Metadata-Version: 1.1
Name: covsirphy
Version: 2.3.0
Summary: Package for COVID-19 data analysis with SIR-derived models
Home-page: https://github.com/lisphilar/covid19-sir
Author: Lisphilar
Author-email: lisphilar@outlook.jp
License: Apache License 2.0
Description: CovsirPhy: COVID-19 data with SIR model |GitHub license|\ |Python version|
        ==========================================================================
        
        CovsirPhy is a Python package for COVID-19 (Coronavirus disease 2019)
        data analysis with SIR-derived models. Please refer to "Method" part of
        `COVID-19 data with SIR
        model <https://www.kaggle.com/lisphilar/covid-19-data-with-sir-model>`__
        notebook in Kaggle to understand the methods.
        
        SIR-F is a customized SIR-derived ODE model. To evaluate the effect of
        measures, parameter estimation of SIR-F will be applied to subsets of
        time series data in each country. Parameter change points will be
        determined by S-R trend analysis.
        
        Functionalities
        ---------------
        
        -  Downloading and cleaning data
        
           -  Epidemic data: the number of confirmed/fatal/recovered cases
           -  Population data: raw data must include country, (province), values
              of population
        
        -  Data visualization with Matplotlib
        -  S-R Trend analysis with Optuna and scipy.optimize.curve\_fit
        -  Numerical simulation of ODE models with scipy.integrate.solve\_ivp
        -  Description of ODE models
        
           -  Basic class of ODE models
           -  SIR, SIR-D, SIR-F, SIR-FV and SEWIR-F model
        
        -  Parameter Estimation of ODE models with Optuna and numerical
           simulation
        -  Simulate the number of cases with user-defined parameter values
        
        Inspiration
        -----------
        
        -  Monitor the spread of COVID-19
        -  Keep track parameter values/reproductive number in each
           country/province
        -  Find the relationship of reproductive number and measures taken in
           each country/province
        
        Trying now
        ----------
        
        The author is trying to add the following functionalities. - Speed-up
        S-R trend analysis and hyperparameter estimation of ODE models - Keep
        track parameter values/reproductive number of all countries with a
        simple code - Find relationship of reproductive number and measures
        automatically
        
        If you have ideas or need new functionalities, please join this project.
        Any suggestions with `Github
        Issues <https://github.com/lisphilar/covid19-sir/issues/new/choose>`__
        are always welcomed.
        
        Installation and dataset preparation
        ------------------------------------
        
        We have the following options to start analysis with CovsirPhy. Datasets
        are not included in this package, but we can prepare them with
        ``DataLoader`` class.
        
        +--------------------------------+----------------+---------------------------------------+
        |                                | Installation   | Dataset preparation                   |
        +================================+================+=======================================+
        | Standard users                 | pip/pipenv     | Automated with ``DataLoader`` class   |
        +--------------------------------+----------------+---------------------------------------+
        | Developers                     | git-cloning    | Automated with ``DataLoader`` class   |
        +--------------------------------+----------------+---------------------------------------+
        | Kagglers (local environment)   | git-cloning    | Kaggle API and Python script          |
        +--------------------------------+----------------+---------------------------------------+
        | Kagglers (Kaggle platform)     | pip            | Kaggle Datasets                       |
        +--------------------------------+----------------+---------------------------------------+
        
        We will use the following datasets.
        
        +-------+-------+-------+
        |       | Descr | URL   |
        |       | iptio |       |
        |       | n     |       |
        +=======+=======+=======+
        | The   | COVID | https |
        | numbe | -19   | ://gi |
        | r     | Data  | thub. |
        | of    | Repos | com/C |
        | cases | itory | SSEGI |
        | (JHU) | by    | SandD |
        |       | the   | ata/C |
        |       | Cente | OVID- |
        |       | r     | 19    |
        |       | for   |       |
        |       | Syste |       |
        |       | ms    |       |
        |       | Scien |       |
        |       | ce    |       |
        |       | and   |       |
        |       | Engin |       |
        |       | eerin |       |
        |       | g     |       |
        |       | (CSSE |       |
        |       | )     |       |
        |       | at    |       |
        |       | Johns |       |
        |       | Hopki |       |
        |       | ns    |       |
        |       | Unive |       |
        |       | rsity |       |
        |       | .     |       |
        +-------+-------+-------+
        | The   | Lisph | https |
        | numbe | ilar  | ://gi |
        | r     | (2020 | thub. |
        | of    | ),    | com/l |
        | cases | COVID | isphi |
        | in    | -19   | lar/c |
        | Japan | datas | ovid1 |
        |       | et    | 9-sir |
        |       | in    | /data |
        |       | Japan | /japa |
        |       | .     | n     |
        +-------+-------+-------+
        | Popul | The   | https |
        | ation | World | ://da |
        | in    | Bank  | ta.wo |
        | each  | Group | rldba |
        | count | (2020 | nk.or |
        | ry    | ),    | g/ind |
        |       | THE   | icato |
        |       | WORLD | r/SP. |
        |       | BANK, | POP.T |
        |       | Popul | OTL   |
        |       | ation |       |
        |       | ,     |       |
        |       | total |       |
        |       | .     |       |
        +-------+-------+-------+
        | Gover | Thoma | https |
        | nment | s     | ://gi |
        | Respo | Hale, | thub. |
        | nse   | Sam   | com/O |
        | Track | Webst | xCGRT |
        | er    | er,   | /covi |
        | (OxCG | Anna  | d-pol |
        | RT)   | Pethe | icy-t |
        |       | rick, | racke |
        |       | Toby  | r     |
        |       | Phill |       |
        |       | ips,  |       |
        |       | and   |       |
        |       | Beatr |       |
        |       | iz    |       |
        |       | Kira. |       |
        |       | (2020 |       |
        |       | ).    |       |
        |       | Oxfor |       |
        |       | d     |       |
        |       | COVID |       |
        |       | -19   |       |
        |       | Gover |       |
        |       | nment |       |
        |       | Respo |       |
        |       | nse   |       |
        |       | Track |       |
        |       | er.   |       |
        |       | Blava |       |
        |       | tnik  |       |
        |       | Schoo |       |
        |       | l     |       |
        |       | of    |       |
        |       | Gover |       |
        |       | nment |       |
        |       | .     |       |
        +-------+-------+-------+
        
        If you want to use a new dataset for your analysis, please kindly inform
        us via `GitHub
        Issues <https://github.com/lisphilar/covid19-sir/issues/new/choose>`__
        with "Request new method of DataLoader class" template.
        
        1. Standard users
        ~~~~~~~~~~~~~~~~~
        
        Covsirphy is available at PyPI (The Python Package Index) and supports
        Python 3.7 or newer versions.
        
        ::
        
            pip install covsirphy
        
        Then, download the datasets with the following codes, when you want to
        save the data in ``input`` directory.
        
        .. code:: python
        
            import covsirphy as cs
            data_loader = cs.DataLoader("input")
            jhu_data = data_loader.jhu()
            japan_data = data_loader.japan()
            population_data = data_loader.population()
            oxcgrt_data = data_loader.oxcgrt()
        
        If ``input`` directory has the datasets, ``DataLoader`` will load the
        local files. If the datasets were updated in remote servers,
        ``DataLoader`` will update the local files automatically.
        
        We can get descriptions of the datasets and raw/cleaned datasets easily.
        As an example, JHU dataset will be used here.
        
        .. code:: python
        
            # Description (string)
            jhu_data.citation
            # Raw data (pandas.DataFrame)
            jhu_data.raw
            # Cleaned data (pandas.DataFrame)
            jhu_data.cleaned()
        
        2. Developers
        ~~~~~~~~~~~~~
        
        Developers will clone this repository with ``git clone`` command and
        install dependencies with pipenv.
        
        ::
        
            git clone https://github.com/lisphilar/covid19-sir.git
            cd covid19-sir
            pip install wheel; pip install --upgrade pip; pip install pipenv
            export PIPENV_VENV_IN_PROJECT=true
            export PIPENV_TIMEOUT=7200
            pipenv install --dev
        
        Developers can perform tests.
        
        -  Windows user need to install `Graphviz for
           Windows <https://graphviz.org/_pages/Download/Download_windows.html>`__
           in advance.
        -  Debian/Ubuntu user need to install Graphviz with
           ``sudo apt install graphviz`` in advance.
        
        Tests can be done with
        ``pipenv run pytest -v --durations=0 --profile-svg`` and call graph will
        be saved as SVG file (prof/combined.svg).
        
        If you can run ``make`` command,
        
        +--------------------+----------------------------------------------------+
        | ``make install``   | Install pipenv and the dependencies of CovsirPhy   |
        +--------------------+----------------------------------------------------+
        | ``make test``      | Run tests using Pytest                             |
        +--------------------+----------------------------------------------------+
        | ``make docs``      | Update sphinx document                             |
        +--------------------+----------------------------------------------------+
        | ``make example``   | Run example codes                                  |
        +--------------------+----------------------------------------------------+
        | ``make clean``     | Clean-up output files and pipenv environment       |
        +--------------------+----------------------------------------------------+
        
        We can prepare the dataset with the same codes as that explained in
        "1.Preferred" subsection.
        
        3. Kagglers (local environment)
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        As explained in "2. Developers" subsection, we need to git-clone this
        repository and install the dependencies when you want to uses this
        package with Kaggle API in your local environment.
        
        Then, please move to account page and download "kaggle.json" by
        selecting "API > Create New API Token" button. Copy the json file to the
        top directory of the local repository. Please refer to `How to Use
        Kaggle: Public API <https://www.kaggle.com/docs/api>`__ and
        `stackoverflow: documentation for Kaggle API *within*
        python? <https://stackoverflow.com/questions/55934733/documentation-for-kaggle-api-within-python#:~:text=Here%20are%20the%20steps%20involved%20in%20using%20the%20Kaggle%20API%20from%20Python.&text=Go%20to%20your%20Kaggle%20account,json%20will%20be%20downloaded>`__
        
        We can download datasets with ``pipenv run ./input.py`` command.
        Modification of environment variables is un-necessary. Files will be
        saved in ``input`` directory of your local repository.
        
        | Note:
        | Except for OxCGRT dataset, the datasets downloaded with ``input.py``
          scripts are not the same as that explained in the previous
          subsections. URLs are shown in the next table.
        
        +-------+-------+-------+
        |       | Descr | URL   |
        |       | iptio |       |
        |       | n     |       |
        +=======+=======+=======+
        | The   | Novel | https |
        | numbe | Coron | ://ww |
        | r     | a     | w.kag |
        | of    | Virus | gle.c |
        | cases | 2019  | om/su |
        | (JHU) | Datas | dalai |
        |       | et    | rajku |
        |       | by    | mar/n |
        |       | SRK   | ovel- |
        |       |       | coron |
        |       |       | a-vir |
        |       |       | us-20 |
        |       |       | 19-da |
        |       |       | taset |
        +-------+-------+-------+
        | The   | COVID | https |
        | numbe | -19   | ://ww |
        | r     | datas | w.kag |
        | of    | et    | gle.c |
        | cases | in    | om/li |
        | in    | Japan | sphil |
        | Japan | by    | ar/co |
        |       | Lisph | vid19 |
        |       | ilar  | -data |
        |       |       | set-i |
        |       |       | n-jap |
        |       |       | an    |
        +-------+-------+-------+
        | Popul | covid | https |
        | ation | 19    | ://ww |
        | in    | globa | w.kag |
        | each  | l     | gle.c |
        | count | forec | om/dg |
        | ry    | astin | rechk |
        |       | g:    | a/cov |
        |       | locat | id19- |
        |       | ions  | globa |
        |       | popul | l-for |
        |       | ation | ecast |
        |       | by    | ing-l |
        |       | Dmitr | ocati |
        |       | y     | ons-p |
        |       | A.    | opula |
        |       | Grech | tion  |
        |       | ka    |       |
        +-------+-------+-------+
        | Gover | Thoma | https |
        | nment | s     | ://gi |
        | Respo | Hale, | thub. |
        | nse   | Sam   | com/O |
        | Track | Webst | xCGRT |
        | er    | er,   | /covi |
        | (OxCG | Anna  | d-pol |
        | RT)   | Pethe | icy-t |
        |       | rick, | racke |
        |       | Toby  | r     |
        |       | Phill |       |
        |       | ips,  |       |
        |       | and   |       |
        |       | Beatr |       |
        |       | iz    |       |
        |       | Kira. |       |
        |       | (2020 |       |
        |       | ).    |       |
        |       | Oxfor |       |
        |       | d     |       |
        |       | COVID |       |
        |       | -19   |       |
        |       | Gover |       |
        |       | nment |       |
        |       | Respo |       |
        |       | nse   |       |
        |       | Track |       |
        |       | er.   |       |
        |       | Blava |       |
        |       | tnik  |       |
        |       | Schoo |       |
        |       | l     |       |
        |       | of    |       |
        |       | Gover |       |
        |       | nment |       |
        |       | .     |       |
        +-------+-------+-------+
        
        Usage of ``DataLoader`` class is as follows. Please specify
        ``local_file`` argument in the methods.
        
        .. code:: python
        
            import covsirphy as cs
            data_loader = cs.DataLoader("input")
            jhu_data = data_loader.jhu(local_file="covid_19_data.csv")
            japan_data = data_loader.japan(local_file="covid_jpn_total.csv")
            population_data = data_loader.population(local_file="locations_population.csv")
            oxcgrt_data = data_loader.oxcgrt(local_file="OxCGRT_latest.csv")
        
        4. Kagglers (Kaggle platform)
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        When you want to use this package in Kaggle notebook, please turn on
        Internet option in notebook setting and download the datasets explained
        in the previous section.
        
        Then, install this package with pip command.
        
        .. code:: python
        
            !pip install covsirphy
        
        Then, please load the datasets with the following codes, specifying the
        filenames.
        
        .. code:: python
        
            import covsirphy as cs
            # The number of cases (JHU)
            jhu_data = cs.JHUData("/kaggle/input/novel-corona-virus-2019-dataset/covid_19_data.csv")
            # (Optional) The number of cases in Japan
            jpn_data = cs.CountryData("/kaggle/input/covid19-dataset-in-japan/covid_jpn_total.csv", country="Japan")
            jpn_data.set_variables(
                date="Date", confirmed="Positive", fatal="Fatal", recovered="Discharged", province=None
            )
            # Population in each country
            pop_data = cs.Population(
                "/kaggle/input/covid19-global-forecasting-locations-population/locations_population.csv"
            )
        
        | Note:
        | Currently, OxCGRT dataset is not supported.
        
        Quick usage for analysis
        ------------------------
        
        Example Python codes are in ``example`` directory. With Pipenv
        environment, we can run the Python codes with Bash code ``example.sh``
        in the top directory of this repository.
        
        Preparation
        ~~~~~~~~~~~
        
        .. code:: python
        
            import covsirphy as cs
            cs.__version__
        
        Please load the datasets as explained in the previous section.
        
        (Optional) We can replace a part of JHU data with country-specific
        datasets. As an example, we will the records in Japan, because values of
        JHU dataset sometimes differ from government-announced values as shown
        in `COVID-19: Government/JHU data in
        Japan <https://www.kaggle.com/lisphilar/covid-19-government-jhu-data-in-japan>`__.
        
        .. code:: python
        
            jhu_data.replace(japan_data)
            ncov_df = jhu_data.cleaned()
        
        Scenario analysis
        ~~~~~~~~~~~~~~~~~
        
        As an example, use dataset in Italy. #### Check records
        
        .. code:: python
        
            ita_scenario = cs.Scenario(jhu_data, population_data, country="Italy", province=None)
        
        See the records as a figure.
        
        .. code:: python
        
            ita_record_df = ita_scenario.records()
        
        S-R trend analysis
        ^^^^^^^^^^^^^^^^^^
        
        Show S-R trend to determine the number of change points.
        
        .. code:: python
        
            ita_scenario.trend()
        
        As an example, set the number of change points as 4.
        
        .. code:: python
        
            ita_scenario.trend(n_points=4, set_phases=True)
        
        Start/end date of the four phase were automatically determined. Let's
        see.
        
        .. code:: python
        
            print(ita_scenario.summary())
        
        Hyperparameter estimation of ODE models
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        As an example, use SIR-F model.
        
        .. code:: python
        
            ita_scenario.estimate(cs.SIRF)
            print(ita_scenario.summary())
        
        We can check the accuracy of estimation with a figure.
        
        .. code:: python
        
            # Table
            ita_scenario.estimate_accuracy(phase="1st")
            # Get a value
            ita_scenario.get("Rt", phase="4th")
            # Show parameter history as a figure
            ita_scenario.param_history(targets=["Rt"], divide_by_first=False, box_plot=False)
            ita_scenario.param_history(targets=["rho", "sigma"])
        
        Prediction of the number of cases
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        we can add some future phases.
        
        .. code:: python
        
            # if needed, clear the registered future phases
            ita_scenario.clear(name="Main")
            # Add future phase to main scenario
            ita_scenario.add_phase(name="Main", end_date="01Aug2020")
            # Get parameter value
            sigma_4th = ita_scenario.get("sigma", name="Main", phase="4th")
            # Add future phase with changed parameter value to new scenario
            sigma_6th = sigma_4th * 2
            ita_scenario.add_phase(end_date="31Dec2020", name="Medicine", sigma=sigma_6th)
            ita_scenario.add_phase(days=30, name="Medicine")
            print(ita_scenario.summary())
        
        Then, we can predict the number of cases and get a figure.
        
        .. code:: python
        
            # Prediction and show figure
            sim_df = ita_scenario.simulate(name="Main")
            # Describe representative values
            print(ita_scenario.describe())
        
        Apache License 2.0
        ------------------
        
        Please refer to
        `LICENSE <https://github.com/lisphilar/covid19-sir/blob/master/LICENSE>`__
        file.
        
        Citation
        --------
        
        Lisphilar, 2020, Kaggle notebook, COVID-19 data with SIR model,
        https://www.kaggle.com/lisphilar/covid-19-data-with-sir-model
        
        CovsirPhy development team, 2020, GitHub repository, CovsirPhy, Python
        package for COVID-19 data with SIR model,
        https://github.com/lisphilar/covid19-sir
        
        .. |GitHub license| image:: https://img.shields.io/github/license/lisphilar/covid19-sir
           :target: https://github.com/lisphilar/covid19-sir/blob/master/LICENSE
        .. |Python version| image:: https://img.shields.io/badge/Python-3.7%7C3.8-green.svg
           :target: https://www.python.org/
        
Keywords: covid19
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
