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# This code released under an MIT license. In notebook form with more explanation: | |
# https://github.com/nikhaldi/covid-notebooks/blob/master/Mobility%20vs%20COVID-19%20growth%20plot.ipynb | |
import numpy as np | |
import pandas as pd | |
us_counties = pd.read_csv( | |
"https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv", | |
dtype={"fips": str}, | |
parse_dates=["date"] | |
) | |
mobility = pd.read_csv( | |
"https://raw.githubusercontent.com/descarteslabs/DL-COVID-19/master/DL-us-mobility-daterow.csv", | |
dtype={'fips': str}, | |
parse_dates=['date'] | |
) | |
us_counties_march_week4 = us_counties.set_index('date').loc["2020-03-23":"2020-03-29"] | |
mobility_march_week2 = mobility.set_index("date").loc["2020-03-09":"2020-03-15"] | |
us_counties_march_week4_ny = us_counties_march_week4[(us_counties_march_week4["state"] == "New York")] | |
mobility_march_week2_ny = mobility_march_week2[ | |
(mobility_march_week2["admin_level"] == 2) & (mobility_march_week2["admin1"] == "New York") | |
] | |
def mean_daily_growth(series): | |
return np.mean(series / series.shift(1) - 1) | |
us_counties_march_week4_ny_mean_growth = us_counties_march_week4_ny[["fips", "cases"]].groupby("fips").aggregate( | |
max_cases=pd.NamedAgg(column="cases", aggfunc=np.max), | |
mean_daily_growth=pd.NamedAgg(column="cases", aggfunc=mean_daily_growth) | |
) | |
mobility_march_week2_ny_mean = mobility_march_week2_ny.groupby("fips").mean() | |
min_cases_threshold = 20 | |
merged = pd.merge( | |
us_counties_march_week4_ny_mean_growth[ | |
us_counties_march_week4_ny_mean_growth["max_cases"] >= min_cases_threshold | |
], | |
mobility_march_week2_ny_mean["m50_index"], | |
on="fips" | |
) | |
slope, intercept = np.polyfit(merged.m50_index, np.log(merged.mean_daily_growth), deg=1) | |
y_log_estimated = slope * merged.m50_index + intercept | |
ax = merged.plot.scatter(x="m50_index", y="mean_daily_growth", figsize=(10,10)) | |
ax.plot(merged.m50_index, np.exp(y_log_estimated)) |
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