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February 9, 2021 11:54
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from datetime import datetime | |
from datetime import timedelta | |
import pandas as pd | |
import plotly.express as px | |
import plotly.graph_objs as go | |
import statsmodels.api as sm | |
import math | |
# df = pd.read_csv('owid-covid-data.csv') | |
df = pd.read_csv('vaccinations.csv') | |
df = df[df.date >= "2021-01-01 00:00:00.000"] | |
df = df[df.date <= "2021-02-08 00:00:00.000"] | |
df['day_of_year'] = [(datetime.strptime(d, "%Y-%m-%d") - datetime(2021,1,1)).days for d in df['date']] | |
df['daily_vaccinations_per_hundred'] = (1.0 / 10_000) * df['daily_vaccinations_per_million'] | |
# XXX sloppy way to set the x scale | |
x_days = 250 | |
x_max_date = datetime(2021,1,1) + timedelta(days=x_days) | |
fig = go.Figure( | |
layout_yaxis_range=[0,4], | |
layout_xaxis_range=["2021-01-01 00:00:00.0000", x_max_date], | |
layout_yaxis_title_text="daily vaccinations per hundred (7-day smoothed)", | |
layout_title_text="time to X% of population vaccinated", | |
layout_title_x=0.5, | |
) | |
# for country_index, country in enumerate(['United Kingdom', 'United States', 'Italy', 'France', 'Germany']): | |
for country_index, country in enumerate(['United Kingdom', 'United States', 'Germany']): | |
country_df = df[df.location == country] | |
country_df = country_df.dropna(subset=['daily_vaccinations_per_hundred']) | |
x = sm.add_constant(country_df['day_of_year']) | |
model = sm.OLS(country_df['daily_vaccinations_per_hundred'], x) | |
results = model.fit() | |
print(results.params) | |
b = results.params[0] | |
m = results.params[1] | |
# Area of triangle is H * W / 2 | |
# y = mx + b | |
# A(x) = x * (mx + b) / 2 | |
# solve for x via quadratic formula | |
# i'm assuming everyone starts at ~0 vaccinations before the line starts... | |
for p in [50, 100, 200]: | |
x_p = (((-1.0 * b) / 2) + math.sqrt(((b * b) / 4) + (2 * m * p)) / m) | |
date_p = datetime(2021,1,1) + timedelta(days=x_p) | |
print(f"time to hit {p}%: {x_p} days") | |
# vertical line marker | |
fig.add_shape( | |
type="line", | |
x0=date_p, | |
y0=0, | |
x1=date_p, | |
y1=((m * x_p) + b), | |
line_color=px.colors.qualitative.Dark2[country_index], | |
) | |
# text label for vertical line | |
fig.add_trace( | |
go.Scatter( | |
x=[date_p], | |
y=[((m * x_p) + b)], | |
text=[f"{country} {p}%"], | |
textfont_color=px.colors.qualitative.Dark2[country_index], | |
textfont_size=10, | |
mode="text", | |
showlegend=False, | |
) | |
) | |
# scatter plot for data | |
fig.add_trace( | |
go.Scatter( | |
x=country_df['date'], | |
y=country_df['daily_vaccinations_per_hundred'], | |
mode="markers", | |
marker_color=px.colors.qualitative.Dark2[country_index], | |
name=country, | |
showlegend=False, | |
) | |
) | |
# extrapolated line | |
fig.add_trace( | |
go.Scatter( | |
x=["2021-01-01 00:00:00.0000", x_max_date], | |
y=[results.params[0], (m*x_days + b)], | |
mode="lines", | |
marker_color=px.colors.qualitative.Dark2[country_index], | |
name=country, | |
showlegend=True, | |
) | |
) | |
fig.show() |
Author
joelanders
commented
Feb 9, 2021
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