Debian Med Epidemiology packages

Epigrass est un logiciel pour visualiser, analyser et simuler les processus épidémiques sur des réseaux géo-référencés.

EpiGrass peut interagir avec le SIG (Système d'Information Géographique) GRASS à partir duquel il peut obtenir des cartes et autres informations géo-référencées. Cependant, EpiGrass ne requiert pas l'installation du SIG GRASS pour la plupart de ses fonctionnalités.

DiagnosisMed is a GNU R package to analyze the accuracy of data from diagnostic tests evaluating health conditions. It was designed to be used by health professionals. This package helps estimating sensitivity and specificity from categorical and continuous test results including some evaluations of indeterminate results, or compare different categorical tests, and estimate reasonable cut-offs of tests and display it in a way commonly used by health professionals. No graphical interface is available yet.

Fonctions pour analyses démographiques et épidémiologiques dans le diagramme Lexis, c'est-à-dire des données de suivi de cohorte ou d'enregistrement, ce qui inclut des données censurées par intervalles et des représentations de données de différents états. Ce paquet contient également des fonctions utiles pour la classification et le tracé de courbes. Il contient des séries de données épidémiologiques.

Le paquet Epi se concentre principalement sur l'épidémiologie « classique » des maladies chroniques. Le paquet a grandi hors des cours de pratique statistique en épidémiologie en utilisant R. (pour plus d'info : http://www.pubhealth.ku.dk/~bxc/SPE).

Une introduction rapide à R pour l'épidémiologie est disponible ici : http://staff.pubhealth.ku.dk/%7Ebxc/Epi/R-intro.pdf Attention, les pages 38 à 120 correspondent aux pages de manuels pour le paquet Epi.

Epi n'est pas le seul paquet R pour l'analyse épidémiologique, un paquet ayant plus d'affinité avec l'épidémiologie des maladies infectieuses est le paquet epitools, qui est aussi disponible dans Debian.

Epi est utilisé par de département de biostatistique de l'université de Copenhague.

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.

Il s’agit d’un paquet pour l’analyse de données épidémiologiques. Il contient des fonctions pour directement ou indirectement ajuster les fréquences de maladies, quantifier les mesures d’association sur la base d’une ou plusieurs couches de données de comptage présentées dans un tableau de contingence, et calculer l’intervalle de confiance entre le risque d’incidence et le taux d’incidence estimés. Il fournit aussi diverses fonctions pour la méta-analyse, l’interprétation de diagnostics et le calcul de taille d’échantillon.

Il s’agit d’outils de GNU R pour l’épidémiologie de santé publique et l’analyse de données. Epitools fournit des outils numériques et des solutions de programmation qui ont été utilisées et testées, en situation réelle, dans des applications d’épidémiologie.

Beaucoup de problèmes pratiques dans l’analyse de données de santé publique ont besoin de programmation ou des logiciels particuliers, et les chercheurs pourraient faire de la programmation en double. Souvent, des analyses simples, telles que la construction d’intervalles de confiance, ne sont pas calculées et par conséquent compliquent les inférences statistiques appropriées pour de petites aires géographiques. Il existe de nombreux exemples où des outils numériques simples et utiles qui amélioreraient le travail d’épidémiologistes dans des départements locaux ne sont pas facilement disponibles pour le problème à résoudre. La disponibilité de ces outils encouragerait une plus large utilisation des méthodes appropriées et promouvrait les pratiques de santé publique basées sur des éléments probants.

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.

Antibody levels measured in a cross-sectional population samples can be translated into an estimate of the frequency with which seroconversions (new infections) occur. In order to interpret the measured cross-sectional antibody levels, parameters which predict the decay of antibodies must be known. In previously published reports (Simonsen et al. 2009 and Versteegh et al. 2005), this information has been obtained from longitudinal studies on subjects who had culture-confirmed Salmonella and Campylobacter infections. A Bayesian back-calculation model was used to convert antibody measurements into an estimation of time since infection. This can be used to estimate the seroincidence in the cross-sectional sample of population. For both the longitudinal and cross-sectional measurements of antibody concentrations, the indirect ELISA was used. The models are only valid for persons over 18 years. The seroincidence estimates are suitable for monitoring the effect of control programmes when representative cross-sectional serum samples are available for analyses. These provide more accurate information on the infection pressure in humans across countries.

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.

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.

Implementation of statistical methods for the modeling and change-point detection in time series of counts, proportions and categorical data, as well as for the modeling of continuous-time epidemic phenomena, e.g., discrete-space setups such as the spatially enriched Susceptible-Exposed-Infectious-Recovered (SEIR) models, or continuous-space point process data such as the occurrence of infectious diseases. Main focus is on outbreak detection in count data time series originating from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics or social sciences.

Currently, the package contains implementations of many typical outbreak detection procedures such as Farrington et al (1996), Noufaily et al (2012) or the negative binomial LR-CUSUM method described in Höhle and Paul (2008). A novel CUSUM approach combining logistic and multinomial logistic modelling is also included. Furthermore, inference methods for the retrospective infectious disease models in Held et al (2005), Held et al (2006), Paul et al (2008), Paul and Held (2011), Held and Paul (2012), and Meyer and Held (2014) are provided.

Continuous self-exciting spatio-temporal point processes are modeled through additive-multiplicative conditional intensities as described in Höhle (2009) ('twinSIR', discrete space) and Meyer et al (2012) ('twinstim', continuous space).

The package contains several real-world data sets, the ability to simulate outbreak data, visualize the results of the monitoring in temporal, spatial or spatio-temporal fashion.

Note: Using the 'boda' algorithm requires the 'INLA' package, which is available from http://www.r-inla.org/download.

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.

Fonctions pour adapter des modèles de Markov temporels cachés et continus à des données longitudinales. Les taux de transitions de Markov et le processus de sortie de Markov caché peuvent être modélisés en terme de co- variables. Une variété de schémas d'observation sont permis, qui comprennent une surveillance des processus à des temps arbitraires, une observation complète des processus, et des états censurés.

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.

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.

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.

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 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.

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