This paper is in the following e-collection/theme issue:

Population and Public Health Informatics (106) Use Cases in Human, Animal, and Zoonotic Communicable/Infectious Disease Surveillance (132)

Published on in Vol 3, No 3 (2011):

Probabilistic, Decision-theoretic Disease Surveillance and Control

Probabilistic, Decision-theoretic Disease Surveillance and Control

Probabilistic, Decision-theoretic Disease Surveillance and Control

Authors of this article:

Michael Wagner1 ;   Fuchiang Tsui1 ;   Gregory Cooper1 ;   Jeremy Espino1 ;   Hendrik Harkema1 ;   John Levander1 ;   Ricardo Villamarin1 ;   Ronald Voorhees2 ;   Nicholas Millett1 ;   Christopher Keane2 ;   Anind Dey3 ;   Manik Razdan2 ;   Yang Hu4 ;   Ming Tsai1 ;   Shawn Brown2 ;   Bruce Y Lee2 ;   Anthony Gallagher5 ;   Margaret Potter2
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Journals

  1. Gwenzi W, Skirmuntt E, Musvuugwa T, Teta C, Halabowski D, Rzymski P. Grappling with (re)-emerging infectious zoonoses: Risk assessment, mitigation framework, and future directions. International Journal of Disaster Risk Reduction 2022;82:103350 View
  2. Vial F, Berezowski J. A practical approach to designing syndromic surveillance systems for livestock and poultry. Preventive Veterinary Medicine 2015;120(1):27 View
  3. Dawson P, Gailis R, Meehan A. Detecting disease outbreaks using a combined Bayesian network and particle filter approach. Journal of Theoretical Biology 2015;370:171 View
  4. Yuan M, Boston-Fisher N, Luo Y, Verma A, Buckeridge D. A systematic review of aberration detection algorithms used in public health surveillance. Journal of Biomedical Informatics 2019;94:103181 View
  5. Ye Y, Wagner M, Cooper G, Ferraro J, Su H, Gesteland P, Haug P, Millett N, Aronis J, Nowalk A, Ruiz V, López Pineda A, Shi L, Van Bree R, Ginter T, Tsui F, Chuang J. A study of the transferability of influenza case detection systems between two large healthcare systems. PLOS ONE 2017;12(4):e0174970 View
  6. Aronis J, Millett N, Wagner M, Tsui F, Ye Y, Ferraro J, Haug P, Gesteland P, Cooper G. A Bayesian system to detect and characterize overlapping outbreaks. Journal of Biomedical Informatics 2017;73:171 View
  7. Craig A, Leong R, Donoghoe M, Muscatello D, Mojica V, Octavo C. Comparison of statistical methods for the early detection of disease outbreaks in small population settings. IJID Regions 2023;8:157 View
  8. Waitman L, Bailey L, Becich M, Chung-Bridges K, Dusetzina S, Espino J, Hogan W, Kaushal R, McClay J, Merritt J, Rothman R, Shenkman E, Song X, Nauman E. Avenues for Strengthening PCORnet’s Capacity to Advance Patient-Centered Economic Outcomes in Patient-Centered Outcomes Research (PCOR). Medical Care 2023;61(12):S153 View
  9. Venkataraman G, Pineda A, Bear Don’t Walk IV O, Zehnder A, Ayyar S, Page R, Bustamante C, Rivas M, Clegg S. FasTag: Automatic text classification of unstructured medical narratives. PLOS ONE 2020;15(6):e0234647 View
  10. Okawa M, Iwata T, Tanaka Y, Kurashima T, Toda H, Kashima H. Context-aware spatio-temporal event prediction via convolutional Hawkes processes. Machine Learning 2022;111(8):2929 View
  11. Maro J, Fryback D, Lieu T, Lee G, Martin D, McVernon J. Responding to Vaccine Safety Signals during Pandemic Influenza: A Modeling Study. PLoS ONE 2014;9(12):e115553 View