Metadata-Version: 2.1
Name: babino2020masks
Version: 0.0.7
Summary: Code used in https://arxiv.org/abs/2006.05532
Home-page: https://github.com/ababino/babino2020masks/tree/master/
Author: Andres Babino
Author-email: ababino@rockefeller.edu
License: Apache Software License 2.0
Description: # Masks and COVID-19: a causal framework for imputing value to public-health interventions
        > Code to reproduce <a href='https://arxiv.org/abs/2006.05532'>Masks and COVID-19</a>.
        
        
        This is a refactored version of the original [code](https://github.com/ababino/corona). 
        
        ## Install
        
        `pip install babino2020masks`
        
        ## How to use
        
        ### Gather data
        
        ```python
        ny = API(api_settings['NYS'][:2], **api_settings['NYS'][2])
        df = ny.get_all_data_statewide()
        ```
        
        ```python
        ax = plot_data_and_fit(df, 'Date', 'Odds', None, None, None, figsize=(10, 7))
        ax.set_title(f'{df.tail(1).Date[0]:%B %d, %Y}, Positivity Odds:{df.tail(1).Odds[0]:2.3}');
        ```
        
        
        ![png](docs/images/output_6_0.png)
        
        
        ### Fit the model
        
        ```python
        sdf = df.loc[df.Date<='15-05-2020'].copy()
        lics = LassoICSelector(sdf['Odds'], 'bic')
        lics.fit_best_alpha()
        ```
        
        ### Positivity Odds in NYS
        
        ```python
        sdf['Fit'], sdf['Odds_l'], sdf['Odds_u'] = lics.odds_hat_l_u()
        ax = plot_data_and_fit(sdf, 'Date', 'Odds', 'Fit', 'Odds_l', 'Odds_u', figsize=(10, 7))
        ```
        
        
        ![png](docs/images/output_10_0.png)
        
        
        ### Instantaneous reproduction number, $R_t$
        
        ```python
        sdf['R'], sdf['Rl'], sdf['Ru'] = lics.rt()
        ax = plot_data_and_fit(sdf, 'Date', None, 'R', 'Rl', 'Ru', figsize=(10, 7), logy=False, palette=[colorblind[1],colorblind[1]])
        ```
        
        
        ![png](docs/images/output_12_0.png)
        
        
        ### Counterfactual Scenario without  Masks
        
        ```python
        sdf['Cf. Odds'], sdf['cf_odds_l'], sdf['cf_odds_u'] = lics.counterfactual()
        ```
        
        ```python
        ax = plot_data_and_fit(sdf, 'Date', 'Odds', 'Fit', 'Odds_l', 'Odds_u', figsize=(10, 7))
        plot_data_and_fit(sdf, 'Date', None, 'Cf. Odds', 'cf_odds_l', 'cf_odds_u', palette=[colorblind[2],colorblind[2]], ax=ax);
        ```
        
        
        ![png](docs/images/output_15_0.png)
        
        
            Last updated on 11/16/2020 14:24:58
        
        
Keywords: coronavirus counterfactual causality masks covid19
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.8
Description-Content-Type: text/markdown
