Masks and COVID-19: a causal framework for imputing value to public-health interventions

Code to reproduce Masks and COVID-19.

This is a refactored version of the original code.

Install

pip install babino2020masks

How to use

Gather data

ny = API(api_settings['NYS'][:2], **api_settings['NYS'][2])
df = ny.get_all_data_statewide()

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}');

Fit the model

sdf = df.loc[df.Date<='15-05-2020'].copy()
lics = LassoICSelector(sdf['Odds'], 'bic')
lics.fit_best_alpha()

Positivity Odds in NYS

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))

Instantaneous reproduction number, $R_t$

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]])

Counterfactual Scenario without Masks

sdf['Cf. Odds'], sdf['cf_odds_l'], sdf['cf_odds_u'] = lics.counterfactual()

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);

Last updated on 04/03/2021 13:33:20