Metadata-Version: 2.1
Name: covid-seird
Version: 0.0.6
Summary: A small package that implements the SEIRD Epidemiological
                   Model on COVID-19 data.
Home-page: https://github.com/paulorobertobranco/covid_seird
Author: Paulo Branco
Author-email: paulorobertobranco@gmail.com
License: UNKNOWN
Description: 
        # [covid-seird](https://pypi.org/project/covid-seird/)
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        ## About
        
        **covid-seird** is a small Python package inspired by Henri Froese's post ['Infectious Disease Modelling: Beyond the Basic SIR Model'](https://towardsdatascience.com/infectious-disease-modelling-beyond-the-basic-sir-model-216369c584c4).
        
        It implements the **SEIRD Epidemiological Model** on COVID-19 data.
        - First, it **fits** a the SEIRD Model into the real timeline data of confirmed cases of COVID-19 of a country.
        - Then, it **simulates** the SEIRD curves based on the previously fitted Model.
        - As a result, the country's **basic reproduction number (R<sub>o</sub>)** of COVID-19 is computed.
        - Also, the **fit and simulation plots** are available.
        
        
        NOTE:
          The COVID-19 timelines data are obtained using the [COVID19Py package](https://github.com/Kamaropoulos/COVID19Py) in order to retrieve the Worldwide Data repository operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE).
        
        ## Installation
        
        In order to install this package, simply run:
        
        ```bash
        pip install covid-seird
        ```
        
        ## Usage
        
        To use covid-seird, you first need to import the package and then create a new instance, passing the country code as a parameter:
        
        ```python
        from covid_seird import CountryCovidSeird
        
        brazil = CountryCovidSeird("br")
        ```
        
        ### Getting the country codes
        
        ```python
        CountryCovidSeird.code_search("brazil")
        ```
        output:
        ```python
        {'BR': 'Brazil'}
        ```
        
        ### Fit the SEIRD Model
        
        ```python
        brazil.fit()
        ```
        After the fit method's call, the fit score (R<sup>2</sup>) can be accessed:
        ```python
        brazil.r2
        ```
        output:
        ```python
        0.9183167162166936
        ```
        Also after fit method's call, the basic reproduction number (R<sub>o</sub>) will be available:
        
        ```python
        brazil.r0
        ```
        output:
        ```python
        2.2353912163446745
        ```
        ### Fit plot
        
        ```python
        brazil.plot_fit("brazil_fit_plot")
        ```
        a plot file named 'brazil_fit_plot' will be created.
        
        ![fit plot](https://raw.githubusercontent.com/paulorobertobranco/covid_seird/master/examples/brazil_fit_plot.png?token=ABKF5K5HY7SASBQAF34EOLK63UH7I)
        
        ### SEIRD simulation
        The simulation method receives as a parameter how many days ahead of the real data the SEIRD model will be computed.
        ```python
        brazil.simulation(days_ahead=150)
        ```
        The SEIRD curves can be accessed after the simulation method's call
        ```python
        brazil.curves['infected']
        ```
        output:
        ```python
        array([4.77396851e-09, 5.32992037e-09, 5.95375686e-09, 6.59631344e-09,
               7.32558927e-09, 8.14396099e-09, 9.05142859e-09, 1.00479921e-08,
               ...
               7.48473200e-03, 7.06776956e-03, 6.67386382e-03, 6.30175876e-03,
               5.95026480e-03, 5.61825534e-03, 5.30466353e-03, 5.00847963e-03])
        ```
        
        ### Simulation plots
        ```python
        brazil.plot_simulation("brazil_simulation_plot")
        ```
        
        a plot file named 'brazil_simulation_plot' will be created.
        
        ![simulation plot](https://raw.githubusercontent.com/paulorobertobranco/covid_seird/master/examples/brazil_simulation_plot.png?token=ABKF5K4FPIDQ3VEWOZKTB2263UH4I)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
