kishino,et al.  Dating of the human-ape splitting by a molecular clock of mitochondrial DNA , 2005, Journal of Molecular Evolution.

[17]  Mark A. Miller,et al.  Synchrony, Waves, and Spatial Hierarchies in the Spread of Influenza , 2006, Science.

[18]  Alexei J Drummond,et al.  Choosing appropriate substitution models for the phylogenetic analysis of protein-coding sequences. , 2006, Molecular biology and evolution.

[19]  H. Philippe,et al.  Computing Bayes factors using thermodynamic integration. , 2006, Systematic biology.

[20]  J. Brownstein,et al.  Empirical Evidence for the Effect of Airline Travel on Inter-Regional Influenza Spread in the United States , 2006, PLoS medicine.

[21]  S. Ho,et al.  Relaxed Phylogenetics and Dating with Confidence , 2006, PLoS biology.

[22]  M. Newton,et al.  Estimating the Integrated Likelihood via Posterior Simulation Using the Harmonic Mean Identity , 2006 .

[23]  Cécile Viboud,et al.  Air Travel and the Spread of Influenza: Important Caveats , 2006, PLoS medicine.

[24]  M. Suchard,et al.  Smooth skyride through a rough skyline: Bayesian coalescent-based inference of population dynamics. , 2008, Molecular biology and evolution.

[25]  C. Viboud,et al.  Explorer The genomic and epidemiological dynamics of human influenza A virus , 2016 .

[26]  Kate E. Jones,et al.  Global trends in emerging infectious diseases , 2008, Nature.

[27]  Marc A Suchard,et al.  Counting labeled transitions in continuous-time Markov models of evolution , 2007, Journal of mathematical biology.

[28]  Colin A. Russell,et al.  The Global Circulation of Seasonal Influenza A (H3N2) Viruses , 2008, Science.

[29]  J. Huelsenbeck,et al.  Efficiency of Markov chain Monte Carlo tree proposals in Bayesian phylogenetics. , 2008, Systematic biology.

[30]  Marc A Suchard,et al.  Fast, accurate and simulation-free stochastic mapping , 2008, Philosophical Transactions of the Royal Society B: Biological Sciences.

[31]  Edward C. Holmes,et al.  Discovering the Phylodynamics of RNA Viruses , 2009, PLoS Comput. Biol..

[32]  Marc A. Suchard,et al.  Many-core algorithms for statistical phylogenetics , 2009, Bioinform..

[33]  E. Lyons,et al.  Pandemic Potential of a Strain of Influenza A (H1N1): Early Findings , 2009, Science.

[34]  Alexei J. Drummond,et al.  Bayesian Phylogeography Finds Its Roots , 2009, PLoS Comput. Biol..

[35]  M. Suchard,et al.  Phylodynamics and Human-Mediated Dispersal of a Zoonotic Virus , 2010, PLoS pathogens.

[36]  M. Suchard,et al.  Bayesian random local clocks, or one rate to rule them all , 2010, BMC Biology.

[37]  D. Brockmann,et al.  Human Mobility and Spatial Disease Dynamics , 2010 .

[38]  Andrew Nelson,et al.  Agglomeration Index : Towards a New Measure of Urban Concentration , 2010 .

[39]  D. Brockmann,et al.  The Structure of Borders in a Small World , 2010, PLoS ONE.

[40]  M. Pascual,et al.  Global Migration Dynamics Underlie Evolution and Persistence of Human Influenza A (H3N2) , 2010, PLoS pathogens.

[41]  Fabian J. Theis,et al.  Modularity maximization and tree clustering: Novel ways to determine effective geographic borders , 2011, ArXiv.

[42]  Dirk Brockmann,et al.  Complexity in human transportation networks: a comparative analysis of worldwide air transportation and global cargo-ship movements , 2011, ArXiv.

[43]  Marc A Suchard,et al.  A Bayesian phylogenetic method to estimate unknown sequence ages. , 2011, Molecular biology and evolution.

[44]  Ming-Hui Chen,et al.  Improving marginal likelihood estimation for Bayesian phylogenetic model selection. , 2011, Systematic biology.

[45]  Hui-Wen Chang,et al.  Molecular epidemiology and antigenic analyses of influenza A viruses H3N2 in Taiwan. , 2011, Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases.

[46]  Ming-Hui Chen,et al.  Choosing among Partition Models in Bayesian Phylogenetics , 2010, Molecular biology and evolution.

[47]  M. Suchard,et al.  Impact of CCR5delta32 host genetic background and disease progression on HIV-1 intrahost evolutionary processes: efficient hypothesis testing through hierarchical phylogenetic models. , 2011, Molecular biology and evolution.

[48]  Yi Guan,et al.  Temporally structured metapopulation dynamics and persistence of influenza A H3N2 virus in humans , 2011, Proceedings of the National Academy of Sciences.

[49]  Alessandro Vespignani,et al.  The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale , 2011, BMC infectious diseases.

[50]  Forrest W. Crawford,et al.  Unifying the spatial epidemiology and molecular evolution of emerging epidemics , 2012, Proceedings of the National Academy of Sciences.

[51]  Panos M. Pardalos,et al.  Handbook of Optimization in Complex Networks , 2012 .

[52]  Catherine Linard,et al.  Large-scale spatial population databases in infectious disease research , 2012, International Journal of Health Geographics.

[53]  M. Suchard,et al.  Improving the accuracy of demographic and molecular clock model comparison while accommodating phylogenetic uncertainty. , 2012, Molecular biology and evolution.

[54]  M. Suchard,et al.  Bayesian Phylogenetics with BEAUti and the BEAST 1.7 , 2012, Molecular biology and evolution.

[55]  Wai Lok Sibon Li,et al.  Accurate model selection of relaxed molecular clocks in bayesian phylogenetics. , 2012, Molecular biology and evolution.

[56]  Jie Dong,et al.  Human Infection with a Novel Avian-Origin Influenza A (H7N9) Virus. , 2018 .

[57]  Guy Baele,et al.  Bayesian evolutionary model testing in the phylogenomics era: matching model complexity with computational efficiency , 2013, Bioinform..

[58]  B. Mallick.  VARIABLE SELECTION FOR REGRESSION MODELS , 2016 .