International Society for Disease Surveillance

Open Access Code

  • Sample methods paper -- Online detection and quantification of epidemics
    Pelat C, Boëlle PY, Cowling BJ, Carrat F, Flahault A, Ansart S, Valleron AJ
    BMC Medical Informatics and Decision Making 2007, 7:29 (15 October 2007)
  • Open-source methods link -- Camile Pelat, on-line Periodic Regression Models
  • Open-access surveillance system -- CDC EARS platform for SAS or Excel
  • Accessible Alerting Algorithms Spreadsheet Tools from ISDS Conference Workshops: The following zip file contains all of the programs and documentation needed to run the Accessible Alerting Algorithms developed by Howard Burkom. To use these programs, download the file, save to your desktop and refer to the enclosed manual. AccAlertAlg_18Oct06.zip
  • surveillance -- an R package for the visualization, use and comparison of surveillance algorithms.
    The package implements the Farrington et al (1996) procedure, several types of count data CUSUMs and the generalized likelihood ratio approach in "Count data regression charts for the monitoring of surveillance time series" (2008), CSDA, 52(9), 4357–4368. Furthermore, retrospective modeling of count data time series is offered using a two-component endemic and epidemic approach.
  • www.projectmimic.com: R package for mimicking authentic health data series or generating outbreak signatures
  • Dataset and Code for the influenza epidemic detection method in "Bayesian Markov switching models for the early detection of influenza epidemics", Martínez-Beneito MA, Conesa D, López-Quílez A, López-Maside A, Stat Med. 2008 Jul 10: http://geeitema.uv.es/doc/meviepi/influenza.html
  • "The outbreak detection program described in M. Frisén, E. Andersson, and L. Schiöler, Robust outbreak surveillance of epidemics in Sweden, Statistics in Medicine 28 (2009), pp. 476-493 and other free computer programs from the Statistical Research Unit at University of Gothenburg are found at www.statistics.gu.se/ surveillance by choosing "software."
  • The R code for the STL loess-based modeling method described in Hafen RP, Anderson DE, Cleveland WS, Maciejewski R, Ebert DS, Abusalah A, Yakout M, Ouzzani M, Grannis SJ."Syndromic surveillance: STL for modeling, visualizing, and monitoring disease counts.", BMC Med Inform Decis Mak. 2009 Apr 21;9(1):21can be found at http://www.biomedcentral.com/imedia/1528885292265369/supp1.zip
  • ALPACA:  "ALPACA Light Parsing And Classifying Application (ALPACA) is a classifying tool designed for use in community-oriented software as well as in Academia. The application consists of two parts: a parsing tool for transforming raw documents into readable data, and a classifying tool for categorizing documents into user-provided classes. The application provides a user-friendly interface and a Plug-in functionality to provide a simple way to add more parsers/classifiers to the application."
  • Weka 3:  Data Mining Software in Java.  "Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes."
  • Riff/Evolve