Statistical Burden Modeling

Statistical Burden Modeling is a computational framework that belongs to the class of epidemiological modeling methods, distinguished by its specific application to quantifying the population health impact of foodborne diseases through the integration of surveillance data, exposure pathways, and disease outcomes using probabilistic and statistical inference techniques.

24

Publications

2.9K

Citations

195

Authors

71

Institutions

Publications per year

2017–2026

11

Authors

195

  1. RL
    Roger Lustig

    George Washington University, United States

    2 publications · 126 citations

    2

    h-index

  2. FH
    François Haguinet

    GlaxoSmithKline (Belgium), Belgium

    2 publications · 126 citations

    2

    h-index

  3. RJ
    Robert J. Taylor

    Texas A&M University, United States

    2 publications · 126 citations

    2

    h-index

  4. GM
    Gonçalo Matias

    GlaxoSmithKline (Belgium), Belgium

    2 publications · 126 citations

    2

    h-index

  5. MZ
    Maigeng Zhou

    National Center for Chronic and Noncommunicable Disease Control and Prevention, China

    2 publications · 100 citations

    2

    h-index

1–5 of 195

Venues

  1. PLoS ONE

    3 publications · 172 citations

    3

    h-index

  2. Clinical Infectious Diseases

    CID · 2 publications · 98 citations

    2

    h-index

  3. Influenza and Other Respiratory Viruses

    2 publications · 77 citations

    2

    h-index

  4. BMC Infectious Diseases

    2 publications · 58 citations

    2

    h-index

  5. Epidemiology and Infection

    2 publications · 160 citations

    2

    h-index

1–5