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      Quantifying Pathogen Surveillance Using Temporal Genomic Data

      research-article
      a , a,b
      mBio
      American Society of Microbiology

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          ABSTRACT

          With the advent of deep sequencing, genomic surveillance has become a popular method for detection of infectious disease, supplementing information gathered by classic clinical or serological techniques to identify host-determinant markers and trace the origin of transmission. However, two main factors complicate genomic surveillance. First, pathogens exhibiting high genetic diversity demand higher levels of scrutiny to obtain an accurate representation of the entire population. Second, current systems of detection are nonuniform, with significant gaps in certain geographic locations and animal reservoirs. Despite past unforeseen pandemics like the 2009 swine-origin H1N1 influenza virus, there is no standardized way of evaluating surveillance. A more complete surveillance system should capture a greater proportion of pathogen diversity. Here we present a novel quantitative method of assessing the completeness of genomic surveillance that incorporates the time of sequence collection, as well as the pathogen’s evolutionary rate. We propose the q2 coefficient, which measures the proportion of sequenced isolates whose closest neighbor in the past is within a genetic distance equivalent to 2 years of evolution, roughly the median time of changing strain selection for influenza A vaccines. Easily interpretable and significantly faster than other methods, the q2 coefficient requires no full phylogenetic characterization or use of arbitrary clade definitions. Application of the q2 coefficient to influenza A virus confirmed poor sampling of swine and avian populations and identified regions with deficient surveillance. We demonstrate that the q2 coefficient can not only be applied to other pathogens, including dengue and West Nile viruses, but also used to describe surveillance dynamics, particularly the effects of different public health policies.

          IMPORTANCE

          Surveillance programs have become key assets in determining the emergence or prevalence of pathogens circulating in human and animal populations. Genomic surveillance, in particular, provides comprehensive information on the history of isolates and potential molecular markers for infectivity and pathogenicity. Current techniques for evaluating genomic surveillance are inaccurate, ignoring the pathogen’s evolutionary rate and biodiversity, as well as the timing of sequence collection. Using sequence data, we propose the q2 coefficient as a quantitative measure of surveillance completeness that combines elements of time and evolution without defining arbitrary criteria for clades or species. Through several case studies of influenza A, dengue, and West Nile viruses, we employed the q2 coefficient to identify sampling deficiencies in different host species and locations, as well as examine the effects of different public health policies through historical records of the q2 coefficient. These results can guide public health agencies to focus resource allocation and virus collection to bolster specific problems in surveillance.

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          Most cited references40

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          Emerging Infectious Diseases of Wildlife-- Threats to Biodiversity and Human Health

          P. Daszak (2000)
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            Factors in the emergence of infectious diseases.

            "Emerging" infectious diseases can be defined as infections that have newly appeared in a population or have existed but are rapidly increasing in incidence or geographic range. Among recent examples are HIV/AIDS, hantavirus pulmonary syndrome, Lyme disease, and hemolytic uremic syndrome (a foodborne infection caused by certain strains of Escherichia coli). Specific factors precipitating disease emergence can be identified in virtually all cases. These include ecological, environmental, or demographic factors that place people at increased contact with a previously unfamiliar microbe or its natural host or promote dissemination. These factors are increasing in prevalence; this increase, together with the ongoing evolution of viral and microbial variants and selection for drug resistance, suggests that infections will continue to emerge and probably increase and emphasizes the urgent need for effective surveillance and control. Dr. David Satcher's article and this overview inaugurate Perspectives, a regular section in this journal intended to present and develop unifying concepts and strategies for considering emerging infections and their underlying factors. The editors welcome, as contributions to the Perspectives section, overviews, syntheses, and case studies that shed light on how and why infections emerge, and how they may be anticipated and prevented.
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              Molecular basis for the generation in pigs of influenza A viruses with pandemic potential.

              Genetic and biologic observations suggest that pigs may serve as "mixing vessels" for the generation of human-avian influenza A virus reassortants, similar to those responsible for the 1957 and 1968 pandemics. Here we demonstrate a structural basis for this hypothesis. Cell surface receptors for both human and avian influenza viruses were identified in the pig trachea, providing a milieu conducive to viral replication and genetic reassortment. Surprisingly, with continued replication, some avian-like swine viruses acquired the ability to recognize human virus receptors, raising the possibility of their direct transmission to human populations. These findings help to explain the emergence of pandemic influenza viruses and support the need for continued surveillance of swine for viruses carrying avian virus genes.
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                Author and article information

                Journal
                mBio
                MBio
                mbio
                mbio
                mBio
                mBio
                American Society of Microbiology (1752 N St., N.W., Washington, DC )
                2150-7511
                29 January 2013
                Jan-Feb 2013
                : 4
                : 1
                : e00524-12
                Affiliations
                Center for Computational Biology and Bioinformatics [ a ] and
                Department of Biomedical Informatics, [ b ] Columbia University College of Physicians and Surgeons, New York, New York, USA
                Author notes
                Address correspondence to Joseph M. Chan, jmc2213@ 123456columbia.edu .

                Editor Rino Rappuoli, Novartis Vaccines and Diagnostics

                Article
                mBio00524-12
                10.1128/mBio.00524-12
                3560527
                23362319
                fff28cf2-ddf0-4490-9476-c5c168a3b2c2
                Copyright © 2013 Chan and Rabadan

                This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-ShareAlike 3.0 Unported license, which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 14 November 2012
                : 29 November 2012
                Page count
                Pages: 10
                Categories
                Research Article
                Custom metadata
                January/February 2013

                Life sciences
                Life sciences

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