Debian Med Project
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Summary
Epidemiology
Epidemiologirelaterede pakker for Debian Med

Denne metapakke vil installere værktøjer, som er brugbare i epidemiologisk forskning. Flere pakker gør brug af GNU R-datasproget for statistiske undersøgelser. Det kan være en god ide at læse den engelske artikel »A short introduction to R for Epidemiology«, som kan ses her http://staff.pubhealth.ku.dk/%7Ebxc/Epi/R-intro.pdf

Description

For a better overview of the project's availability as a Debian package, each head row has a color code according to this scheme:

If you discover a project which looks like a good candidate for Debian Med to you, or if you have prepared an unofficial Debian package, please do not hesitate to send a description of that project to the Debian Med mailing list

Links to other tasks

Debian Med Epidemiology packages

Official Debian packages with high relevance

Epigrass
videnskabeligt værktøj for simulationer og scenarieanalyse i netværksepidemilogi
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Epigrass er et program for visualisering, analyse og simulering af epidemiske processer på geo-refererede netværk.

EpiGrass kan interagere med GRASS GIS hvorfra den kan hente kort og andre georefererede informationer. EpiGrass kræver dog ikke en installation af GRASS GIS for de fleste af sine funktioner.

The package is enhanced by the following packages: epigrass-doc
Please cite: Flavio Coelho, Oswaldo Cruz and Claudia Codeco: Epigrass: a tool to study disease spread in complex networks. (PubMed) Source Code for Biology and Medicine 3(1):3 (2008)
Registry entries: OMICtools 
R-cran-diagnosismed
Præcisionsevaluering af diagnostisk test for læger
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DiagnosisMed er en GNU R-pakke til analyse af data fra præcisionsevalueringer af diagnostiske test af sundhedsbetingelser. Det bliver udviklet til anvendelse i sundhedsvæsenet. Pakken er i stand til at estimere følsomhed og specificitet fra kategoriske og fortløbende testresultater, inklusive enkelte evalueringer af ubestemmelige resultater, eller sammenligning af forskellige kategoriske test, samt estimering af rimelige afgrænsninger af test. Dette vises på en måde som ofte anvendes i sundhedsvæsenet. Der er endnu ingen grafisk grænseflade.

R-cran-epi
GNU R epidemiologisk analyse
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Funktioner til demografisk og epidemiologisk analyse i Lexis-programmet, dvs. register- og kohort-opfølgningsdata, inklusive intervalcensorerede data og repræsentation af data i flere tilstande. Indeholder også nyttige funktioner til tabulering og plotning. Indeholder også nogle epidemiologisk datasæt.

Epi-pakken er primært fokuseret på "klassisk" kronisk sydomsepidemiologi. Pakken er vokset ud af kurset "Statistical Practice in Epidemiology using R" (se http://www.pubhealth.ku.dk/~bxc/SPE).

Der er en kort introduktion til epidemiologi med R tilgængelig på http://staff.pubhealth.ku.dk/%7Ebxc/Epi/R-intro.pdf Vær opmærksom på at siderne 38-120 fra denne blot er manualsiderne til Epi- pakken.

Epi er ikke den eneste R-pakke til epidemiologisk analyse. En pakke med tættere tilhørsforhold til smitsom sygdomsepidemiologi er pakken epitools, der også er tilgængelig gennem Debian.

Epi anvendes af Biostatistisk Afdeling ved Københavns Universitet.

Please cite: Martyn Plummer and Bendix Carstensen: Lexis: An R Class for Epidemiological Studies with Long-Term Follow-Up. Journal of Statistical Software 38(5):1-12 (2011)
R-cran-epibasix
GNU R Elementary Epidemiological-funktioner
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Elementary Epidemiological-funktioner for et Epidemiology/Biostatistics-forskningskursus.

Denne pakke indeholder elementære værktøjer for analyse af gængse epidemiologiske problemer, fra estimering af prøvestørrelse, via 2x2 kontingens tabelanalyse og grundlæggende målinger for aftale (kappa, sensitivitet). Passende udskrivnings- og summeringsudtryk skrives også for at facilitere fortolkning når det er muligt. Denne pakke er under udvikling, så der tages godt imod alle kommentarer eller forslag. Kildekoden er kommenteret udførligt for at facilitere ændring. Målgruppen inkluderer forskningsstuderende i forskellige epi/biostatistiske kurser.

Epibasix blev udviklet i Canada.

R-cran-epicalc
GNU R epidemiologisk beregner
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Funktioner der gør det let at foretage epidemiologiske beregninger med R.

Flere datasæt fra formaterne Dbase (.dbf), Stata (.dta), SPSS (.sav), EpiInfo (.rec) og kommaseparerede værdier (.csv) såvel som R-datarammer, kan behandles til foretagelse af adskillige epidemiologiske beregninger.

R-cran-epir
GNU R-funktioner for analyse af epidemiologiske data
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En pakke til analyse af epidemiologiske data. Indeholder funktioner til direkte og indirekte at justere mål for sygdomsfrekvens, kvantificere associationsforanstaltninger på grundlag af en enkelt eller flere lag af optalte data præsenteret i en antalstabel og beregning af konfidensintervaller omkring risikoforekomst og skøn for udbrud. Hjælpefunktioner til brug i metaanalyse, diagnostiske testfortolkninger og beregninger af størrelse på stikprøve.

R-cran-epitools
GNU R epidemiologi-værktøjer til data og grafik
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GNU R-værktøjer til epidemiologer og dataanalytikere i den offentlige sundhedssektor. Epitools tilbyder talmæssige værktøjer og programmeringsløsninger som er blevet anvendt og testet i epidemiologiske anvendelsesområder fra den virkelige verden.

Mange praktiske problemer i analysen af offentlige sundhedsdata kræver programmering eller specialprogrammel, og undersøgelsespersonale på forskellige steder kan duplikere programmeringsindsatsen. Ofte vil simple analyser, såsom konstruktionen af fortrolighedsintervaller, ikke blive beregnet og dermed komplicere passende statistiske slutninger for mindre geografiske områder. Der er mange eksempler på simple og talmæssige værktøjer som ville forbedre epidemiologers arbejde på lokale sundhedsafdelinger, og og endnu ikke er parate og tilgængelige til problemet foran dem. Tilgængeligheden af disse værktøjer vil fremme bredere anvendelse af passende metoder og fremme evidensbaseret praksis i den offentlige sundhedssektor.

R-cran-lexrankr
extractive summarization of text with the LexRank algorithm
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An R implementation of the LexRank algorithm implementing stochastic graph-based method for computing relative importance of textual units for Natural Language Processing. The technique on the problem of Text Summarization (TS) is tested. Extractive TS relies on the concept of sentence salience to identify the most important sentences in a document or set of documents. Salience is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence.

Please cite: Güneş Erkan and Dragomir R. Radev: LexRank: Graph-based Lexical Centrality as Salience in Text Summarization. (eprint) Journal of Artific Intelligence Research 22:457-479 (2004)
R-cran-seroincidence
GNU R seroincidence - lommeregnerværktøj
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Antistofniveauer målt i en tværsnitsundersøgelser af befolkningen, kan oversættes til et skøn over, hvor hyppigt serokonversioner (nye infektioner) forekommer. For at fortolke det målte tværsnitsbillede for antistofniveauer, skal parametre som forudsiger henfaldet af antistoffer være kendt. I tidligere udgivede rapporter (Simonsen et al. 2009 og Versteegh et al. 2005), er der indhentet disse oplysninger fra longitudinelle studier om emner, der havde kulturbekræftede salmonella- og campylobacterinfektioner. En Bayesiansk tilbageberegningsmodel blev anvendt til at omdanne antistofmålinger til en estimering af tid siden infektion. Dette kan anvendes til at estimere seroincidence i tværsnitsstikprøven af ​ ​befolkningen. For både den langsgående måling og tværsnitsmålingen af antistofkoncentrationer blev den indirekte ELISA anvendt. Modellerne gælder kun for personer over 18 år. Seroincidence-estimaterne er egnede til overvågning af effekten af kontrolprogrammer, når repræsentative tværsnitsserumprøver er til rådighed for analyser. Disse giver mere præcise oplysninger om infektionspres i mennesker på tværs af lande.

Please cite: PFM Teunis, JCH van Eijkeren, CW Ang, YTHP van Duynhoven, JB Simonsen, MA Strid and W van Pelt: Biomarker dynamics: estimating infection rates from serological data. (PubMed) Statistics in Medicine 31(20):2240–2248 (2012)
R-cran-sf
Simple Features for R
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Support for simple features, a standardized way to encode spatial vector data. Binds to 'GDAL' for reading and writing data, to 'GEOS' for geometrical operations, and to 'PROJ' for projection conversions and datum transformations.

R-cran-sjplot
GNU R data visualization for statistics in social science
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Collection of plotting and table output functions for data visualization. Results of various statistical analyses (that are commonly used in social sciences) can be visualized using this package, including simple and cross tabulated frequencies, histograms, box plots, (generalized) linear models, mixed effects models, principal component analysis and correlation matrices, cluster analyses, scatter plots, stacked scales, effects plots of regression models (including interaction terms) and much more. This package supports labelled data.

R-cran-surveillance
GNU R-pakke for modellering og overvågning af epidemiologiske udbrud
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Implementering af statiske metoder for modellering og detektering af ændringspunkt i tidsserier for antal, proportioner og kategoridata, samt modellering af fortsættende epidemiologiske udbrud, f.eks. »discrete-space«-opsætninger såsom de delvist berigede Susceptible-Exposed-Infectious-Recovered-modeller (SEIR) eller fortsættende punktprocesdata såsom forekomsten af smitsomme sygdomme. Hovedfokus er detektering af udbrud i antal datatidsserier, der kommer fra offentlige sygdomsovervågning af smitsomme sygdomme, men anvendelsen kunne også komme fra miljømålinger, troværdighedsmålinger, økonometri eller samfundsvidenskab.

I øjeblikket indeholder pakken implementeringer af mange typiske detekteringsprocedurer for udbrud såsom Farrington et al (1996), Noufaily et al (2012) eller den negative binomial LR-CUSUM-metode beskrevet i Höhle og Paul (2008). En CUSUM-fremgangsmåde for begyndere der kombinerer logistik og flernomial logistisk modellering er også inkluderet. Derudover tilbydes inferensmetoder for de retrospektive smitsomme sygdomsmodeller i Held et al (2005), Held et al (2006), Paul et al (2008), Paul og Held (2011), Held og Paul (2012) og Meyer og Held (2014).

Fortsættende selvspændende spatio-temporal punktprocesser er modelleret via additve-multiplikative betingede intensiteter som beskrevet i Höhle (2009) (»twinSIR«, discrete space) og Meyer et al (2012) (»twinstim«, continuous space).

Denne pakke indeholder flere datasæt fra den virkelige verden, evnen til at simulere udbrudsdata, visualisere resultaterne af overvågningen i temporal, delvis eller spatio-temporal betydning.

Bemærk: Brug af algoritmen boda kræver pakken INLA, som er tilgængelig fra http://www.r-inla.org/download.

Please cite: Michael Höhle: Surveillance: An R package for the surveillance of infectious diseases. (eprint) Computational Statistics 22(4):571-582 (2007)

Official Debian packages with lower relevance

R-cran-cmprsk
GNU R subdistribution analysis of competing risks
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This GNU R package supports estimation, testing and regression modeling of subdistribution functions in competing risks, as described in Gray (1988), A class of K-sample tests for comparing the cumulative incidence of a competing risk.

Please cite: Jason P. Fine and Robert J. Gray: A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 94(446):496-509 (1999)
R-cran-msm
GNU R Multi-state Markov og skjulte Markovmodeller i sammenhængende tid
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Funktioner for tilpasning af generel Markov i sammenhængende tid og skjulte Markov flertilstandsmodeller til data i længderetningen. Både overgangsrater for Markov og den skjulte Markov-resultatproces kan modelleres i form af kovariater. Et udvalg af observationsskemaer er understøttet, inklusive processer observeret på arbitrære tidspunkter, fuldstændig-observerede processer, og censorerede tilstande.

Please cite: Christopher H. Jackson: Multi-State Models for Panel Data: The msm Package for R. Journal of Statistical Software 38(8):1-29 (2011)

Packaging has started and developers might try the packaging code in VCS

Epifire
model the spread of an infectious disease in a population
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EpiFire is a C++ applications programming interface (API) that does two things:

  • Model the spread of an infectious disease in a population
  • Generate and manipulate networks of nodes and edges

While the network code can be used independently from the epidemiological code and vice versa—they are conceptually and functionally distinct—from the beginning, the libraries were developed to be compatible with each other. What EpiFire excels at is simulating the stochastic spread of disease on contact networks.

Please cite: Thomas Hladish, Eugene Melamud, Luis Alberto Barrera, Alison Galvani and Lauren Ancel Meyers: EpiFire: An open source C++ library and application for contact network epidemiology. (PubMed,eprint) BMC Bioinformatics 13:76 (2012)
Netepi-analysis
network-enabled tools for epidemiology and public health practice
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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.

Remark of Debian Med team: See also: http://www.stockholmchallenge.se/data/2123 and

http://www.publish.csiro.au/?act=view_file&file_id=NB07103.pdf

Netepi-collection
network-enabled tools for epidemiology and public health practice
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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.

Remark of Debian Med team: See also: http://www.stockholmchallenge.se/data/2123 and

http://www.publish.csiro.au/?act=view_file&file_id=NB07103.pdf

Shiny-server
put Shiny web apps online
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Version: 1.5.6.875+dfsg-1

Shiny Server lets you put shiny web applications and interactive documents online. Take your Shiny apps and share them with your organization or the world.

Shiny Server lets you go beyond static charts, and lets you manipulate the data. Users can sort, filter, or change assumptions in real-time. Shiny server empower your users to customize your analysis for their specific needs and extract more insight from the data.

Ushahidi
web platform for information collection
Versions of package ushahidi
ReleaseVersionArchitectures
VCS2.7.4-1all
Versions and Archs
License: LGPL-3+
Debian package not available
Git
Version: 2.7.4-1

Ushahidi is a platform that allows information collection, visualization and interactive mapping, allowing anyone to submit information through text messaging using a mobile phone, email or web form.

It can be used to monitor epidemic diseases, measuring the impact of natural disasters, uncovering corruption, and empowering peace makers.

No known packages available but some record of interest (WNPP bug)

Repast - wnpp
framework for creating agent based simulations
License: BSD
Debian package not available

Repast Simphony is a free and open source agent-based modeling toolkit that simplifies model creation and use. Repast Simphony offers users a rich variety of features including the following:

  • Fluid model component development using any mixture of Java, Groovy, and flowcharts in each project;
  • A pure Java point-and-click model execution environment that includes built-in results logging and graphing tools as well as automated connections to a variety of optional external tools including the R statistics environment, *ORA and Pajek network analysis plugins, A live agent SQL query tool plugin, the VisAD scientific visualization package, the Weka data mining platform, many popular spreadsheets, the MATLAB computational mathematics environment, and the iReport visual report designer;
  • An extremely flexible hierarchically nested definition of space including the ability to do point-and-click and modeling and visualization of 2D environments; 3D environments; networks including full integration with the JUNG network modeling library as well as Microsoft Excel spreadsheets and UCINET DL file importing; and geographical spaces including 2D and 3D Geographical Information Systems (GIS) support;
  • A range of data storage "freeze dryers" for model check pointing and restoration including XML file storage, text file storage, and database storage;
  • A fully concurrent multithreaded discrete event scheduler;
  • Libraries for genetic algorithms, neural networks, regression, random number generation, and specialized mathematics;
  • An automated Monte Carlo simulation framework which supports multiple modes of model results optimization;
  • Built-in tools for integrating external models;
  • Distributed computing with Terracotta;
  • Full object-orientation;
  • Optional end-to-end XML simulation
  • A point-and-click model deployment system
Remark of Debian Med team: Please read also
 http://www.tbiomed.com/content/5/1/11
 http://lists.debian.org/debian-med/2009/08/msg00013.html (and following mails)
*Popularitycontest results: number of people who use this package regularly (number of people who upgraded this package recently) out of 196492