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  • GeoStats v0.11.4 Diff since v0.11.3
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  • This is the v1.16.1 release of PyCBC. This release is identical to the 1.16.0 release other than a fix to the Travis build scripts so that PyPi deployment does not generate an error in the build and test. A Docker container for this release is available from the pycbc/pycbc-el7 repository on Docker Hub and can be downloaded using the command: docker pull pycbc/pycbc-el7:v1.16.1 On a machine with CVMFS installed, a pre-built virtual environment is available for Red Hat 7 compatible operating systems by running the command: source /cvmfs/oasis.opensciencegrid.org/ligo/sw/pycbc/x86_64_rhel_7/virtualenv/pycbc-v1.16.1/bin/activate A singularity container is available at /cvmfs/singularity.opensciencegrid.org/pycbc/pycbc-el7:v1.16.1 which can be started with the command: singularity shell --home ${HOME}:/srv --pwd /srv --bind /cvmfs --contain --ipc --pid /cvmfs/singularity.opensciencegrid.org/pycbc/pycbc-el7\:v1.16.1
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  • XML Canonical resources for Greek Literature
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  • R package for geographic assignment
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  • XML Canonical resources for Greek Literature
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  • AbstractPlotting v0.10.7 Diff since v0.10.6 Merged pull requests: Export Observable as well as Node (#406) (@asinghvi17) Changes for PlotUtils v1.0 compat (#417) (@asinghvi17)
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  • PyPI: https://pypi.org/project/atpbar/1.1.0/ Changes from the previous release: (diff) reimplemented the logic in funcs.py with a state machine #17 optimized the brief sleep of pickup cleaned code updated setup.py, tests made the number of iterations in examples smaller set start method of multiprocessing in an example for Python 3.8 removed code for Python 2.7
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  • A generalized SEIR model [1] with seven states, as proposed by ref. [2] is implemented in MATLAB. There exist other types of generalized SEIR model that can be explored, but here I only use a single one for the sake of simplicity. The numerical implementation is done from scratch except for the fitting, that relies on the function "lsqcurvfit". One major difference with respect to ref. [2] is the expression of the death rate and recovery rate, which are here analytical and empirical functions of the time. The idea behind this time-dependency as that the death rate (due to the disease) should converge toward zero as the time increases. If the death rate is kept constant, the number of death may be overestimated. At the same time, the recovery rate is also converging toward a constant value. Births and natural death are not modeled here. This means that the total population, including the number of deceased cases, is kept constant. Note that ref. [2] is a preprint that is not peer-reviewed and I am not qualified enough to judge the quality of the paper. Content The present submission contains: A function SEIQRDP.m that is used to simulate the time histories of the infectious, recovered and dead cases (among others) A function fit_SEIQRDP.m that estimates the eight parameters used in SEIQRDP.m in the least square sense. One example file Example1.mlx, which presents the numerical implementation. One example file Example2.mlx, which uses data collected by the Johns Hopkins University for the COVID-19 epidemy [3] for Hubei province (China). One example file Example3.mlx, which uses data collected by the Johns Hopkins University for the COVID-19 epidemy [3] for South Korea. One file "ItalianRegions.mlx" written by Matteo Secli (https://github.com/matteosecli) that I have modified for a slightly more robust fitting. One file "FrenchRegions.mlx", which gives another example for Data collected in France. The data quality is not as good as expected, so the fitting is unlikely to provide reliable parameter estimates. One example file ChineseProvinces.mlx, which illustrates how the function fit_SEIQRDP.m is used in a for loop to be fitted to the data [3] from the different Chinese provinces. One example "uncertaintiesIssues.mlx", which illustrates the danger of fitting limited data sets. One example "Example_US_cities.mlx" that illustrates the fitting when "recovered" data are not available. One function getDataCOVID, which read from [3] the data collected by Johns Hopkins University. One function getDataCOVID_ITA written by Matteo Secli (https://github.com/matteosecli), that collects the updated data of the COVID-19 pandemic in Italy from the Italian government [4] One function getDataCOVID_FRA that collects the updated data in France from [5] One function getDataCOVID_US that collects the updated data in the USA from [3] Any question, comment or suggestion is welcome. References [1] https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology [2] Peng, L., Yang, W., Zhang, D., Zhuge, C., & Hong, L. (2020). Epidemic analysis of COVID-19 in China by dynamical modeling. arXiv preprint arXiv:2002.06563. [3] https://github.com/CSSEGISandData/COVID-19 [4] https://github.com/pcm-dpc/COVID-19 [5] https://github.com/cedricguadalupe/FRANCE-COVID-19
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  • This is the final release of FABM before 1.0, which has a different API. All users are encouraged to upgrade to FABM 1. This 0.96 release is preserved for users and applications that cannot upgrade yet.
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  • AbstractPlotting v0.10.5 Diff since v0.10.4 Merged pull requests: New text layout (#401) (@SimonDanisch)
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