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Summary
Epidemiology
pacchetti relativi all'epidemiologia per Debian Med

Questo metapacchetto installa gli strumenti utili nella ricerca epidemiologica. Diversi pacchetti utilizzano il linguaggio dati GNU R per l'investigazione statistica. Potrebbe essere una buona idea leggere l'articolo "A short introduction to R for Epidemiology" all'indirizzo 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
Tensors and Dynamic neural networks in Python (Python Interface)
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PyTorch is a Python package that provides two high-level features:

(1) Tensor computation (like NumPy) with strong GPU acceleration (2) Deep neural networks built on a tape-based autograd system

You can reuse your favorite Python packages such as NumPy, SciPy and Cython to extend PyTorch when needed.

This is the CPU-only version of PyTorch (Python interface).

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:
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python3-treetime
inference of time stamped phylogenies and ancestral reconstruction (Python 3)
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TreeTime provides routines for ancestral sequence reconstruction and the maximum likelihoo inference of molecular-clock phylogenies, i.e., a tree where all branches are scaled such that the locations of terminal nodes correspond to their sampling times and internal nodes are placed at the most likely time of divergence.

TreeTime aims at striking a compromise between sophisticated probabilistic models of evolution and fast heuristics. It implements GTR models of ancestral inference and branch length optimization, but takes the tree topology as given. To optimize the likelihood of time-scaled phylogenies, treetime uses an iterative approach that first infers ancestral sequences given the branch length of the tree, then optimizes the positions of unconstraine d nodes on the time axis, and then repeats this cycle. The only topology optimization are (optional) resolution of polytomies in a way that is most (approximately) consistent with the sampling time constraints on the tree. The package is designed to be used as a stand-alone tool or as a library used in larger phylogenetic analysis workflows.

Features

  • ancestral sequence reconstruction (marginal and joint maximum likelihood)
  • molecular clock tree inference (marginal and joint maximum likelihood)
  • inference of GTR models
  • rerooting to obtain best root-to-tip regression
  • auto-correlated relaxed molecular clock (with normal prior)

This package provides the Python 3 module.

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r-cran-covid19us
casi di COVID-19 negli Stati Uniti preparati per GNU R
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Questo pacchetto fornisce un wrapper GNU R per la "COVID Tracking Project API" (https://covidtracking.com/api/) che fornisce dati sui casi di COVID-19 negli Stati Uniti.

r-cran-diagnosismed
toolkit per analisi di accuratezza per test diagnostici medicali
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DiagnosisMed è un pacchetto GNU R per analizzare l'accuratezza dei dati da test diagnostici per valutare le condizioni di salute. È stato creato per essere usato da professionisti in campo medico. Questo pacchetto aiuta a stimare la sensibilità e la specificità da risultati di test categorici e continui incluse alcune valutazioni di risultati indeterminati, oppure confrontare diversi test categorici e stimare valori limite ragionevoli per i test e visualizzarli in un modo usato comunemente da professionisti in campo medico. Non è ancora disponibile un'interfaccia grafica.

r-cran-epi
analisi epidemiologica GNU R
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Funzioni per analisi demografiche ed epidemiologiche nel diagramma di Lexis, cioè registrazioni e dati di aggiornamento di un gruppo, inclusi dati censiti ad intervalli e la rappresentazione di dati multi-stato. Inoltre vi sono alcune funzioni utili per la tabulazione e il tracciamento di grafici. Contiene alcuni insiemi di dati epidemiologici.

Il pacchetto Epi è orientato principalmente all'epidemiologia "classica" per le malattie croniche. Il pacchetto si è sviluppato dal corso di Statistica applicata all'epidemiologia usando R (vedere http://www.pubhealth.ku.dk/~bxc/SPE).

Una breve introduzione a R per l'epidemiologia è disponibile su http://staff.pubhealth.ku.dk/%7Ebxc/Epi/R-intro.pdf Notare che le pagine 38-120 di questo documento sono semplicemente le pagine di manuale del pacchetto Epi.

Epi non è l'unico pacchetto R per l'analisi epidemiologica, il pacchetto epitools, anch'esso disponibile in Debian, è un pacchetto più indirizzato all'epidemiologia delle malattie infettive.

Epi è usato dal Dipartimento di Biostatistica dell'Università di Copenaghen.

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
funzioni epidemiologiche elementari per GNU R
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Funzioni epidemiologiche elementari per un corso universitario di epidemiologia/biostatistica.

Questo pacchetto contiene strumenti elementari per l'analisi di problemi epidemiologici comuni, che vanno dalla stima della grandezza di un campione all'analisi di tabelle di contingenza 2x2 e misure di base di concordanza (kappa, sensibilità/specificità). Vengono inoltre prodotti, quando possibile, rapporti riassuntivi e stampe appropriati per facilitare l'interpretazione. Questo pacchetto è in fase di sviluppo, perciò qualsiasi commento o suggerimento è benvenuto. Il codice sorgente è largamente commentato per facilitare modifiche. L'utenza a cui è indirizzato include studenti universitari di vari corsi di statistica epidemiologica/biologica.

Epibasix è stato sviluppato in Canada.

r-cran-epicalc
calcolatore epidemiologico per GNU R
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Funzioni per rendere facile il calcolo epidemiologico in R.

Permette di elaborare insiemi di dati da Dbase (.dbf), Stata (.dta), SPSS (.sav), EpiInfo (.rec) e valori separati da virgole (.csv) o anche gruppi di dati in R per eseguire diversi calcoli epidemiologici.

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
funzioni GNU R per analisi di dati epidemiologici
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Un pacchetto per analizzare dati epidemiologici. Contiene funzioni per regolare direttamente e indirettamente misure della frequenza di malattie, quantificare misure di associazioni sulla base di strati singoli o multipli di dati numerici presentati in una tabella di contingenza e calcolare intervalli di confidenza sul rischio di incidenza e le stime delle percentuali di incidenza. Sono fornite funzioni varie per l'uso in meta-analisi, interpretazione di test diagnostici e calcoli della dimensione del campione.

r-cran-epitools
strumenti GNU R per epidemiologia per dati e grafici
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Strumenti GNU R per epidemiologi e analisti di dati. Epitools fornisce strumenti numerici e soluzioni software che sono state usate e testate in applicazioni epidemiologiche nel mondo reale.

Molti problemi pratici nell'analisi dei dati sulla salute pubblica richiedono programmazione o software speciale e gli studiosi, in diversi luoghi, possono fare sforzi di programmazione duplicati. Spesso analisi semplici, come la costruzione di intervalli di confidenza, non sono calcolate e perciò complicano un'inferenza statistica corretta per aree geografiche limitate. Ci sono molti esempi di strumenti numerici semplici e utili che migliorerebbero il lavoro degli epidemiologi nei dipartimenti di salute pubblica e che, però, non sono disponibili immediatamente per i problemi reali che si trovano di fronte. La disponibilità di questi strumenti incoraggerà un più vasto uso dei metodi appropriati e promuoverà politiche di salute pubblica basate su prove.

r-cran-incidence
calcolo, gestione, disegno e modellazione in GNU R dell'incidenza di eventi con data
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Fornisce funzioni e classi per calcolare, gestire e visualizzare l'incidenza da eventi con data per un intervallo di tempo definito. Le date possono essere fornite in vari formati standard. La classe "incidence" è usata per memorizzare l'incidenza calcolata e può essere facilmente manipolata, divisa in sottoinsiemi e disegnata. In aggiunta può essere fatto il fit di modelli log-lineari su oggetti "incidence" usando "fit". Questo pacchetto fa parte dell'insieme di strumenti RECON (http://www.repidemicsconsortium.org/) per l'analisi di epidemie.

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
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-prevalence
strumenti GNU R per studi di calcolo della prevalenza
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Il pacchetto prevalence fornisce metodi frequentisti e bayesiani per studi di valutazione della prevalenza. IMPORTANTE: le funzioni truePrev nel pacchetto prevalence chiamano JAGS (Just Another Gibbs Sampler), che perciò deve essere disponibile nel sistema dell'utente. JAGS può essere scaricato da http://mcmc-jags.sourceforge.net/.

r-cran-seroincidence
strumento GNU R per calcolare la sieroincidenza
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I livelli di anticorpi misurati in campioni di popolazione trasversali possono essere tradotti in stime della frequenza con cui avvengono sieroconversioni (nuove infezioni). Per poter interpretare i livelli anticorpali trasversali misurati, devono essere conosciuti i parametri di predizione del decadimento degli anticorpi. In rapporti precedentemente pubblicati (Simonsen et al. 2009 e Versteegh et al. 2005), queste informazioni sono state ottenute da studi longitudinali su soggetti che avevano infezioni da Salmonella o Campylobacter confermate colturalmente. È stato utilizzato un modello bayesiano di calcolo all'indietro per convertire le misure anticorpali in una stima del tempo trascorso dall'infezione. Ciò può essere utilizzato per stimare la sieroincidenza nel campione trasversale della popolazione. Per entrambe le misure longitudinale e trasversale delle concentrazioni anticorpali, è stato usato un metodo ELISA indiretto. I modelli sono validi solo per soggetti con più di 18 anni. Le stime della sieroincidenza sono adatte per monitorare l'effetto di programmi di controllo quando sono disponibili per l'analisi campioni di siero trasversali rappresentativi. Questi forniscono informazioni più accurate sulla pressione dell'infezione nella popolazione umana in più nazioni.

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
pacchetto GNU R per la modellazione e il monitoraggio di fenomeni epidemici
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Metodi statistici per la modellazione e il monitoraggio di serie temporali di conteggi, proporzioni e dati categorici, così come per la modellazione di processi puntuali di fenomeni epidemici nel tempo continuo.

I metodi di monitoraggio si focalizzano sul rilevamento di aberrazioni in serie temporali di conteggi da dati di sorveglianza per la salute pubblica di malattie trasmissibili, ma potrebbero anche esserci applicazioni per l'environmetrica, l'ingegneria dell'affidabilità, l'econometria o le scienze sociali. Il pacchetto implementa molte comuni procedure di rilevamento di epidemie, come l'algoritmo di Farrington (migliorato) o il metodo GLR-CUSUM negativo binomiale di Höhle & Paul (2008) . È anche incluso un approccio CUSUM innovativo che combina modellazione logistica e logistica multinomiale. Il pacchetto contiene diversi insiemi di dati di casi reali, la capacità di simulare dati di epidemie e di visualizzare i risultati del monitoraggio in maniera temporale, spaziale o spazio-temporale. Una recente panoramica delle procedure di monitoraggio disponibili è fornita in Salmon et al. (2016) .

Per l'analisi retrospettiva di diffusioni epidemiche, il pacchetto fornisce tre infrastrutture di modellazione endemica-epidemica con strumenti per visualizzazione, inferenza di verosimiglianza e simulazione. hhh4() stima modelli per serie temporali di conteggi (multivariate) secondo Paul & Held (2011) e Meyer & Held (2014) . twinSIR() modella la cronologia di eventi SIR (suscettibili-infetti-rimossi) di una popolazione fissa, ad esempio epidemie tra fattorie o reti, come un processo puntuale multivariato come proposto da Höhle (2009) . twinstim() stima modelli di processi puntuali auto-eccitanti per un modello puntuale spazio-temporale di eventi infettivi, ad esempio dati di sorveglianza georeferenziati con marcatura temporale, come proposto da Meyer et al. (2012) . Una recente panoramica delle infrastrutture di modellazione spazio-tempo implementate per i fenomeni epidemici è fornita in 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
semplice interfaccia per simulare modelli epidemici matematici in Python 3
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Questa libreria fornisce una semplice interfaccia per simulare modelli epidemici matematici in Python 3. È una precondizione per il programma epigrass.

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
modelli multistato di Markov e di Markov nascosti in tempo continuo per GNU R
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Funzioni per il fitting di modelli generici multi-stato di Markov e di Markov nascosti in tempo continuo a dati longitudinali. Sia le probabilità di transizione tra gli stati markoviani sia il processo di output del modello di Markov nascosto possono essere modellati in termini di covariate. Sono gestiti svariati schemi di osservazione, inclusi processi osservati ad intervalli arbitrari, processi osservati completamente e stati censurati.

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
put Shiny web apps online
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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.

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

chime
COVID-19 Hospital Impact Model for Epidemics
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License: MIT
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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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License: GPL-3
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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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License: LGPL-3+
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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 244163