Bayesian Surveillance

Bayesian Surveillance is a statistical methodology within time-series surveillance that employs Bayesian inference to sequentially update probabilistic assessments of anomalous patterns or change points, distinguishing itself by its explicit incorporation of prior knowledge and uncertainty quantification in dynamic monitoring environments.

35

Publications

1.4K

Citations

131

Authors

78

Institutions

Publications per year

2017–2026

6

Authors

131

  1. MH
    Michael Höhle

    Robert Koch Institute, Germany

    4 publications · 110 citations

    4

    h-index

  2. CR
    Carlos R. del‐Blanco

    Universidad Politécnica de Madrid, Spain

    2 publications · 93 citations

    2

    h-index

  3. CR
    Chawarat Rotejanaprasert

    Mahidol University, Thailand

    2 publications · 73 citations

    2

    h-index

  4. NG
    Narciso Garcı́a

    Universidad Politécnica de Madrid, Spain

    2 publications · 93 citations

    2

    h-index

  5. GF
    Gregory F. Cooper

    University of Pittsburgh, United States

    2 publications · 38 citations

    2

    h-index

1–5 of 131

Venues

  1. PLoS ONE

    3 publications · 64 citations

    3

    h-index

  2. Artificial Intelligence

    2 publications · 249 citations

    2

    h-index

  3. Biometrical Journal

    2 publications · 75 citations

    2

    h-index

  4. International Journal of Health Geographics

    1 publications · 58 citations

    1

    h-index

  5. Statistics in Medicine

    1 publications · 26 citations

    1

    h-index

1–5