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Related Concept Videos

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Generating Whole Bacterial Genomes from Clinical Samples using a Target Enrichment Workflow
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Published on: August 15, 2025

Quantifying pathogen surveillance using temporal genomic data.

Joseph M Chan1, Raul Rabadan

  • 1Center for Computational Biology and Bioinformatics, Columbia University College of Physicians and Surgeons, New York, New York, USA. jmc2213@columbia.edu

Mbio
|January 31, 2013
PubMed
Summary
This summary is machine-generated.

A new q2 coefficient method quantitatively assesses genomic surveillance completeness by considering pathogen evolution and time. This tool helps identify gaps in pathogen detection for improved public health strategies.

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Area of Science:

  • * Infectious disease dynamics
  • * Molecular epidemiology
  • * Public health surveillance

Background:

  • * Genomic surveillance is crucial for detecting infectious diseases and understanding pathogen transmission.
  • * Current methods for evaluating genomic surveillance are limited, failing to account for pathogen evolutionary rates and sequence collection timing.
  • * Non-uniform detection systems create significant gaps in geographic coverage and animal reservoir sampling.

Purpose of the Study:

  • * To introduce a novel quantitative method, the q2 coefficient, for assessing genomic surveillance completeness.
  • * To incorporate pathogen evolutionary rates and sequence collection time into a standardized surveillance evaluation.
  • * To guide public health agencies in optimizing resource allocation and virus collection efforts.

Main Methods:

  • * Developed the q2 coefficient, measuring the proportion of sequenced isolates with a recent evolutionary neighbor (within 2 years).
  • * Applied the q2 coefficient to genomic data from influenza A, dengue, and West Nile viruses.
  • * Analyzed surveillance completeness across different host species, geographic locations, and time periods.

Main Results:

  • * The q2 coefficient effectively identified deficiencies in influenza A virus surveillance in swine and avian populations.
  • * Demonstrated the method's applicability to other viruses like dengue and West Nile virus.
  • * Revealed surveillance gaps in specific geographic regions and highlighted the impact of public health policies.

Conclusions:

  • * The q2 coefficient provides a rapid, interpretable, and standardized approach to evaluating genomic surveillance.
  • * This method can pinpoint areas needing improved pathogen sampling and inform public health policy.
  • * Enhanced genomic surveillance through the q2 coefficient is vital for pandemic preparedness and infectious disease control.