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

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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

python3-seirsplus
Models of SEIRS epidemic dynamics with extensions
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This package implements generalized SEIRS infectious disease dynamics models with extensions that model the effect of factors including population structure, social distancing, testing, contact tracing, and quarantining detected cases.

Notably, this package includes stochastic implementations of these models on dynamic networks.

python3-torch
Tensorer og dynamiske neurale netværk i Python - Pythongrænseflade
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PyTorch er en Pythonpakke, der tilbyder to funktioner på højt niveau.

(1) Tensorberegning (som NumPy) med stærk GPU-acceleration (2) Dybe neurale netværk bygget på et båndbaseret autograd-system

Du kan genbruge dine favoritpakker fra Python såsom NumPy, SciPy og Cython for at udvide PyTorch efter behov.

Dette er versionen kun for cpu af PyTorch (Pythongrænseflade).

Please cite: Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai and Soumith Chintala:
Registry entries: SciCrunch 
python3-treetime
Inferens af tidsstemplede fylogenier og forfædres rekonstruktion - Python 3
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TreeTime giver rutiner til genopbygning af forfædresekvenser og maksimal likelihoo-inferens af molekylært ur-fylogenier, dvs. et træ hvor alle grene er skaleret, så placeringen af ​​terminalknudepunkter svarer til deres samplingstider, og interne knuder er placeret ved mest sandsynlige tidspunkt for afvigelse.

TreeTime sigter mod at nå et kompromis mellem sofistikerede sandsynlighedsmodeller for evolution og hurtig heuristik. Det implementerer GTR-modeller af forfædres inferens og optimering af grenlængden, men tager trætopologien som givet. For at optimere sandsynligheden for tidsskaleret fylogenier, treetime bruger en iterativ tilgang, som først inferer forfædresekvenser givet træets grenlængde og optimerer derefter positionerne for ubegrænsede d-noder på tidsaksen og gentages derefter denne cyklus. Den eneste topologioptimering er (valgfri) opløsning på polytomier på en måde, der er mest (cirka) konsistent med prøvetagning af tidsbegrænsninger på træet. Pakken er designet til brug som et selvstændigt værktøj eller som et bibliotek brugt i større fylogenetisk analyse af arbejdsgange.

Funktioner

  • rekonstruktion af forfædresekvens (marginalt og fælles maksimum sandsynlighed)
  • inferens af molekylært ur-træ (marginalt og fælles maksimum sandsynlighed)
  • inferens af GTR-modeller
  • genstart for at opnå den bedste root-to-tip-regression
  • autokorreleret afslappet molekylært ur (med normalt forudgående)

Denne pakke indeholder Python 3-modulet.

Registry entries: Bioconda 
r-cran-covid19us
Tilfælde af COVI-19 i USA forberedt for GNU R
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Denne pakke tilbyder et GNU R-omslag omkring »COVID Tracking Project API« https://covidtracking.com/api/ der tilbyder data på tilfælde af COVID-19 i USA.

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 kohorte-opfølgningsdata, inklusive intervalcensorerede data og repræsentation af data i flere tilstande. Indeholder også nyttige funktioner til tabulering og plotning. Indeholder også nogle epidemiologiske datasæt.

Epi-pakken er primært fokuseret på »klassisk« kronisk sygdomsepidemiologi. 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-epiestim
GNU R estimate time varying reproduction numbers from rpidemic curves
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Tools to quantify transmissibility throughout an epidemic from the analysis of time series of incidence as described in Cori et al. (2013) and Wallinga and Teunis (2004) .

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-incidence
GNU R - beregn, håndter, plot og eksempler på forekomst af daterede hændelser
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Denne pakke tilbyder funktioner og klasser til at beregne, håndtere og visualisere forekomst fra daterede hændelser for et defineret tidsinterval. Datoer kan tilbydes i diverse standardformater. Klassen »incidence« bruges til at lagre beregnet forekomst og kan nemt manipuleres, underopdeles og plottes. Derudover kan log-linear-modeller tilpasses til »incidence«-objekter via »fit«. Denne pakke er en del af RECON-værktøjssættet (http://www.repidemicsconsortium.org/) for udbrudsanalyse.

r-cran-kernelheaping
GNU R kernel density estimation for heaped and rounded data
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In self-reported or anonymised data the user often encounters heaped data, i.e. data which are rounded (to a possibly different degree of coarseness). While this is mostly a minor problem in parametric density estimation the bias can be very large for non-parametric methods such as kernel density estimation. This package implements a partly Bayesian algorithm treating the true unknown values as additional parameters and estimates the rounding parameters to give a corrected kernel density estimate. It supports various standard bandwidth selection methods. Varying rounding probabilities (depending on the true value) and asymmetric rounding is estimable as well: Gross, M. and Rendtel, U. (2016) (). Additionally, bivariate non- parametric density estimation for rounded data, Gross, M. et al. (2016) (), as well as data aggregated on areas is supported.

r-cran-lexrankr
Udtræk summering af tekst med LexRank-algoritmen
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En R-implementering af LexRank-algoritmen, der implementerer en stokastisk grafbaseret metode til beregning af relativ vigtighed for tekstenheder for naturlig sprogbehandling. Teknikken på problemet Test Summarization (TS) er testet. Extractive TS afhænger af koncepter for sætningsfremtræden i et dokument eller sæt af dokumenter. Fremtræden er typisk defineret i form af tilstedeværelsen af bestemte vigtige ord eller termer af lighed for en pseudosætning med tyngde.

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-prevalence
GNU R-værktøjer for prævalensvurderingsundersøgelser
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Pakken prevalence tilbyder Frequentist- og Bayesian-metoder for prævalensvurderingsundersøgelser. VIGTIGT: TruePrev-funktioner i pakken prevalence kalder på JAGS (Just Another Gibbs Sampler), der derfor skal være tilgængelig på brugerens system. JAGS kan hentes fra http://mcmc-jags.sourceforge.net/.

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-datavisualisering for statistik indenfor social videnskab
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Samling af plot- og tabelfunktioner for datavisualisering. Resultater for diverse statistiske analyser (der ofte bruges i sociale videnskaber) kan visualiseres via denne pakke, inklusive simple og krydstabulerede frekvenser, histogrammer, boksplot, (generaliserede) lineære modeller, blandede effektmodeller, principel komponentanalyse og korrelationsmatricer, klyngeanalyser, punktplot, stakskalaer, effektplot for regressionsmodeller (inklusive interaktionstermer) og meget mere. Denne pakke understøtter data med etiketter.

r-cran-surveillance
GNU R-pakke for modellering og overvågning af epidemiologiske udbrud
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Implementering af statiske metoder for modellering og overvågning af tidsserier for antal, proportioner og kategoridata, samt modellering af fortsættende epidemiologiske udbrud.

The monitoring methods focus on aberration detection in count data time series from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics, or social sciences. The package implements many typical outbreak detection procedures such as the (improved) Farrington algorithm, or the negative binomial GLR-CUSUM method of Höhle and Paul (2008) . A novel CUSUM approach combining logistic and multinomial logistic modeling is also included. The package contains several real-world data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatio-temporal fashion. A recent overview of the available monitoring procedures is given by Salmon et al. (2016) .

For the retrospective analysis of epidemic spread, the package provides three endemic-epidemic modeling frameworks with tools for visualization, likelihood inference, and simulation. hhh4() estimates models for (multivariate) count time series following Paul and Held (2011) and Meyer and Held (2014) . twinSIR() models the susceptible-infectious-recovered (SIR) event history of a fixed population, e.g, epidemics across farms or networks, as a multivariate point process as proposed by Höhle (2009) . twinstim() estimates self-exciting point process models for a spatio-temporal point pattern of infective events, e.g., time-stamped geo-referenced surveillance data, as proposed by Meyer et al. (2012) . A recent overview of the implemented space-time modeling frameworks for epidemic phenomena is given by Meyer et al. (2017) .

Please cite: Maëlle Salmon, Dirk Schumacher and Michael Höhle: Monitoring Count Time Series in R: Aberration Detection in Public Health Surveillance. Journal of Statistical Software 70(10):1-35 (2016)

Official Debian packages with lower relevance

python3-epimodels
Simpel grænseflade til at simulere matematiske epidemiske modeller i Python 3
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Dette bibliotek tilbyder en simpel grænseflade til at simulere matematiske epidemiske modeller i Python 3. Det er en betingelse for programmet epigrass.

r-cran-cmprsk
GNU R-underdistributionsanalyse af konkurrerende risici
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Denne GNU R-pakker understøtter estimering, test og regressionsmodeller for underdistributionsfunktioner i konkurrerende risici, som beskrevet i Gray (1988), en klasse af K-prøvetest for sammenligning af de kumulative forekomster af en konkurrerende risiko.

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)
shiny-server
Placer Shiny-internetprogrammer på nettet
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Shiny Server lader dig placere Shiny-internetprogrammer og interaktive dokumenter på nettet. Tag dine Shiny-programmer og del dem med din organisation eller verden.

Shiny Server lader dig gå bag statiske diagrammer, og lader dig manipulere dataene. Brugere kan sortere, filtrere eller ændre antagelser i realtid. Shiny-server giver dine brugere mulighed for at tilpasse din analyse for deres specifikke behov og trækker mere indblik ud af dine data.

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

chime
COVID-19 Hospital Impact Model for Epidemics
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Penn Medicine - COVID-19 Hospital Impact Model for Epidemics

This tool was developed by the Predictive Healthcare team at Penn Medicine. For questions and comments please see our contact page. Code can be found on Github. Join our Slack channel if you would like to get involved!

The estimated number of currently infected individuals is 533. The 91 confirmed cases in the region imply a 17% rate of detection. This is based on current inputs for Hospitalizations (4), Hospitalization rate (5%), Region size (4119405), and Hospital market share (15%).

An initial doubling time of 6 days and a recovery time of 14.0 days imply an R_0 of 2.71.

Mitigation: A 0% reduction in social contact after the onset of the outbreak reduces the doubling time to 6.0 days, implying an effective R_t of 2.712.712.71.

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

r-cran-covid19
GNU R Coronavirus COVID-19 data acquisition and visualization
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This GNU R package provides pre-processed, ready-to-use, tidy format datasets of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic. The latest data are downloaded in real-time, processed and merged with demographic indicators from several trusted sources. The package implements advanced data visualization across the space and time dimensions by means of animated mapping. Besides worldwide data, the package includes granular data for Italy, Switzerland and the Diamond Princess.

ushahidi
web platform for information collection
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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 245498