import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from epimargin.utils import cwd
import statsmodels.api as sm
# load data
data = cwd() / "example_data"
df = pd.read_csv(data / "metro_state_policy_evaluation.csv").dropna()
df["Rt"] = df["RR_pred"]
df["Rt_binarized"] = (df["RR_pred"] >= 1).astype(int)
drop_cols = [col for col in df.columns if col.startswith("RR_")] + [
"metro-state", "date", "state", "state_name", "start_stay_at_home",
"cbsa_fips", "end_stay_at_home", "mask_mandate_all",
"metro_outbreak_start", "threshold_ind", "new_cases_ts",
"daily_confirmed_cases", "religious_exception_stay_home"
]
df.drop(columns=drop_cols, inplace=True)
df.rename(lambda x: x.replace(".0", ""), axis=1, inplace=True)
# set up metro fixed effects (some metro cols are all 0)
metros = [
col for col in df.columns if col.startswith("metro") if df[col].sum() > 0
]
# drop one metro to avoid multicollinearity
reference_metro, *metros = metros
drop_metro_cols = [
col for col in df.columns
if show:
plt.show()
return coef
def notch_filter(ts):
"Implement notch filter with notches at {1/7, 2/7}"
fs, f0, Q = 1, 1/7, 1
b1, a1 = iirnotch(f0, Q, fs)
b2, a2 = iirnotch(2*f0, 2*Q, fs)
b = convolve(b1, b2)
a = convolve(a1, a2)
notched = pd.Series(filtfilt(b, a, ts))
notched.index = ts.index
return notched
root = cwd()
data = mkdir(root/"data")
figs = mkdir(root/"figs")
###########################################################
# download latest case data
# download_data(data, 'state_wise_daily.csv')
# df = load_statewise_data(data/"state_wise_daily.csv")
# ts = get_time_series(df, "state")
###########################################################
# load delay data
api_diff = pd.read_csv(data/"daily_diff.csv", parse_dates=["status_change_date", "report_date"], dayfirst=True)
delay = api_diff[(api_diff.current_status == "Hospitalized") & (api_diff.report_date > "2020-08-02")].copy()
delay = delay.drop(columns = [col for col in delay.columns if col.startswith("Unnamed")] + ["rowhash"])
delay["newhash"] = delay[["patient_number", "date_announced", "detected_district", "detected_state","current_status", "status_change_date", "num_cases"]].apply(lambda x: hash(tuple(x)), axis = 1)
simplefilter("ignore")
def project(dates, R_values, smoothing, period=7 * days):
julian_dates = [_.to_julian_date() for _ in dates[-smoothing // 2:None]]
return OLS(
RR_pred[-smoothing//2:None],
add_constant(julian_dates)
)\
.fit()\
.predict([1, julian_dates[-1] + period])[0]
# set to cloud temp directory if not explicitly told to run locally
root = cwd() if len(sys.argv) > 1 and sys.argv[1] == "--local" else Path(
"/tmp")
data = root / "data"
# model details
gamma = 0.2
smoothing = 10
CI = 0.95
download_data(data, 'state_wise_daily.csv')
state_df = load_statewise_data(data / "state_wise_daily.csv")
country_time_series = get_time_series(state_df)
estimates = []
timeseries = []
