Selected article for: "evolutionary rate and genomic surveillance"

Author: Chan, Joseph M.; Rabadan, Raul
Title: Quantifying Pathogen Surveillance Using Temporal Genomic Data
  • Document date: 2013_1_29
  • ID: u2t1x89m_29
    Snippet: In this paper, we offer a quantitative method of measuring the quality of surveillance of a particular pathogen that reflects its evolutionary rate and genetic diversity. The q2 coefficient repre- sents an easily interpretable quantitative measure of genomic surveillance that does not rely on a full phylogenetic reconstruction. A number between 0 and 1, the q2 coefficient reflects the fraction of isolates with a closest isolate in the past within.....
    Document: In this paper, we offer a quantitative method of measuring the quality of surveillance of a particular pathogen that reflects its evolutionary rate and genetic diversity. The q2 coefficient repre- sents an easily interpretable quantitative measure of genomic surveillance that does not rely on a full phylogenetic reconstruction. A number between 0 and 1, the q2 coefficient reflects the fraction of isolates with a closest isolate in the past within 2 years of evolution. However, in its simplicity, the q2 coefficient does not capture the complexity of evolutionary processes such as variable evolutionary rates, recombination, reassortment, and population genetic effects. We decided on the q2 coefficient instead of other equally valid metrics (Q1, q5, or q100) because 2 years represents the median influenza A virus vaccine update time. However, different metrics may be applicable, depending on the specific aim of the surveillance program.

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