Summary
Epidemiology
Debian Med epidemiology related packages
This metapackage will install tools that are useful in epidemiological
research. Several packages making use of the GNU R data language for
statistical investigation. It might be a good idea to read the paper
"A short introduction to R for Epidemiology" at
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
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Debian Med Epidemiology packages
Official Debian packages with high relevance
python3-seirsplus
Models of SEIRS epidemic dynamics with extensions
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Versions of package python3-seirsplus |
Release | Version | Architectures |
bullseye | 0.1.4+git20200528.5c04080+ds-2 | all |
bookworm | 1.0.9-1 | all |
trixie | 1.0.9-2 | all |
sid | 1.0.9-2 | all |
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License: DFSG free
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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.
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python3-torch
Tensors and Dynamic neural networks in Python (Python Interface)
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Versions of package python3-torch |
Release | Version | Architectures |
sid | 2.4.1-1 | amd64,arm64,ppc64el,riscv64,s390x |
bullseye | 1.7.1-7 | amd64,arm64,armhf,ppc64el,s390x |
bookworm | 1.13.1+dfsg-4 | amd64,arm64,ppc64el,s390x |
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License: DFSG free
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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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Versions of package python3-treetime |
Release | Version | Architectures |
bullseye | 0.8.1-1 | all |
buster | 0.5.3-1 | all |
sid | 0.11.1-1 | all |
trixie | 0.11.1-1 | all |
bookworm | 0.9.4-1 | all |
upstream | 0.11.4 |
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License: DFSG free
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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
cases of COVID-19 in the United States prepared for GNU R
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Versions of package r-cran-covid19us |
Release | Version | Architectures |
trixie | 0.1.9-1 | all |
bullseye | 0.1.7-1 | all |
sid | 0.1.9-1 | all |
bookworm | 0.1.9-1 | all |
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License: DFSG free
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This package provides a GNU R wrapper around the 'COVID Tracking Project API'
https://covidtracking.com/api/ providing data on cases of COVID-19
in the US.
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r-cran-diagnosismed
medical diagnostic test accuracy analysis toolkit
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Versions of package r-cran-diagnosismed |
Release | Version | Architectures |
stretch | 0.2.3-4 | all |
jessie | 0.2.3-3 | all |
buster | 0.2.3-6 | all |
bullseye | 0.2.3-7 | all |
bookworm | 0.2.3-7 | all |
trixie | 0.2.3-7 | all |
sid | 0.2.3-7 | all |
Debtags of package r-cran-diagnosismed: |
devel | lang:r |
field | medicine |
interface | commandline |
role | program |
use | analysing |
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License: DFSG free
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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.
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r-cran-epi
GNU R epidemiological analysis
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Versions of package r-cran-epi |
Release | Version | Architectures |
stretch | 2.7-1 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
buster | 2.32-2 | amd64,arm64,armhf,i386 |
bullseye | 2.43-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
bookworm | 2.47-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
trixie | 2.53-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
sid | 2.53-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
jessie | 1.1.67-4 | amd64,armel,armhf,i386 |
upstream | 2.56 |
Debtags of package r-cran-epi: |
field | medicine |
interface | commandline |
role | program |
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License: DFSG free
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Functions for demographic and epidemiological analysis in the Lexis diagram,
i.e. register and cohort follow-up data, including interval censored data and
representation of multistate data. Also some useful functions for tabulation
and plotting. Contains some epidemiological datasets.
The Epi package is mainly focused on "classical" chronic disease epidemiology.
The package has grown out of the course Statistical Practice in Epidemiology
using R (see http://www.pubhealth.ku.dk/~bxc/SPE).
There is A short introduction to R for Epidemiology available at
http://staff.pubhealth.ku.dk/%7Ebxc/Epi/R-intro.pdf
Beware that the pages 38-120 of this is merely the manual pages for the Epi
package.
Epi is not the only R-package for epidemiological analysis, a package with
more affinity to infectious disease epidemiology is the epitools package
which is also evailable in Debian.
Epi is used in the Department of Biostatistics of the University of Copenhagen.
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r-cran-epibasix
GNU R Elementary Epidemiological Functions
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Versions of package r-cran-epibasix |
Release | Version | Architectures |
bookworm | 1.5-2 | all |
bullseye | 1.5-2 | all |
buster | 1.5-1 | all |
jessie | 1.3-1 | amd64,armel,armhf,i386 |
sid | 1.5-2 | all |
trixie | 1.5-2 | all |
stretch | 1.3-2 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
Debtags of package r-cran-epibasix: |
field | medicine |
interface | commandline |
role | program |
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License: DFSG free
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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.
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r-cran-epicalc
GNU R Epidemiological calculator
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Versions of package r-cran-epicalc |
Release | Version | Architectures |
trixie | 2.15.1.0-5 | all |
jessie | 2.15.1.0-1 | all |
stretch | 2.15.1.0-2 | all |
buster | 2.15.1.0-4 | all |
bullseye | 2.15.1.0-5 | all |
bookworm | 2.15.1.0-5 | all |
sid | 2.15.1.0-5 | all |
Debtags of package r-cran-epicalc: |
devel | lang:r |
field | medicine, statistics |
interface | commandline |
role | program |
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License: DFSG free
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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.
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r-cran-epiestim
GNU R estimate time varying reproduction numbers from rpidemic curves
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Versions of package r-cran-epiestim |
Release | Version | Architectures |
trixie | 2.2-4+dfsg-1 | all |
buster-backports | 2.2-4+dfsg-1~bpo10+1 | all |
bullseye | 2.2-4+dfsg-1 | all |
bookworm | 2.2-4+dfsg-1 | all |
sid | 2.2-4+dfsg-1 | all |
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License: DFSG free
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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)
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r-cran-epir
GNU R Functions for analysing epidemiological data
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Versions of package r-cran-epir |
Release | Version | Architectures |
jessie | 0.9-59-1 | all |
stretch | 0.9-79-1 | all |
buster | 0.9-99-1 | all |
bookworm | 2.0.57+dfsg-1 | all |
sid | 2.0.76+dfsg-1 | all |
bullseye | 2.0.19-1 | all |
Debtags of package r-cran-epir: |
devel | lang:r |
field | medicine |
interface | commandline |
role | program |
use | analysing |
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License: DFSG free
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A package for analysing epidemiological data. Contains functions for
directly and indirectly adjusting measures of disease frequency,
quantifying measures of association on the basis of single or multiple
strata of count data presented in a contingency table, and computing
confidence intervals around incidence risk and incidence rate estimates.
Miscellaneous functions for use in meta-analysis, diagnostic test
interpretation, and sample size calculations.
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r-cran-epitools
GNU R Epidemiology Tools for Data and Graphics
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Versions of package r-cran-epitools |
Release | Version | Architectures |
bookworm | 0.5-10.1-2 | all |
trixie | 0.5-10.1-2 | all |
bullseye | 0.5-10.1-2 | all |
buster | 0.5-10-2 | all |
stretch | 0.5-7-1 | all |
jessie | 0.5-7-1 | all |
sid | 0.5-10.1-2 | all |
Debtags of package r-cran-epitools: |
field | medicine |
interface | commandline |
role | program |
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License: DFSG free
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GNU R Tools for public health epidemiologists and data analysts.
Epitools provides numerical tools and programming solutions that
have been used and tested in real-world epidemiologic applications.
Many practical problems in the analysis of public health data
require programming or special software, and investigators in
different locations may duplicate programming efforts. Often,
simple analyses, such as the construction of confidence intervals,
are not calculated and thereby complicate appropriate statistical
inferences for small geographic areas. There are many examples of
simple and useful numerical tools that would enhance the work of
epidemiologists at local health departments and yet are not readily
available for the problem in front of them. The availability of
these tools will encourage wider use of appropriate methods and
promote evidence-based public health practices.
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r-cran-incidence
GNU R compute, handle, plot and model incidence of dated events
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Versions of package r-cran-incidence |
Release | Version | Architectures |
trixie | 1.7.5-1 | all |
buster-backports | 1.7.3-1~bpo10+1 | all |
bullseye | 1.7.3-1 | all |
bookworm | 1.7.3-1 | all |
sid | 1.7.5-1 | all |
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License: DFSG free
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Provides functions and classes to compute, handle and visualise
incidence from dated events for a defined time interval. Dates can be
provided in various standard formats. The class 'incidence' is used to
store computed incidence and can be easily manipulated, subsetted, and
plotted. In addition, log-linear models can be fitted to 'incidence'
objects using 'fit'. This package is part of the RECON
(http://www.repidemicsconsortium.org/) toolkit for outbreak analysis.
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r-cran-kernelheaping
GNU R kernel density estimation for heaped and rounded data
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Versions of package r-cran-kernelheaping |
Release | Version | Architectures |
sid | 2.3.0-1 | all |
bookworm | 2.3.0-1 | all |
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License: DFSG free
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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.
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r-cran-lexrankr
extractive summarization of text with the LexRank algorithm
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Versions of package r-cran-lexrankr |
Release | Version | Architectures |
buster | 0.5.0-2 | amd64,arm64,armhf,i386 |
sid | 0.5.2-8 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
trixie | 0.5.2-8 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bookworm | 0.5.2-8 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
bullseye | 0.5.2-2 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
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License: DFSG free
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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.
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r-cran-prevalence
GNU R tools for prevalence assessment studies
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Versions of package r-cran-prevalence |
Release | Version | Architectures |
trixie | 0.4.1-1 | all |
bookworm | 0.4.1-1 | all |
sid | 0.4.1-1 | all |
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License: DFSG free
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The prevalence package provides Frequentist and Bayesian methods for
prevalence assessment studies. IMPORTANT: the truePrev functions in the
prevalence package call on JAGS (Just Another Gibbs Sampler), which
therefore has to be available on the user's system. JAGS can be
downloaded from http://mcmc-jags.sourceforge.net/.
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r-cran-seroincidence
GNU R seroincidence calculator tool
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Versions of package r-cran-seroincidence |
Release | Version | Architectures |
sid | 2.0.0-3 | all |
trixie | 2.0.0-3 | all |
bookworm | 2.0.0-3 | all |
bullseye | 2.0.0-2 | all |
buster | 2.0.0-1 | all |
stretch | 1.0.5-1 | all |
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License: DFSG free
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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.
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r-cran-sf
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Versions of package r-cran-sf |
Release | Version | Architectures |
sid | 1.0-17+dfsg-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bullseye | 0.9-7+dfsg-5 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
buster | 0.7-2+dfsg-1 | amd64,arm64,armhf,i386 |
stretch-backports | 0.7-2+dfsg-1~bpo9+1 | amd64 |
stretch-backports | 0.6-3+dfsg-1~bpo9+1 | arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
bookworm | 1.0-9+dfsg-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
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License: DFSG free
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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.
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r-cran-sjplot
GNU R data visualization for statistics in social science
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Versions of package r-cran-sjplot |
Release | Version | Architectures |
bullseye | 2.8.7-1 | all |
stretch-backports | 2.6.2-1~bpo9+1 | all |
buster | 2.6.2-1 | all |
bookworm | 2.8.12+dfsg-1 | all |
sid | 2.8.16+dfsg-1 | all |
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License: DFSG free
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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.
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r-cran-surveillance
GNU R package for the Modeling and Monitoring of Epidemic Phenomena
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Versions of package r-cran-surveillance |
Release | Version | Architectures |
jessie | 1.8-0-1 | amd64,armel,armhf,i386 |
sid | 1.24.0-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
trixie | 1.23.0-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bookworm | 1.20.3-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
bullseye | 1.19.0-2 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
buster | 1.16.2-1 | amd64,arm64,armhf,i386 |
stretch | 1.13.0-1 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
Debtags of package r-cran-surveillance: |
field | medicine |
interface | commandline |
role | program |
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License: DFSG free
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Statistical methods for the modeling and monitoring of time series of
counts, proportions and categorical data, as well as for the modeling of
continuous-time point processes of epidemic phenomena.
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) .
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Official Debian packages with lower relevance
python3-epimodels
simple interface to simulate mathematical epidemic models in Python3
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Versions of package python3-epimodels |
Release | Version | Architectures |
bookworm | 0.4.0-1 | all |
sid | 0.4.0-2 | all |
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License: DFSG free
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This library provides a simple interface to simulate mathematical
epidemic models in Python3. It is a precondition for the program
epigrass.
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r-cran-cmprsk
GNU R subdistribution analysis of competing risks
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Versions of package r-cran-cmprsk |
Release | Version | Architectures |
bookworm | 2.2-11-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
trixie | 2.2-11-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
sid | 2.2-11-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bullseye | 2.2-10-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
stretch | 2.2-7-2 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
buster | 2.2-7-4 | amd64,arm64,armhf,i386 |
upstream | 2.2-12 |
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License: DFSG free
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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.
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r-cran-msm
GNU R Multi-state Markov and hidden Markov models in continuous time
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Versions of package r-cran-msm |
Release | Version | Architectures |
jessie | 1.4-2 | amd64,armel,armhf,i386 |
sid | 1.8-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
trixie | 1.8-1 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
bookworm | 1.7-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
bullseye | 1.6.8-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
buster | 1.6.6-2 | amd64,arm64,armhf,i386 |
stretch | 1.6.4-1 | amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x |
upstream | 1.8.1 |
Debtags of package r-cran-msm: |
interface | commandline |
role | program |
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License: DFSG free
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Functions for fitting general continuous-time Markov and hidden Markov
multi-state models to longitudinal data. Both Markov transition rates and the
hidden Markov output process can be modelled in terms of covariates. A variety
of observation schemes are supported, including processes observed at arbitrary
times, completely-observed processes, and censored states.
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shiny-server
put Shiny web apps online
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Versions of package shiny-server |
Release | Version | Architectures |
bookworm | 1.5.20.1002-1 | amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x |
sid | 1.5.20.1002-2 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
trixie | 1.5.20.1002-2 | amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x |
upstream | 1.5.23.1030 |
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License: DFSG free
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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.
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Packaging has started and developers might try the packaging code in VCS
chime
COVID-19 Hospital Impact Model for Epidemics
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Versions of package chime |
Release | Version | Architectures |
VCS | 0.2.1-1 | all |
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License: MIT
Debian package not available
Version: 0.2.1-1
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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.
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epifire
model the spread of an infectious disease in a population
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Versions of package epifire |
Release | Version | Architectures |
VCS | 3.34.0+dfsg-1 | all |
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License: BSD-3-clause
Debian package not available
Version: 3.34.0+dfsg-1
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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.
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netepi-analysis
network-enabled tools for epidemiology and public health practice
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Versions of package netepi-analysis |
Release | Version | Architectures |
VCS | 0.9.0-2 | all |
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License: HACOS
Debian package not available
Version: 0.9.0-2
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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.
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netepi-collection
network-enabled tools for epidemiology and public health practice
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Versions of package netepi-collection |
Release | Version | Architectures |
VCS | 1.8.4-2 | all |
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License: HACOS
Debian package not available
Version: 1.8.4-2
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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.
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r-cran-covid19
GNU R Coronavirus COVID-19 data acquisition and visualization
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Versions of package r-cran-covid19 |
Release | Version | Architectures |
VCS | 0.2.1-1 | all |
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License: GPL-3
Debian package not available
Version: 0.2.1-1
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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.
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ushahidi
web platform for information collection
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Versions of package ushahidi |
Release | Version | Architectures |
VCS | 2.7.4-1 | all |
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License: LGPL-3+
Debian package not available
Version: 2.7.4-1
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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.
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No known packages available but some record of interest (WNPP bug)
framework for creating agent based simulations
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License: BSD
Debian package not available
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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
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