Transmission Network Inference

Transmission Network Inference is a computational and statistical methodology within genomic and phylogenetic epidemiology that reconstructs the pathways of infectious disease spread among individuals or populations by integrating pathogen genetic sequences with epidemiological data to infer the most probable transmission links.

47

Publications

2.9K

Citations

422

Authors

147

Institutions

Publications per year

2017–2026

24

Authors

422

  1. JO
    Joel O. Wertheim

    University of California San Diego, United States

    6 publications · 604 citations

    6

    h-index

  2. SL
    Sergei L. Kosakovsky Pond

    University of California San Diego, United States

    4 publications · 533 citations

    4

    h-index

  3. DM
    Davey M. Smith

    University of California San Diego, United States

    4 publications · 528 citations

    4

    h-index

  4. SR
    Sanjay R. Mehta

    University of California San Diego, United States

    4 publications · 524 citations

    4

    h-index

  5. MA
    Mark A. Stevenson

    The University of Melbourne, Australia

    3 publications · 55 citations

    3

    h-index

1–5 of 422

Venues

  1. PLoS ONE

    5 publications · 280 citations

    5

    h-index

  2. Scientific Reports

    3 publications · 82 citations

    3

    h-index

  3. The Journal of Infectious Diseases

    JID · 3 publications · 377 citations

    3

    h-index

  4. Journal of The Royal Society Interface

    2 publications · 91 citations

    2

    h-index

  5. PLoS Computational Biology

    2 publications · 223 citations

    2

    h-index

1–5