Metadata-Version: 2.1
Name: sid-dev
Version: 0.0.8
Summary: Simulate the spread of COVID-19 with different policies.
Home-page: https://github.com/covid-19-impact-lab/sid
Author: Janos Gabler, Tobias Raabe, Klara Roehrl
Author-email: janos.gabler@gmail.com
License: MIT
Project-URL: Changelog, https://sid-dev.readthedocs.io/en/latest/changes.html
Project-URL: Documentation, https://sid-dev.readthedocs.io/en/latest
Project-URL: Github, https://github.com/covid-19-impact-lab/sid
Project-URL: Tracker, https://github.com/covid-19-impact-lab/sid/issues
Description: sid
        ===
        
        .. start-badges
        
        .. image:: https://img.shields.io/pypi/v/sid-dev?color=blue
            :alt: PyPI
            :target: https://pypi.org/project/sid-dev
        
        .. image:: https://img.shields.io/pypi/pyversions/sid-dev
            :alt: PyPI - Python Version
            :target: https://pypi.org/project/sid-dev
        
        .. image:: https://img.shields.io/conda/vn/conda-forge/sid-dev.svg
            :target: https://anaconda.org/conda-forge/sid-dev
        
        .. image:: https://img.shields.io/conda/pn/conda-forge/sid-dev.svg
            :target: https://anaconda.org/conda-forge/sid-dev
        
        .. image:: https://img.shields.io/pypi/l/sid-dev
            :alt: PyPI - License
            :target: https://pypi.org/project/sid-dev
        
        .. image:: https://readthedocs.org/projects/sid-dev/badge/?version=latest
            :target: https://sid-dev.readthedocs.io/en/latest
        
        .. image:: https://img.shields.io/github/workflow/status/covid-19-impact-lab/sid/Continuous%20Integration%20Workflow/main
           :target: https://github.com/covid-19-impact-lab/sid/actions?query=branch%3Amain
        
        .. image:: https://codecov.io/gh/covid-19-impact-lab/sid/branch/main/graph/badge.svg
            :target: https://codecov.io/gh/covid-19-impact-lab/sid
        
        .. image:: https://results.pre-commit.ci/badge/github/covid-19-impact-lab/sid/main.svg
            :target: https://results.pre-commit.ci/latest/github/covid-19-impact-lab/sid/main
            :alt: pre-commit.ci status
        
        .. image:: https://img.shields.io/badge/code%20style-black-000000.svg
            :target: https://github.com/psf/black
        
        .. end-badges
        
        
        Features
        --------
        
        sid is an agent-based simulation model for infectious diseases like COVID-19. It scales
        from simple examples to complex models which makes it an ideal tool for prototyping,
        educational purposes, and research.
        
        sid's focus is on predicting the effects of non-pharmaceutical interventions on the
        spread of an infectious disease. To accomplish this task it is built to capture
        important aspects of contacts between people. In particular, sid has the following
        features:
        
        1. At the core of the model, people meet people based on a matching algorithm. It is
           possible to represent a variety of contact types like households, leisure activities,
           school classes and nurseries with teachers and several types of contacts at the
           workplace. Contact types can be random or recurrent and vary in frequency.
        
        2. Policies allow to shut down contact types entirely or partially. The reduction of
           contacts can be random or systematic, e.g., to allow for essential workers.
        
        3. Infection probabilities vary across contact types and depending on the age of the
           susceptible individual, but are invariant to policies which reduce contacts. The
           invariance is an essential property for predicting the effects of policies for which
           empirical data does not exist.
        
        4. The model achieves a good fit on German infection and fatality rate data even if only
           the infection probabilities are fit to the data and the remaining parameters are
           calibrated from the medical literature and data on contact frequencies.
        
        5. The model allows for two testing mechanisms, representing PCR and rapid tests. PCR
           tests always reveal the true health status of the tested individual after some days
           which can be used for testing policies or to differentiate between known and unknown
           infections.
        
           In contrast, rapid tests immediately return the test outcome and identify infected
           people based on the sensitivity and specificity of the test. It is possible to
           implement reactions to the outcome of the test enabling individuals to plan meetings,
           test with a rapid test, and to refrain from meeting if the test is positive.
        
        6. Mutations may lead to multiple, prevalent virus strains with different
           characteristics. For now, sid is able to model an unlimited amount of virus strains
           with different degrees of infectiousness.
        
        7. It is possible to implement models for vaccinating people who, then, gain perfect
           immunity from the disease.
        
        More information can be found in the `discussion paper
        <https://www.iza.org/publications/dp/13899>`_ or in the `documentation
        <https://sid-dev.readthedocs.io/en/latest/>`_.
        
        
        .. start-installation
        
        Installation
        ------------
        
        sid is available on `PyPI <https://pypi.org/project/sid-dev>`_ and on `Anaconda.org
        <https://anaconda.org/conda-forge/sid-dev>`_ and can be installed with
        
        .. code-block:: bash
        
            $ pip install sid-dev
        
            # or
        
            $ conda install -c conda-forge sid-dev
        
        .. end-installation
        
        
        Publications
        ------------
        
        sid has been featured in some publications which are listed here:
        
        - Gabler, J., Raabe, T., & Röhrl, K. (2020). `People Meet People: A Microlevel Approach
          to Predicting the Effect of Policies on the Spread of COVID-19
          <http://ftp.iza.org/dp13899.pdf>`_.
        
        - Dorn, F., Gabler, J., von Gaudecker, H. M., Peichl, A., Raabe, T., & Röhrl, K. (2020).
          `Wenn Menschen (keine) Menschen treffen: Simulation der Auswirkungen von
          Politikmaßnahmen zur Eindämmung der zweiten Covid-19-Welle
          <https://www.ifo.de/DocDL/sd-2020-digital-15-dorn-etal-politikmassnahmen-covid-19-
          zweite-welle.pdf>`_. ifo Schnelldienst Digital, 1(15).
        
        - Gabler, J., Raabe, T., Röhrl, K., & Gaudecker, H. M. V. (2020). `Die Bedeutung
          individuellen Verhaltens über den Jahreswechsel für die Weiterentwicklung der
          Covid-19-Pandemie in Deutschland <http://ftp.iza.org/sp99.pdf>`_ (No. 99). Institute
          of Labor Economics (IZA).
        
        - Gabler, J., Raabe, T., Röhrl, K., & Gaudecker, H. M. V. (2021). `Der Effekt von
          Heimarbeit auf die Entwicklung der Covid-19-Pandemie in Deutschland
          <http://ftp.iza.org/sp100.pdf>`_ (No. 100). Institute of Labor Economics (IZA).
        
        
        Citation
        --------
        
        If you rely on sid for your own research, please cite it with
        
        .. code-block::
        
            @article{Gabler2020,
              Title = {
                People Meet People: A Microlevel Approach to Predicting the Effect of Policies
                on the Spread of COVID-19
              },
              Author = {Gabler, Janos and Raabe, Tobias and R{\"o}hrl, Klara},
              Year = {2020},
              Publisher = {IZA Discussion Paper}
            }
        
Platform: any
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
