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Incorporating Epidemiological Data into the Genomic Analysis of Partially Sampled Infectious Disease Outbreaks.

Jake Carson1,2, Matt Keeling1,2, Paolo Ribeca3

  • 1Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK.

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This study introduces a new algorithm to combine pathogen genomic and epidemiological data for reconstructing infectious disease outbreaks. Integrating these data sources enhances outbreak understanding and transmission link accuracy.

Keywords:
epidemiological datagenomic epidemiologyinfectious disease outbreaktransmission analysis

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Area of Science:

  • Epidemiology
  • Genomics
  • Computational Biology

Background:

  • Pathogen genomic data are crucial for understanding infectious disease transmission dynamics.
  • Integrating genomic and epidemiological data is challenging, especially in partially sampled outbreaks with missing intermediate transmission links.

Purpose of the Study:

  • To develop an efficient dynamic programming algorithm for reconstructing partially sampled outbreaks using combined genomic and epidemiological data.
  • To improve the accuracy of transmission link inference and estimate epidemiological parameters.
  • To apply the methodology to real-world infectious disease outbreaks.

Main Methods:

  • Development and implementation of a new dynamic programming algorithm within the TransPhylo framework.
  • Utilizing simulated datasets to validate the algorithm's performance.
  • Application to tuberculosis outbreak data (including HIV status) in Argentina.
  • Application to avian influenza H7N7 epidemic data (including geographical data) in the Netherlands.

Main Results:

  • The new algorithm efficiently reconstructs partially sampled outbreaks by integrating genomic and epidemiological data.
  • Including epidemiological data significantly improves the accuracy of inferred transmission links compared to genomic data alone.
  • The method enables estimation of specific epidemiological parameters, such as transmission rates between groups.
  • Analysis of real-world outbreaks revealed insights into the role of HIV coinfection in tuberculosis spread and the spatial epidemiology of avian influenza.

Conclusions:

  • Combining pathogen genomic and epidemiological data provides a more comprehensive understanding of infectious disease outbreaks.
  • The developed methodology offers an efficient and accurate approach for outbreak reconstruction and analysis.
  • This integrated approach is valuable for investigating the influence of epidemiological factors on disease spread and informing public health interventions.