Debian Med Epidemiology packages

Epigrass is a software for visualizing, analyzing and simulating of epidemic processes on geo-referenced networks.

EpiGrass can interact with the GRASS GIS from which it can obtain maps and other geo-referenced information. However, EpiGrass does not require an installation of the GRASS GIS for most of its features.

Functions for demographic and epidemiological analysis in the Lexis diagram, i.e. register and cohort follow-up data, including interval censored data and representation of multistate data. Also some useful functions for tabulation and plotting. Contains some epidemiological datasets.

The Epi package is mainly focused on "classical" chronic disease epidemiology. The package has grown out of the course Statistical Practice in Epidemiology using R (see http://www.pubhealth.ku.dk/~bxc/SPE).

There is A short introduction to R for Epidemiology available at http://staff.pubhealth.ku.dk/%7Ebxc/Epi/R-intro.pdf Beware that the pages 38-120 of this is merely the manual pages for the Epi package.

Epi is not the only R-package for epidemiological analysis, a package with more affinity to infectious disease epidemiology is the epitools package which is also evailable in Debian.

Epi is used in the Department of Biostatistics of the University of Copenhagen.

Elementary Epidemiological Functions for a Graduate Epidemiology / Biostatistics Course.

This package contains elementary tools for analysis of common epidemiological problems, ranging from sample size estimation, through 2x2 contingency table analysis and basic measures of agreement (kappa, sensitivity/specificity). Appropriate print and summary statements are also written to facilitate interpretation wherever possible. This package is a work in progress, so any comments or suggestions would be appreciated. Source code is commented throughout to facilitate modification. The target audience includes graduate students in various epi/biostatistics courses.

Epibasix was developed in Canada.

Functions making R easy for epidemiological calculation.

Datasets from Dbase (.dbf), Stata (.dta), SPSS(.sav), EpiInfo(.rec) and Comma separated value (.csv) formats as well as R data frames can be processed to do make several epidemiological calculations.

GNU R Tools for public health epidemiologists and data analysts. Epitools provides numerical tools and programming solutions that have been used and tested in real-world epidemiologic applications.

Many practical problems in the analysis of public health data require programming or special software, and investigators in different locations may duplicate programming efforts. Often, simple analyses, such as the construction of confidence intervals, are not calculated and thereby complicate appropriate statistical inferences for small geographic areas. There are many examples of simple and useful numerical tools that would enhance the work of epidemiologists at local health departments and yet are not readily available for the problem in front of them. The availability of these tools will encourage wider use of appropriate methods and promote evidence-based public health practices.

Functions for fitting general continuous-time Markov and hidden Markov multi-state models to longitudinal data. Both Markov transition rates and the hidden Markov output process can be modelled in terms of covariates. A variety of observation schemes are supported, including processes observed at arbitrary times, completely-observed processes, and censored states.

The R-package 'surveillance' is a framework for the development and the evaluation of outbreak detection algorithms in univariate and multivariate routine collected public health surveillance data. It is hosted on CRAN..

The intention of the R-package surveillance is to provide open source software for the visualization and monitoring of count data time series in public health surveillance. Potential users are epidemiologists and others working in applied infectious disease epidemiology. Furthermore, surveillance also provides a data structure and framework for methodological developments of surveillance algorithms.

NetEpi, which is short for "Network-enabled Epidemiology", is a collaborative project to create a suite of free, open source software tools for epidemiology and public health practice. Anyone with an interest in population health epidemiology or public health informatics is encouraged to examine the prototype tools and to consider contributing to their further development. Contributions which involve formal and/or informal testing of the tools in a wide range of circumstances and environments are particularly welcome, as is assistance with design, programming and documentation tasks.

This is a tool for conducting epidemiological analysis of data sets, both large and small, either through a Web browser interface, or via a programmatic interface. In many respects it is similar to the analysis facilities included in the Epi Info suite, except that NetEpi Analysis is designed to be installed on servers and accessed remotely via Web browsers, although it can also be installed on individual desktop or laptop computers.

The software was developed by New South Wales Department of Health.

There is also a Google code project page at http://code.google.com/p/netepi/

See also: http://www.stockholmchallenge.se/data/2123 and http://www.publish.csiro.au/?act=view_file&file_id=NB07103.pdf

NetEpi, which is short for "Network-enabled Epidemiology", is a collaborative project to create a suite of free, open source software tools for epidemiology and public health practice. Anyone with an interest in population health epidemiology or public health informatics is encouraged to examine the prototype tools and to consider contributing to their further development. Contributions which involve formal and/or informal testing of the tools in a wide range of circumstances and environments are particularly welcome, as is assistance with design, programming and documentation tasks.

NetEpi Case Manager is a tool for securely collecting structured information about cases and contacts of communicable (and other) diseases through Web browsers and the Internet. New data collection forms can be designed and deployed quickly by epidemiologists, using a "point-and-click" interface, without the need for knowledge of or training in any programming language. Data can then be collected from users of the system, who can be located anywhere in the world, into a centralised database. All that is needed by users of the system is a relatively recent Web browser and an Internet connection ("NetEpi" is short for "Network-enabled Epidemiology"). In many respects, NetEpi Case Manager is like a Web-enabled version of the data entry facilities in the very popular Epi Info suite of programmes published by the US Centers for Disease Control and Prevention, and in the Danish EpiData project, which is available for several languages. The software was developed by the Centre for Epidemiology and Research of the New South Wales Department of Health, with contributions from Population Health Division of the Australian Government Department of Health and Ageing.

The software was developed by New South Wales Department of Health.

See also: http://www.stockholmchallenge.se/data/2123 and http://www.publish.csiro.au/?act=view_file&file_id=NB07103.pdf