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PLoS Pathog. 2014 Feb 20;10(2):e1003932. doi: 10.1371/journal.ppat.1003932. eCollection 2014 Feb.

Unifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza H3N2.

Author information

1
Department of Microbiology and Immunology, KU Leuven, Leuven, Belgium.
2
Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, United Kingdom ; Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America.
3
Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, United Kingdom.
4
Department of Zoology, University of Cambridge, Cambridge, United Kingdom ; World Health Organization Collaborating Center for Modeling, Evolution, and Control of Emerging Infectious Diseases, Cambridge, United Kingdom.
5
Department of Zoology, University of Cambridge, Cambridge, United Kingdom ; World Health Organization Collaborating Center for Modeling, Evolution, and Control of Emerging Infectious Diseases, Cambridge, United Kingdom ; Department of Virology, Erasmus Medical Centre, Rotterdam, Netherlands.
6
Department of Zoology, University of Oxford, Oxford, United Kingdom.
7
Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, Illinois, United States of America ; Northwestern Institute on Complex Systems, Evanston, Illinois, United States of America ; Robert-Koch-Institute, Berlin, Germany.
8
Departments of Biomathematics and Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America ; Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, California, United States of America.

Abstract

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.

PMID:
24586153
PMCID:
PMC3930559
DOI:
10.1371/journal.ppat.1003932
[Indexed for MEDLINE]
Free PMC Article

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