+++ /dev/null
----
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----
-
-Data from [European Centre for Disease Prevention and Control](https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide)
-
-```python
-import itertools
-import collections
-import json
-import pandas as pd
-import numpy as np
-from scipy.stats import gmean
-import datetime
-
-import matplotlib as mpl
-import matplotlib.pyplot as plt
-%matplotlib inline
-```
-
-```python
-DEATH_COUNT_THRESHOLD = 10
-COUNTRIES_CORE = 'IT DE UK ES IE FR'.split()
-COUNTRIES_NORDIC = 'SE NO DK FI UK'.split()
-# COUNTRIES_FRIENDS = 'IT UK ES BE SI MX'.split()
-COUNTRIES_FRIENDS = 'IT UK ES BE SI PT'.split()
-
-COUNTRIES_AMERICAS = ['AG', 'AR', 'AW', 'BS', 'BB', 'BZ', 'BM', 'BO', 'BR', 'VG', 'KY', # excluding Canada and USA
- 'CL', 'CO', 'CR', 'CU', 'CW', 'DM', 'DO', 'EC', 'SV', 'GL', 'GD', 'GT',
- 'GY', 'HT', 'HN', 'JM', 'MX', 'MS', 'NI', 'PA', 'PY', 'PE', 'PR', 'KN',
- 'LC', 'VC', 'SX', 'SR', 'TT', 'TC', 'VI', 'UY', 'VE']
-COUNTRIES_OF_INTEREST = list(set(COUNTRIES_CORE + COUNTRIES_FRIENDS))
-COUNTRIES_ALL = list(set(COUNTRIES_CORE + COUNTRIES_FRIENDS + COUNTRIES_NORDIC + COUNTRIES_AMERICAS))
-```
-
-```python
-!curl https://opendata.ecdc.europa.eu/covid19/casedistribution/csv/ > covid.csv
-```
-
-```python
-# First col is a date, treat geoId of NA as 'Namibia', not "NA" value
-raw_data = pd.read_csv('covid.csv',
- parse_dates=[0], dayfirst=True,
- keep_default_na=False, na_values = [''],
-# dtype = {'day': np.int64,
-# 'month': np.int64,
-# 'year': np.int64,
-# 'cases': np.int64,
-# 'deaths': np.int64,
-# 'countriesAndTerritories': str,
-# 'geoId': str,
-# 'countryterritoryCode': str,
-# 'popData2019': np.int64,
-# 'continentExp': str,
-# }
- )
-```
-
-```python
-raw_data.size
-```
-
-```python
-raw_data.fillna(0, inplace=True)
-```
-
-```python
-raw_data.head()
-```
-
-```python
-raw_data.dtypes
-```
-
-```python
-raw_data = raw_data.astype({'dateRep': np.datetime64,
- 'day': np.int64,
- 'month': np.int64,
- 'year': np.int64,
- 'cases': np.int64,
- 'deaths': np.int64,
- 'countriesAndTerritories': str,
- 'geoId': str,
- 'countryterritoryCode': str,
- 'popData2019': np.int64,
- 'continentExp': str })
-```
-
-```python
-raw_data.dtypes
-```
-
-```python
-raw_data[((raw_data.geoId == 'UK') & (raw_data.dateRep >= '2020-07-10'))]
-```
-
-```python
-# raw_data = raw_data[~ ((raw_data.geoId == 'ES') & (raw_data.dateRep >= '2020-05-22'))]
-```
-
-```python
-base_data = raw_data.set_index(['geoId', 'dateRep'])
-base_data.sort_index(inplace=True)
-base_data
-```
-
-```python
-base_data.loc['ES'].loc['2020-05-10':]
-```
-
-```python
-countries = raw_data[['geoId', 'countriesAndTerritories', 'popData2019', 'continentExp']]
-countries = countries[countries['popData2019'] != '']
-countries = countries.drop_duplicates()
-countries.set_index('geoId', inplace=True)
-countries = countries.astype({'popData2019': 'int64'})
-countries.head()
-```
-
-```python
-countries.shape
-```
-
-```python
-countries[countries.countriesAndTerritories == 'Finland']
-```
-
-```python
-countries.loc[COUNTRIES_OF_INTEREST]
-```
-
-```python
-countries[countries.continentExp == 'America'].index
-```
-
-```python
-data_by_date = base_data[['cases', 'deaths']]
-data_by_date.head()
-```
-
-```python
-data_by_date.loc['UK']
-```
-
-```python
-# data_by_date.deaths.drop_duplicates().sort_values().to_csv('dth.csv', header=True)
-```
-
-```python
-data_by_date.groupby(level=0).cumsum()
-```
-
-```python
-data_by_date = data_by_date.merge(
- data_by_date.groupby(level=0).cumsum(),
- suffixes=('', '_culm'),
- left_index=True, right_index=True)
-data_by_date
-```
-
-```python
-data_by_date = data_by_date.merge(
- data_by_date[['cases', 'deaths']].groupby(level=0).diff(),
- suffixes=('', '_diff'),
- left_index=True, right_index=True)
-data_by_date
-```
-
-```python
-data_by_date.loc['UK', '2020-04-17']
-```
-
-```python
-data_by_date.loc['UK']
-```
-
-```python
-# data_by_date[data_by_date.deaths_culm > DEATH_COUNT_THRESHOLD]
-```
-
-```python
-# days_since_threshold = data_by_date[data_by_date.deaths_culm > DEATH_COUNT_THRESHOLD].groupby(level=0).cumcount()
-# days_since_threshold.rename('since_threshold', inplace=True)
-```
-
-```python
-dbd = data_by_date[data_by_date.deaths_culm > DEATH_COUNT_THRESHOLD].reset_index(level=1)
-dbd['since_threshold'] = dbd.dateRep
-dbd.set_index('dateRep', append=True, inplace=True)
-dbd.sort_index(inplace=True)
-days_since_threshold = dbd.groupby(level=0).diff().since_threshold.dt.days.fillna(0).astype(int).groupby(level=0).cumsum()
-# days_since_threshold.groupby(level=0).cumsum()
-
-# days_since_threshold = dbd.rename('since_threshold')
-days_since_threshold
-```
-
-```python
-# days_since_threshold = (data_by_date[data_by_date.deaths_culm > DEATH_COUNT_THRESHOLD]
-# .reset_index(level=1).groupby(level=0)
-# .diff().dateRep.dt.days
-# .groupby(level=0).cumcount()
-# )
-# days_since_threshold.rename('since_threshold', inplace=True)
-# days_since_threshold
-```
-
-```python
-data_since_threshold = data_by_date.merge(days_since_threshold,
- left_index=True, right_index=True)
-data_since_threshold
-```
-
-```python
-data_since_threshold = data_since_threshold.set_index('since_threshold', append=True
- ).reorder_levels(['since_threshold', 'geoId', 'dateRep']
- ).reset_index('dateRep')
-data_since_threshold.sort_index(inplace=True)
-data_since_threshold
-```
-
-```python
-data_since_threshold.loc[(slice(None), ['UK', 'DE', 'IT']), :]
-```
-
-```python
-data_since_threshold.loc[(slice(None), ['ES']), :].tail(8)
-```
-
-```python
-data_since_threshold.loc[(slice(None), ['UK', 'DE', 'IT']), ['deaths_culm']].unstack().plot(logy=True)
-```
-
-```python
-# deaths = data_since_threshold.loc[(slice(None), ['UK', 'DE', 'IT', 'IE']), ['deaths_culm']].unstack().xs('deaths_culm', axis=1, drop_level=True)
-```
-
-```python
-deaths = data_since_threshold.loc[(slice(None), COUNTRIES_ALL), ['deaths_culm']].unstack().sort_index().xs('deaths_culm', axis=1, drop_level=True)
-```
-
-```python
-cases = data_since_threshold.loc[(slice(None), COUNTRIES_ALL), ['cases_culm']].unstack().sort_index().xs('cases_culm', axis=1, drop_level=True)
-```
-
-```python
-COUNTRIES_AMERICAS_DEAD = list(set(deaths.columns) & set(COUNTRIES_AMERICAS))
-```
-
-```python
-data_since_threshold.reset_index().merge(countries, on='geoId').set_index(['since_threshold', 'geoId'])
-```
-
-```python
-data_since_threshold.reset_index().merge(countries, on='geoId').set_index(['since_threshold', 'geoId']).sort_index(inplace=True)
-```
-
-```python
-data_since_threshold_per_capita = data_since_threshold.reset_index().merge(countries, on='geoId').set_index(['since_threshold', 'geoId'])
-data_since_threshold_per_capita['cases_culm_pc'] = data_since_threshold_per_capita.cases_culm / data_since_threshold_per_capita.popData2019
-data_since_threshold_per_capita['deaths_culm_pc'] = data_since_threshold_per_capita.deaths_culm / data_since_threshold_per_capita.popData2019
-data_since_threshold_per_capita
-```
-
-```python
-deaths_pc = data_since_threshold_per_capita.loc[(slice(None), ['UK', 'DE', 'IT', 'IE']), ['deaths_culm_pc']].unstack().sort_index().xs('deaths_culm_pc', axis=1, drop_level=True)
-```
-
-```python
-deaths_pc.index
-```
-
-```python
-deaths_pc = data_since_threshold_per_capita.loc[(slice(None), COUNTRIES_ALL), ['deaths_culm_pc']].unstack().xs('deaths_culm_pc', axis=1, drop_level=True)
-```
-
-```python
-deaths[COUNTRIES_CORE].plot()
-```
-
-```python
-deaths[COUNTRIES_FRIENDS].plot()
-```
-
-```python
-ax = deaths[COUNTRIES_FRIENDS].plot(figsize=(10, 6), title="Total deaths, linear")
-ax.set_xlabel(f"Days since {DEATH_COUNT_THRESHOLD} deaths")
-for c in COUNTRIES_FRIENDS:
- lvi = deaths[c].last_valid_index()
- ax.text(x = lvi + 1, y = deaths[c][lvi], s = f"{c}: {deaths[c][lvi]:.0f}")
-# plt.savefig('covid_deaths_total_linear.png')
-```
-
-```python
-ax = deaths[COUNTRIES_CORE].plot(figsize=(10, 6), title="Total deaths, linear")
-ax.set_xlabel(f"Days since {DEATH_COUNT_THRESHOLD} deaths")
-for c in COUNTRIES_CORE:
- lvi = deaths[c].last_valid_index()
- ax.text(x = lvi + 1, y = deaths[c][lvi], s = f"{c}: {deaths[c][lvi]:.0f}")
-plt.savefig('covid_deaths_total_linear.png')
-```
-
-```python
-deaths_prime = deaths[COUNTRIES_CORE].copy()
-deaths_prime.loc[73:, 'ES'] = np.NaN
-# deaths_prime['ES'][70:]
-```
-
-```python
-ax = deaths_prime[COUNTRIES_CORE].plot(figsize=(10, 6), title="Total deaths, linear")
-for c in COUNTRIES_CORE:
- lvi = deaths_prime[c].last_valid_index()
- ax.text(x = lvi + 1, y = deaths_prime[c][lvi], s = f"{c}: {deaths_prime[c][lvi]:.0f}")
-# plt.savefig('covid_deaths_total_linear.png')
-```
-
-```python
-ax = cases[COUNTRIES_CORE].plot(figsize=(10, 6), title="Total cases, linear")
-for c in COUNTRIES_CORE:
- lvi = cases[c].last_valid_index()
- ax.text(x = lvi + 1, y = cases[c][lvi], s = c)
-plt.savefig('covid_cases_total_linear.png')
-```
-
-```python
-ax = deaths[COUNTRIES_AMERICAS_DEAD].plot(figsize=(10, 6), title="Total deaths, linear")
-for c in COUNTRIES_AMERICAS_DEAD:
- lvi = deaths[c].last_valid_index()
- ax.text(x = lvi + 1, y = deaths[c][lvi], s = c)
-# plt.savefig('covid_deaths_total_linear.png')
-```
-
-```python
-ax = deaths[COUNTRIES_CORE + ['BR', 'MX']].plot(figsize=(10, 6), title="Total deaths, linear")
-for c in COUNTRIES_CORE + ['BR', 'MX']:
- lvi = deaths[c].last_valid_index()
- ax.text(x = lvi + 1, y = deaths[c][lvi], s = c)
-# plt.savefig('covid_deaths_total_linear.png')
-```
-
-```python
-ax = deaths[COUNTRIES_NORDIC].plot(figsize=(10, 6), title="Total deaths, linear")
-for c in COUNTRIES_NORDIC:
- lvi = deaths[c].last_valid_index()
- ax.text(x = lvi + 1, y = deaths[c][lvi], s = c)
-# plt.savefig('covid_deaths_total_linear.png')
-```
-
-```python
-ax = deaths[COUNTRIES_OF_INTEREST].plot(figsize=(10, 6), title="Total deaths, linear")
-for c in COUNTRIES_OF_INTEREST:
- lvi = deaths[c].last_valid_index()
- ax.text(x = lvi + 1, y = deaths[c][lvi], s = c)
-plt.savefig('covid_deaths_total_linear_of_interest.png')
-```
-
-```python
-ax = deaths[COUNTRIES_CORE].plot(logy=True, figsize=(10, 6), title="Total deaths, log")
-for c in COUNTRIES_CORE:
- lvi = deaths[c].last_valid_index()
- ax.text(x = lvi + 1, y = deaths[c][lvi], s = c)
-
-plt.savefig('covid_deaths_total_log.png')
-```
-
-```python
-ylim = (5*10**3, 5*10**4)
-ax = deaths[COUNTRIES_CORE].plot(logy=True, figsize=(10, 6), ylim=ylim, title="Total deaths, log")
-for c in COUNTRIES_CORE:
- lvi = deaths[c].last_valid_index()
- if ylim[0] < deaths[c][lvi] < ylim[1]:
- ax.text(x = lvi + 1, y = deaths[c][lvi], s = c)
-
-# plt.savefig('covid_deaths_total_log.png')
-```
-
-```python
-ax = deaths[COUNTRIES_FRIENDS].plot(logy=True, figsize=(10, 6), title="Total deaths, log")
-for c in COUNTRIES_FRIENDS:
- lvi = deaths[c].last_valid_index()
- ax.text(x = lvi + 1, y = deaths[c][lvi], s = c)
-
-# plt.savefig('covid_deaths_total_log.png')
-```
-
-```python
-ax = deaths[COUNTRIES_NORDIC].plot(logy=True, figsize=(10, 6), title="Total deaths, log")
-for c in COUNTRIES_NORDIC:
- lvi = deaths[c].last_valid_index()
- ax.text(x = lvi + 1, y = deaths[c][lvi], s = c)
-
-# plt.savefig('covid_deaths_total_log.png')
-```
-
-```python
-ax = deaths[COUNTRIES_OF_INTEREST].plot(logy=True, figsize=(10, 6), title="Total deaths, log")
-for c in COUNTRIES_OF_INTEREST:
- lvi = deaths[c].last_valid_index()
- ax.text(x = lvi + 1, y = deaths[c][lvi], s = c)
-
-plt.savefig('covid_deaths_total_log.png')
-```
-
-```python
-ax = deaths_pc.plot(figsize=(10, 6), title="Deaths per capita, linear")
-for c in deaths_pc.columns:
- lvi = deaths_pc[c].last_valid_index()
- ax.text(x = lvi + 1, y = deaths_pc[c][lvi], s = c)
-plt.savefig('covid_deaths_per_capita_linear.png')
-```
-
-```python
-ax = deaths_pc.plot(logy=True, figsize=(10, 6), title="Deaths per capita, log")
-for c in deaths_pc.columns:
- lvi = deaths_pc[c].last_valid_index()
- ax.text(x = lvi + 1, y = deaths_pc[c][lvi], s = c)
-```
-
-```python
-deaths_pc[['UK', 'IE']].plot( figsize=(10, 6), title="Deaths per capita, linear")
-```
-
-```python
-deaths_pc[['UK', 'IE']].plot(logy=True, figsize=(10, 6), title="Deaths per capita, log")
-```
-
-```python
-deaths[['UK', 'ES', 'IT']].plot(logy=True, figsize=(10, 6), title="Deaths, log")
-plt.savefig('covid_deaths_selected_log.png')
-```
-
-```python
-deaths[['UK', 'ES', 'IT', 'MX']].plot(logy=True, figsize=(10, 6), title="Deaths, log")
-```
-
-```python
-data_since_threshold.loc[(slice(None), ['UK', 'DE', 'IT']), :]
-```
-
-```python
-data_since_threshold['deaths_m4'] = data_since_threshold.groupby(level=1)['deaths'].transform(lambda x: x.rolling(4, 1).mean())
-data_since_threshold['deaths_m7'] = data_since_threshold.groupby(level=1)['deaths'].transform(lambda x: x.rolling(7, 1).mean())
-data_since_threshold['cases_m7'] = data_since_threshold.groupby(level=1)['cases'].transform(lambda x: x.rolling(7, 1).mean())
-# data_since_threshold['deaths_diff_m4'] = data_since_threshold.groupby(level=1)['deaths_diff'].transform(lambda x: x.rolling(4, 1).mean())
-# data_since_threshold['deaths_diff_m7'] = data_since_threshold.groupby(level=1)['deaths_diff'].transform(lambda x: x.rolling(7, 1).mean())
-data_since_threshold.loc[(slice(None), ['UK', 'DE', 'IT']), :]
-```
-
-```python
-deaths_m4 = (data_since_threshold.loc[(slice(None), COUNTRIES_ALL), ['deaths_m4']]
- .unstack().sort_index().xs('deaths_m4', axis=1, drop_level=True))
-```
-
-```python
-deaths_m7 = (data_since_threshold.loc[(slice(None), COUNTRIES_ALL), ['deaths_m7']]
- .unstack().sort_index().xs('deaths_m7', axis=1, drop_level=True))
-```
-
-```python
-cases_m7 = (data_since_threshold.loc[(slice(None), COUNTRIES_ALL), ['cases_m7']]
- .unstack().sort_index().xs('cases_m7', axis=1, drop_level=True))
-```
-
-```python
-ax = deaths_m4.plot(figsize=(10, 6), title="Deaths per day, 4 day moving average")
-for c in deaths_m4.columns:
- lvi = deaths_m4[c].last_valid_index()
- ax.text(x = lvi + 1, y = deaths_m4[c][lvi], s = c)
-plt.savefig('covid_deaths_per_day.png')
-```
-
-```python
-ax = deaths_m4[COUNTRIES_CORE].plot(figsize=(10, 6), title="Deaths per day, 4 day moving average")
-for c in COUNTRIES_CORE:
- lvi = deaths_m4[c].last_valid_index()
- ax.text(x = lvi + 1, y = deaths_m4[c][lvi], s = c)
-plt.savefig('covid_deaths_per_day-core.png')
-```
-
-```python
-ax = deaths_m4[COUNTRIES_FRIENDS].plot(figsize=(10, 6), title="Deaths per day, 4 day moving average")
-for c in COUNTRIES_FRIENDS:
- lvi = deaths_m4[c].last_valid_index()
- ax.text(x = lvi + 1, y = deaths_m4[c][lvi], s = c)
-plt.savefig('covid_deaths_per_day-friends.png')
-```
-
-```python
-C7s = 'ES FR IT UK'.split()
-ax = deaths_m7[C7s].plot(figsize=(10, 6), title="Deaths per day, 7 day moving average")
-for c in C7s:
- lvi = deaths_m7[c].last_valid_index()
- ax.text(x = lvi + 1, y = deaths_m7[c][lvi], s = c)
-# plt.savefig('covid_deaths_per_day-friends.png')
-```
-
-```python
-ax = deaths_m7[COUNTRIES_CORE].plot(figsize=(10, 6), title="Deaths per day, 7 day moving average")
-ax.set_xlabel(f"Days since {DEATH_COUNT_THRESHOLD} deaths")
-for c in COUNTRIES_CORE:
- lvi = deaths_m7[c].last_valid_index()
- ax.text(x = lvi + 1, y = deaths_m7[c][lvi], s = c)
-# plt.axhline(0, color='0.7')
-plt.savefig('covid_deaths_per_day_7.png')
-```
-
-```python
-ax = deaths_m7[COUNTRIES_FRIENDS].plot(figsize=(10, 6), title="Deaths per day, 7 day moving average")
-ax.set_xlabel(f"Days since {DEATH_COUNT_THRESHOLD} deaths")
-for c in COUNTRIES_FRIENDS:
- lvi = deaths_m7[c].last_valid_index()
- ax.text(x = lvi + 1, y = deaths_m7[c][lvi], s = c)
-# plt.axhline(0, color='0.7')
-# plt.savefig('covid_deaths_per_day_7.png')
-```
-
-```python
-deaths_m7_prime = deaths_m7[COUNTRIES_CORE].copy()
-deaths_m7_prime.loc[73:, 'ES'] = np.NaN
-deaths_m7_prime['ES'][70:]
-```
-
-```python
-ax = deaths_m7_prime[COUNTRIES_CORE].plot(figsize=(10, 6), title="Deaths per day, 7 day moving average")
-for c in COUNTRIES_CORE:
- lvi = deaths_m7_prime[c].last_valid_index()
- ax.text(x = lvi + 1, y = deaths_m7_prime[c][lvi], s = c)
-# plt.savefig('covid_deaths_per_day_7.png') # see below for where this is written, with the projection
-```
-
-```python
-ax = deaths_m7[COUNTRIES_FRIENDS].plot(figsize=(10, 6), title="Deaths per day, 7 day moving average")
-for c in COUNTRIES_FRIENDS:
- lvi = deaths_m7[c].last_valid_index()
- ax.text(x = lvi + 1, y = deaths_m7[c][lvi], s = c)
-plt.savefig('covid_deaths_per_day_friends_7.png')
-```
-
-```python
-ax = deaths_m7[COUNTRIES_CORE + ['BR', 'MX']].plot(figsize=(10, 6), title="Deaths per day, 7 day moving average")
-for c in COUNTRIES_CORE + ['BR', 'MX']:
- lvi = deaths_m7[c].last_valid_index()
- ax.text(x = lvi + 1, y = deaths_m7[c][lvi], s = c)
-# plt.savefig('covid_deaths_per_day_7.png')
-```
-
-```python
-ax = cases_m7[COUNTRIES_CORE].plot(figsize=(10, 6), title="Cases per day, 7 day moving average")
-for c in COUNTRIES_CORE:
- lvi = cases_m7[c].last_valid_index()
- ax.text(x = lvi + 1, y = cases_m7[c][lvi], s = c)
-plt.savefig('covid_cases_per_day-core.png')
-```
-
-```python
-ax = cases_m7[COUNTRIES_FRIENDS].plot(figsize=(10, 6), title="Cases per day, 7 day moving average")
-for c in COUNTRIES_FRIENDS:
- lvi = cases_m7[c].last_valid_index()
- ax.text(x = lvi + 1, y = cases_m7[c][lvi], s = c)
-# plt.savefig('covid_cases_per_day-core.png')
-```
-
-```python
-def gmean_scale(items):
- return gmean(items) / items[-1]
-```
-
-```python
-def doubling_time(df):
- return np.log(2) / np.log((df.deaths_culm + df.deaths_g4) / df.deaths_culm)
-
-def doubling_time_7(df):
- return np.log(2) / np.log((df.deaths_culm + df.deaths_g7) / df.deaths_culm)
-```
-
-```python
-# data_since_threshold['deaths_g4'] = data_since_threshold.groupby(level=1)['deaths'].transform(lambda x: x.rolling(4, 1).apply(gmean_scale, raw=True))
-# data_since_threshold.loc[(slice(None), ['UK', 'DE', 'IT']), :]
-```
-
-```python
-data_since_threshold['deaths_g4'] = data_since_threshold.groupby(level=1)['deaths'].transform(lambda x: x.rolling(4, 1).apply(gmean, raw=True))
-data_since_threshold['deaths_g7'] = data_since_threshold.groupby(level=1)['deaths'].transform(lambda x: x.rolling(7, 1).apply(gmean, raw=True))
-data_since_threshold.loc[(slice(None), ['UK', 'DE', 'IT']), :]
-```
-
-```python
-data_since_threshold['doubling_time'] = data_since_threshold.groupby(level=1).apply(doubling_time).reset_index(level=0, drop=True).sort_index()
-data_since_threshold['doubling_time_7'] = data_since_threshold.groupby(level=1).apply(doubling_time_7).reset_index(level=0, drop=True).sort_index()
-# data_since_threshold.loc[(slice(None), 'UK'), :]
-```
-
-```python
-doubling_times = (data_since_threshold.loc[(slice(None), COUNTRIES_OF_INTEREST), ['doubling_time']]
- .unstack().sort_index().xs('doubling_time', axis=1, drop_level=True))
-doubling_times.replace([np.inf, -np.inf], np.nan, inplace=True)
-```
-
-```python
-doubling_times_7 = (data_since_threshold.loc[(slice(None), COUNTRIES_OF_INTEREST), ['doubling_time_7']]
- .unstack().sort_index().xs('doubling_time_7', axis=1, drop_level=True))
-doubling_times_7.replace([np.inf, -np.inf], np.nan, inplace=True)
-```
-
-```python
-ax = doubling_times.plot(figsize=(10, 6), title="Doubling times, 4 day average")
-for c in doubling_times.columns:
- lvi = doubling_times[c].last_valid_index()
- ax.text(x = lvi + 1, y = doubling_times[c][lvi], s = c)
-# plt.savefig('covid_deaths_per_day.png')
-```
-
-```python
-ax = doubling_times_7[COUNTRIES_CORE].plot(figsize=(10, 6), title="Doubling times, 7 day average")
-ax.legend(loc="upper left")
-for c in COUNTRIES_CORE:
- lvi = doubling_times_7[c].last_valid_index()
- ax.text(x = lvi + 1, y = doubling_times_7[c][lvi], s = c)
-plt.savefig('covid_doubling_times_7.png')
-```
-
-```python
-ax = doubling_times[COUNTRIES_CORE].plot(figsize=(10, 6), title="Doubling times, 4 day average")
-for c in COUNTRIES_CORE:
- lvi = doubling_times[c].last_valid_index()
- ax.text(x = lvi + 1, y = doubling_times[c][lvi], s = c)
-plt.savefig('covid_doubling_times.png')
-```
-
-```python
-ax = doubling_times[COUNTRIES_FRIENDS].plot(figsize=(10, 6), title="Doubling times")
-for c in COUNTRIES_FRIENDS:
- lvi = doubling_times[c].last_valid_index()
- ax.text(x = lvi + 1, y = doubling_times[c][lvi], s = c)
-plt.savefig('covid_doubling_times_friends.png')
-```
-
-```python
-ax = doubling_times[C7s].plot(figsize=(10, 6), title="Doubling times")
-for c in C7s:
- lvi = doubling_times[c].last_valid_index()
- ax.text(x = lvi + 1, y = doubling_times[c][lvi], s = c)
-# plt.savefig('covid_doubling_times_friends.png')
-```
-
-```python
-# deaths_diff_m4 = (data_since_threshold.loc[(slice(None), COUNTRIES_ALL), ['deaths_diff_m4']]
-# .unstack().sort_index().xs('deaths_diff_m4', axis=1, drop_level=True))
-```
-
-```python
-# deaths_diff_m7 = (data_since_threshold.loc[(slice(None), COUNTRIES_ALL), ['deaths_diff_m7']]
-# .unstack().sort_index().xs('deaths_diff_m7', axis=1, drop_level=True))
-```
-
-```python
-# deaths_diff_m7
-```
-
-```python
-# data_since_threshold.replace([np.inf, -np.inf], np.nan).groupby(level=1).last().loc[COUNTRIES_ALL]#, [doubling_time]]
-```
-
-```python
-dstl = data_since_threshold.replace([np.inf, -np.inf], np.nan).groupby(level=1).last()
-dstl.loc[dstl.index.intersection(COUNTRIES_ALL)]
-```
-
-```python
-# data_since_threshold.replace([np.inf, -np.inf], np.nan).groupby(level=1).last().loc[['UK', 'DE', 'IT']]#, [doubling_time]]
-dstl.loc[['UK', 'DE', 'IT', 'FR', 'ES']]
-```
-
-```python
-data_since_threshold.loc[(slice(None), ['UK']), :].tail(20)
-```
-
-```python
-data_since_threshold.loc[(slice(None), ['ES']), :].tail(20)
-```
-
-## Death projections
-
-```python
-data_since_threshold.loc[(slice(None), ['UK']), :].tail(15)
-```
-
-```python
-it_since_threshold = data_since_threshold.replace([np.inf, -np.inf], np.nan).loc[(slice(None), ['IT']), :]
-s_end = it_since_threshold.index.max()[0]
-s_end
-```
-
-```python
-uk_projection = data_since_threshold.replace([np.inf, -np.inf], np.nan).loc[(slice(None), ['UK']), :]
-uk_current_end = uk_projection.index.max()[0] + 1
-# s_start = uk_projection.index.max()[0] + 1
-uk_current_end
-```
-
-```python
-current_uk_deaths_m7 = uk_projection[uk_projection.deaths_m7 >= 0].iloc[-1].deaths_m7
-current_uk_deaths_m7
-```
-
-```python
-it_since_threshold[it_since_threshold.deaths_m7 <= current_uk_deaths_m7].loc[60:].first_valid_index()[0]
-```
-
-```python
-s_start = it_since_threshold[it_since_threshold.deaths_m7 <= current_uk_deaths_m7].loc[60:].first_valid_index()[0]
-s_start
-```
-
-```python
-s_start_date = data_since_threshold.loc[(89, 'IT'), 'dateRep']# .iloc[0]
-s_start_date
-```
-
-```python
-s_end - s_start
-```
-
-```python
-uk_end = s_end - s_start + uk_current_end
-uk_end
-```
-
-```python
-proj = it_since_threshold.loc[(slice(s_start, s_end), slice(None)), ['cases', 'deaths', 'deaths_m7']]
-ndiff = uk_current_end - s_start
-proj.index = pd.MultiIndex.from_tuples([(n + ndiff, 'UK') for n, _ in proj.index], names=proj.index.names)
-proj
-```
-
-```python
-it_since_threshold.loc[(slice(s_start - 8, s_start + 2), slice(None)), ['cases', 'deaths', 'deaths_m7']]
-```
-
-```python
-uk_projection[['cases', 'deaths', 'deaths_m7']].tail()
-```
-
-```python
-# proj['deaths_m7'] = proj['deaths_m7'] + 20
-# proj
-```
-
-Projected deaths, UK following IT trend from now.
-
-```python
-uk_projection = uk_projection.append(proj, sort=True)
-uk_projection.deaths.sum()
-```
-
-```python
-uk_projection = uk_projection.droplevel(1)
-uk_projection
-```
-
-```python
-uk_projection.loc[152, 'deaths']
-```
-
-## Correction for cumulative deaths correction on 14 August
-
-```python
-# uk_projection.loc[152, 'deaths'] = 50
-```
-
-```python
-uk_projection['deaths_m7'] = uk_projection['deaths'].transform(lambda x: x.rolling(7, 1).mean())
-uk_projection.loc[(uk_current_end - 20):(uk_current_end + 5)]
-```
-
-```python
-uk_projection.loc[(uk_current_end - 5):]
-```
-
-```python
-uk_projection.deaths_m7.plot()
-```
-
-```python
-proj.droplevel(level=1)
-```
-
-```python
-ax = deaths_m7[COUNTRIES_CORE].plot()
-# uk_projection['deaths_m7'].plot(figsize=(10, 6), title="Deaths per day, 7 day moving average", label="Projection", style='--', ax=ax)
-proj.droplevel(level=1)['deaths_m7'].plot(figsize=(10, 6), title="Deaths per day, 7 day moving average", label="Projection", style='--', ax=ax)
-ax.set_xlabel(f"Days since {DEATH_COUNT_THRESHOLD} deaths")
-for c in COUNTRIES_CORE:
- lvi = deaths_m7[c].last_valid_index()
- ax.text(x = lvi + 1, y = deaths_m7[c][lvi], s = c)
-plt.savefig('covid_deaths_per_day_7.png')
-```
-
-```python
-it_since_threshold.deaths.sum()
-```
-
-# Excess deaths calculation
-
-```python
-with open('excess_deaths.json') as f:
- excess_deaths_data = json.load(f)
-
-with open('excess_death_accuracy.json') as f:
- excess_death_accuracy = json.load(f)
-
-excess_deaths_data, excess_death_accuracy
-```
-
-```python
-# excess_deaths_upto = '2020-05-08'
-# excess_deaths = 54500
-```
-
-```python
-excess_deaths_upto = excess_deaths_data['end_date']
-excess_deaths = excess_deaths_data['excess_deaths']
-```
-
-Recorded deaths in period where ONS has reported total deaths
-
-```python
-ons_reported_deaths = base_data.loc['UK'][:excess_deaths_upto]['deaths'].sum()
-ons_reported_deaths
-```
-
-```python
-excess_deaths_upto
-```
-
-## Correction for deaths total correction on 14 August
-
-```python
-ons_unreported_deaths_data = base_data.loc['UK'][excess_deaths_upto:].iloc[1:]['deaths']
-# ons_unreported_deaths_data['2020-08-14'] = 50
-```
-
-```python
-ons_unreported_deaths = ons_unreported_deaths_data.sum()
-ons_unreported_deaths
-```
-
-```python
-scaled_ons_unreported_deaths = ons_unreported_deaths * excess_death_accuracy
-scaled_ons_unreported_deaths
-```
-
-```python
-uk_deaths_to_date = excess_deaths + scaled_ons_unreported_deaths
-uk_deaths_to_date
-```
-
-```python
-# data_since_threshold.loc[(slice(None), 'UK'), :][data_since_threshold.dateRep == excess_deaths_data['end_date']]
-```
-
-```python
-data_since_threshold[data_since_threshold.dateRep == excess_deaths_data['end_date']].loc[(slice(None), 'UK'), :]
-```
-
-```python
-ons_unreported_start = data_since_threshold[data_since_threshold.dateRep == excess_deaths_data['end_date']].loc[(slice(None), 'UK'), :].first_valid_index()[0] + 1
-ons_unreported_start
-```
-
-```python
-unreported_projected_deaths = uk_projection.loc[ons_unreported_start:].deaths.sum()
-unreported_projected_deaths
-```
-
-```python
-unreported_projected_deaths_scaled = unreported_projected_deaths * excess_death_accuracy
-unreported_projected_deaths_scaled
-```
-
-```python
-uk_projection.loc[(s_start):].deaths.sum()
-```
-
-```python
-deaths_actual_projected_scaled = uk_deaths_to_date + uk_projection.loc[(s_start):].deaths.sum() * excess_death_accuracy
-deaths_actual_projected_scaled
-```
-
-```python
-# excess_deaths / reported_deaths
-```
-
-True deaths to date, if we follow the scaling of excess deaths over reported deaths so far.
-
-```python
-uk_covid_deaths = data_since_threshold.replace([np.inf, -np.inf], np.nan).loc[(slice(None), ['UK']), 'deaths_culm'].iloc[-1]
-uk_covid_deaths
-```
-
-```python
-# uk_covid_deaths_scaled = excess_deaths + unreported_deaths * excess_death_accuracy
-# uk_covid_deaths_scaled
-```
-
-```python
-# data_since_threshold.replace([np.inf, -np.inf], np.nan).loc[(slice(None), ['IT']), 'dateRep'].iloc[-1] + pd.Timedelta(s_end - s_start, unit='days')
-```
-
-```python
-# data_since_threshold.replace([np.inf, -np.inf], np.nan).loc[(slice(None), ['UK']), 'dateRep'].iloc[-1].strftime("%Y-%m-%d")
-```
-
-```python
-# uk_covid_deaths * excess_deaths / reported_deaths
-```
-
-```python
-# uk_projection.deaths.sum() * excess_deaths / reported_deaths
-```
-
-```python
-# data_since_threshold.loc[(slice(None), 'FR'), :]
-# data_since_threshold[data_since_threshold.dateRep == '2020-05-18'].loc[(slice(None), 'FR'), :]
-```
-
-## School reopenings
-
-```python
-school_reopenings = {
- 'ES': {'date': '2020-05-18'},
- 'FR': {'date': '2020-05-18'}, # some areas only
-# 'IT': {'date': '2020-09-01'},
- # 'IE': {'date': '2020-09-01'},
- 'DE': {'date': '2020-05-04'},
- 'UK': {'date': '2020-06-01'}
-}
-```
-
-```python
-data_since_threshold[data_since_threshold.dateRep == '2020-05-04'].loc[(slice(None), ['DE']), :].first_valid_index()
-```
-
-```python
-data_since_threshold[data_since_threshold.dateRep == '2020-05-04'].loc[(slice(None), ['DE']), :].iloc[0].deaths_m7
-```
-
-```python
-for cID in school_reopenings:
- dst_in = data_since_threshold[data_since_threshold.dateRep == (school_reopenings[cID]['date'])].loc[(slice(None), [cID]), :]
- dst_i = dst_in.first_valid_index()
- dst_n = dst_in.iloc[0].deaths_m7
- school_reopenings[cID]['since_threshold'] = dst_i[0]
- school_reopenings[cID]['deaths_m7'] = dst_n
-school_reopenings
-```
-
-```python
-ax = deaths_m7[COUNTRIES_CORE].plot(figsize=(15, 9), title="Deaths per day, 7 day moving average")
-# uk_projection.deaths_m7.plot(ax=ax)
-for c in COUNTRIES_CORE:
- lvi = deaths_m7[c].last_valid_index()
- ax.text(x = lvi + 1, y = deaths_m7[c][lvi], s = f"{c}: {deaths_m7[c][lvi]:.0f}")
- if c in school_reopenings:
- marker_col = [l for l in ax.lines if l.get_label() == c][0].get_color()
- ax.plot(school_reopenings[c]['since_threshold'], school_reopenings[c]['deaths_m7'], '*',
- markersize=18, markerfacecolor=marker_col, markeredgecolor=marker_col)
- ax.text(x = school_reopenings[c]['since_threshold'] + 1, y = school_reopenings[c]['deaths_m7'],
- s = f"{school_reopenings[c]['date']}: {school_reopenings[c]['deaths_m7']:.0f}")
-plt.savefig('school_reopenings.png')
-```
-
-```python
-# ax = deaths_m7[COUNTRIES_CORE].plot(figsize=(15, 9), title="Deaths per day, 7 day moving average",
-# xlim=(46, 91), ylim=(0, 400))
-# # uk_projection.deaths_m7.plot(ax=ax)
-# for c in COUNTRIES_CORE:
-# lvi = deaths_m7[c].last_valid_index()
-# ax.text(x = lvi + 1, y = deaths_m7[c][lvi], s = f"{c}: {deaths_m7[c][lvi]:.0f}", fontsize=14)
-# if c in school_reopenings:
-# marker_col = [l for l in ax.lines if l.get_label() == c][0].get_color()
-# ax.plot(school_reopenings[c]['since_threshold'], school_reopenings[c]['deaths_m7'], '*',
-# markersize=18, markerfacecolor=marker_col, markeredgecolor=marker_col)
-# ax.text(x = school_reopenings[c]['since_threshold'] + 1, y = school_reopenings[c]['deaths_m7'],
-# s = f"{school_reopenings[c]['date']}: {school_reopenings[c]['deaths_m7']:.0f}",
-# fontsize=14)
-# plt.savefig('school_reopenings_detail.png')
-```
-
-# Lockdown graphs
-
-```python
-lockdown_dates = {
- 'ES': { 'part_start': {'date': '2020-03-14'}
- , 'full_start': {'date': '2020-03-15'}
- , 'part_finish': {'date': '2020-05-18'}
- },
- 'FR': { 'part_start': {'date': '2020-03-13'}
- , 'full_start': {'date': '2020-03-17'}
- , 'part_finish': {'date': '2020-05-11'}
- },
- 'IT': { 'part_start': {'date': '2020-03-08'}
- , 'full_start': {'date': '2020-03-10'}
- , 'part_finish': {'date': '2020-05-04'}
- },
- 'DE': { #'part_start': {'date': '2020-03-13'}
- 'full_start': {'date': '2020-03-22'}
- , 'part_finish': {'date': '2020-05-06'}
- },
- 'UK': { 'part_start': {'date': '2020-03-23'}
- , 'full_start': {'date': '2020-03-23'}
- , 'part_finish': {'date': '2020-05-31'}
- },
- 'IE': { #'part_start': {'date': '2020-03-12'}
- 'full_start': {'date': '2020-03-27'}
- , 'part_finish': {'date': '2020-05-18'}
- },
-}
-```
-
-```python
-for cID in lockdown_dates:
- for phase in lockdown_dates[cID]:
- dst_in = data_since_threshold[data_since_threshold.dateRep == (lockdown_dates[cID][phase]['date'])].loc[(slice(None), [cID]), :]
- dst_i = dst_in.first_valid_index()
- dst_n = dst_in.iloc[0].deaths_m7
- dst_c = dst_in.iloc[0].cases_m7
- lockdown_dates[cID][phase]['since_threshold'] = dst_i[0]
- lockdown_dates[cID][phase]['deaths_m7'] = dst_n
- lockdown_dates[cID][phase]['cases_m7'] = dst_c
-
-lockdown_dates
-```
-
-```python
-ax = deaths_m7[COUNTRIES_CORE].plot(figsize=(15, 9), title="Deaths per day, 7 day moving averagee, with lockdown dates")
-ax.set_xlabel(f"Days since {DEATH_COUNT_THRESHOLD} deaths")
-# uk_projection.deaths_m7.plot(ax=ax)
-for c in COUNTRIES_CORE:
- lvi = deaths_m7[c].last_valid_index()
- if c != 'UK':
- ax.text(x = lvi + 1, y = deaths_m7[c][lvi], s = f"{c}: {deaths_m7[c][lvi]:.0f}")
- if c in lockdown_dates:
- for phase in lockdown_dates[c]:
- marker_col = [l for l in ax.lines if l.get_label() == c][0].get_color()
- ax.plot(lockdown_dates[c][phase]['since_threshold'], lockdown_dates[c][phase]['deaths_m7'], '*',
- markersize=18, markerfacecolor=marker_col, markeredgecolor=marker_col)
- if 'start' not in phase:
- ax.text(x = lockdown_dates[c][phase]['since_threshold'] + 1, y = lockdown_dates[c][phase]['deaths_m7'],
- s = f"{lockdown_dates[c][phase]['date']}: {lockdown_dates[c][phase]['deaths_m7']:.0f}")
-# plt.savefig('school_reopenings.png')
-```
-
-```python
-ax = cases_m7.iloc[-50:][COUNTRIES_CORE].plot(figsize=(15, 9), title="Cases per day, 7 day moving average, with lockdown dates") #, ylim=(-10, 1500))
-ax.set_xlabel(f"Days since {DEATH_COUNT_THRESHOLD} deaths")
-# uk_projection.deaths_m7.plot(ax=ax)
-for c in COUNTRIES_CORE:
- lvi = cases_m7[c].last_valid_index()
-# if c != 'UK':
- ax.text(x = lvi + 1, y = cases_m7[c][lvi], s = f"{c}: {cases_m7[c][lvi]:.0f}")
-
-```
-
-```python
-ax = cases_m7[COUNTRIES_CORE].plot(figsize=(15, 9), title="Cases per day, 7 day moving average, with lockdown dates")
-ax.set_xlabel(f"Days since {DEATH_COUNT_THRESHOLD} deaths")
-# uk_projection.deaths_m7.plot(ax=ax)
-for c in COUNTRIES_CORE:
- lvi = cases_m7[c].last_valid_index()
-# if c != 'UK':
- ax.text(x = lvi + 1, y = cases_m7[c][lvi], s = f"{c}: {cases_m7[c][lvi]:.0f}")
- if c in lockdown_dates:
- for phase in lockdown_dates[c]:
- marker_col = [l for l in ax.lines if l.get_label() == c][0].get_color()
- if 'start' in phase:
- marker_shape = '^'
- else:
- marker_shape = 'v'
- ax.plot(lockdown_dates[c][phase]['since_threshold'], lockdown_dates[c][phase]['cases_m7'],
- marker_shape,
- markersize=18, markerfacecolor=marker_col, markeredgecolor=marker_col)
- if 'start' not in phase:
- ax.text(x = lockdown_dates[c][phase]['since_threshold'] + 1, y = lockdown_dates[c][phase]['cases_m7'],
- s = f"{lockdown_dates[c][phase]['date']}: {lockdown_dates[c][phase]['cases_m7']:.0f}")
-plt.savefig('cases_per_day_with_lockdown.png')
-```
-
-```python
-ax = cases_m7[COUNTRIES_CORE].plot(figsize=(10, 6), title="Cases per day, 7 day moving average")
-for c in COUNTRIES_CORE:
- lvi = cases_m7[c].last_valid_index()
- ax.text(x = lvi + 1, y = cases_m7[c][lvi], s = c)
-plt.savefig('covid_cases_per_day-core.png')
-```
-
-```python
-ax = deaths_m7[COUNTRIES_CORE].plot(figsize=(15, 9), title="Deaths per day, 7 day moving average",
- xlim=(0, 15),
- ylim=(0, 66)
- )
-# uk_projection.deaths_m7.plot(ax=ax)
-for c in COUNTRIES_CORE:
- lvi = deaths_m7[c].last_valid_index()
- if c in lockdown_dates:
- for phase in lockdown_dates[c]:
- if 'start' in phase:
- print(c, phase)
- marker_col = [l for l in ax.lines if l.get_label() == c][0].get_color()
- ax.plot(lockdown_dates[c][phase]['since_threshold'], lockdown_dates[c][phase]['deaths_m7'], '*',
- markersize=18, markerfacecolor=marker_col, markeredgecolor=marker_col)
- ax.text(x = lockdown_dates[c][phase]['since_threshold'] + 0.3, y = lockdown_dates[c][phase]['deaths_m7'],
- s = f"{lockdown_dates[c][phase]['date']}: {lockdown_dates[c][phase]['deaths_m7']:.0f}")
-# plt.savefig('school_reopenings.png')
-```
-
-```python
-
-```
-
-```python
-
-```
-
-# Write results to summary file
-
-```python
-with open('covid_summary.md', 'w') as f:
- f.write('% Covid death data summary\n')
- f.write('% Neil Smith\n')
- f.write(f'% Created on {datetime.datetime.now().strftime("%Y-%m-%d")}\n')
- f.write('\n')
-
- last_uk_date = data_since_threshold.replace([np.inf, -np.inf], np.nan).loc[(slice(None), ['UK']), 'dateRep'].iloc[-1]
- f.write(f'> Last UK data from {last_uk_date.strftime("%Y-%m-%d")}\n')
-```
-
-```python
-with open('covid_summary.md', 'a') as f:
- f.write('\n')
- f.write('## Headlines\n')
- f.write('\n')
- f.write('| []() | |\n')
- f.write('|:---|---:|\n')
- f.write(f'| Deaths reported so far | {uk_covid_deaths} | \n')
- f.write(f'| Total Covid deaths to date (estimated) | {uk_deaths_to_date:.0f} |\n')
- projection_date = data_since_threshold.replace([np.inf, -np.inf], np.nan).loc[(slice(None), ['IT']), 'dateRep'].iloc[-1] + pd.Timedelta(s_end - s_start, unit='days')
- f.write(f'| Projected total deaths up to {projection_date.strftime("%Y-%m-%d")} | {deaths_actual_projected_scaled:.0f} | \n')
- f.write('\n')
-```
-
-```python
-with open('covid_summary.md', 'a') as f:
- f.write('\n')
- f.write('## Total deaths\n')
- f.write(f'Time based on days since {DEATH_COUNT_THRESHOLD} deaths\n')
- f.write('\n')
- f.write('\n')
- f.write('\n')
- f.write('| Country ID | Country name | Total deaths |\n')
- f.write('|:-----------|:-------------|-------------:|\n')
- for c in sorted(COUNTRIES_CORE):
- lvi = deaths[c].last_valid_index()
- f.write(f'| {c} | {countries.loc[c].countriesAndTerritories} | {int(deaths[c][lvi])} |\n')
- f.write('\n')
-```
-
-```python
-with open('covid_summary.md', 'a') as f:
- f.write('\n')
- f.write('## All-causes deaths, UK\n')
- f.write('\n')
- f.write('\n')
- f.write('\n')
- f.write('### Excess deaths\n')
- f.write('\n')
- f.write(f'From week ending 20 March 2020 until week ending {pd.to_datetime(excess_deaths_upto).strftime("%d %B %Y")}, ')
- f.write(f'there were approximately **{excess_deaths:.0f}** excess deaths, over the average for the previous five years.\n')
- f.write('\n')
-```
-
-```python
-with open('covid_summary.md', 'a') as f:
- f.write(f'In that period, the UK reported {ons_reported_deaths} Covid deaths.\n')
- f.write(f'In the last three weeks for which excess deaths have been reported, the excess deaths have been {excess_death_accuracy:.3f} higher than the Covid-reported deaths.\n')
-# f.write(f'That means the actual number of Covid death is about {excess_deaths / reported_deaths:.2f} times higher than the reported figures.\n')
- f.write('\n')
- f.write(f'The UK has reported {uk_covid_deaths} deaths so far.\n')
- f.write(f'Using the scaling factor above (for Covid-19 deaths after the ONS figures), I infer that there have been **{uk_deaths_to_date:.0f}** total deaths so far.\n')
- f.write('\n')
-```
-
-```python
-with open('covid_summary.md', 'a') as f:
- f.write('\n')
- f.write('## Deaths per day\n')
- f.write(f'Based on a 7-day moving average\n')
- f.write('\n')
- f.write('\n')
- f.write('\n')
-```
-
-```python
-s_end - s_start - 1
-```
-
-```python
-with open('covid_summary.md', 'a') as f:
- f.write('\n')
- f.write('## Projected deaths\n')
- f.write(f"The UK's daily deaths data is very similar to Italy's.\n")
- f.write(f'If I use the Italian data for the next {s_end - s_start - 1} days (from {s_start_date.strftime("%d %B %Y")} onwards),')
- f.write(f' the UK will report {uk_projection.deaths.sum()} deaths on day {uk_end} of the epidemic.\n')
- f.write('\n')
- f.write('Using the excess deaths scaling from above, that will translate into ')
- f.write(f'**{deaths_actual_projected_scaled:.0f}** Covid deaths total.\n')
- f.write('\n')
-```
-
-```python
-with open('covid_summary.md', 'a') as f:
- f.write('\n')
- f.write('## Deaths doubling times\n')
- f.write(f'Based on a 7-day moving average\n')
- f.write('\n')
- f.write('\n')
- f.write('\n')
-```
-
-```python
-with open('covid_summary.md', 'a') as f:
- f.write('\n')
- f.write('## Cases per day and lockdown dates\n')
- f.write(f'Based on a 7-day moving average\n')
- f.write('\n')
- f.write('\n')
- f.write('\n')
-```
-
-```python
-with open('covid_summary.md', 'a') as f:
- f.write('\n')
- f.write('| Country ID | Country name | Most recent daily cases | Most recent daily deaths |\n')
- f.write('|:-----------|:-------------|------------------------:|-------------------------:|\n')
- for c in sorted(COUNTRIES_CORE):
- lvic = cases_m7[c].last_valid_index()
- lvid = deaths_m7[c].last_valid_index()
- f.write(f'| {c} | {countries.loc[c].countriesAndTerritories} | {cases_m7[c][lvic]:.0f} | {deaths_m7[c][lvid]:.0f} | \n')
- f.write('\n')
- f.write('(Figures are 7-day averages)\n')
- f.write('\n')
-```
-
-```python
-with open('covid_summary.md', 'a') as f:
- f.write('# Data sources\n')
- f.write('\n')
- f.write('> Covid data from [European Centre for Disease Prevention and Control](https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide)\n')
- f.write('\n')
- f.write("""> Population data from:
-
-* [Office of National Statistics](https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/weeklyprovisionalfiguresondeathsregisteredinenglandandwales) (Endland and Wales) Weeks start on a Saturday.
-* [Northern Ireland Statistics and Research Agency](https://www.nisra.gov.uk/publications/weekly-deaths) (Northern Ireland). Weeks start on a Saturday. Note that the week numbers don't match the England and Wales data.
-* [National Records of Scotland](https://www.nrscotland.gov.uk/statistics-and-data/statistics/statistics-by-theme/vital-events/general-publications/weekly-and-monthly-data-on-births-and-deaths/weekly-data-on-births-and-deaths) (Scotland). Note that Scotland uses ISO8601 week numbers, which start on a Monday.""")
-
- f.write('\n\n')
- f.write('> [Source code available](https://git.njae.me.uk/?p=covid19.git;a=tree)\n')
- f.write('\n')
-
-```
-
-```python
-!pandoc -s covid_summary.md > covid_summary.html
-```
-
-```python
-!scp covid_summary.html neil@ogedei:/var/www/scripts.njae.me.uk/covid/index.html
-!scp covid_deaths_total_linear.png neil@ogedei:/var/www/scripts.njae.me.uk/covid/
-!scp deaths-radar.png neil@ogedei:/var/www/scripts.njae.me.uk/covid/
-!scp covid_deaths_per_day_7.png neil@ogedei:/var/www/scripts.njae.me.uk/covid/
-!scp covid_doubling_times_7.png neil@ogedei:/var/www/scripts.njae.me.uk/covid/
-!scp cases_per_day_with_lockdown.png neil@ogedei:/var/www/scripts.njae.me.uk/covid/
-```
-
-```python
-with open('uk_covid_deaths.js', 'w') as f:
- f.write(f"document.write('{uk_covid_deaths}');")
-
-with open('estimated_total_deaths.js', 'w') as f:
- f.write(f"document.write('{uk_deaths_to_date:.0f}');")
-
-with open('projection_date.js', 'w') as f:
- f.write(f"document.write(\'{projection_date.strftime('%d %B %Y')}\');")
-
-with open('projected_deaths.js', 'w') as f:
- f.write(f"document.write('{uk_projection.deaths.sum():.0f}');")
-
-with open('projected_excess_deaths.js', 'w') as f:
- f.write(f"document.write('{deaths_actual_projected_scaled:.0f}');")
-
-with open('excess_deaths_upto.js', 'w') as f:
- f.write(f"document.write('{pd.to_datetime(excess_deaths_upto).strftime('%d %B %Y')}');")
-
-with open('excess_deaths.js', 'w') as f:
- f.write(f"document.write('{excess_deaths:.0f}');")
-
-with open('reported_deaths.js', 'w') as f:
- f.write(f"document.write('{ons_reported_deaths:.0f}');")
-
-with open('scaling_factor.js', 'w') as f:
- f.write(f"document.write('{excess_death_accuracy:.2f}');")
-
-with open('projection_length.js', 'w') as f:
- f.write(f"document.write('{s_end - s_start - 1}');")
-
-with open('s_end.js', 'w') as f:
- f.write(f"document.write('{s_end}');")
-
-s_start_date_str = s_start_date.strftime("%d %B %Y")
-with open('s_start_date.js', 'w') as f:
- f.write(f"document.write('{s_start_date_str}');")
-
-with open('uk_end.js', 'w') as f:
- f.write(f"document.write('{uk_end}');")
-
-with open('last_uk_date.js', 'w') as f:
- f.write(f"document.write('{pd.to_datetime(last_uk_date).strftime('%d %B %Y')}');")
-```
-
-```python
-pd.to_datetime(excess_deaths_upto).strftime('%d %B %Y')
-```
-
-```python
-!scp uk_covid_deaths.js neil@ogedei:/var/www/scripts.njae.me.uk/covid/
-!scp estimated_total_deaths.js neil@ogedei:/var/www/scripts.njae.me.uk/covid/
-!scp projection_date.js neil@ogedei:/var/www/scripts.njae.me.uk/covid/
-!scp projected_deaths.js neil@ogedei:/var/www/scripts.njae.me.uk/covid/
-!scp projected_excess_deaths.js neil@ogedei:/var/www/scripts.njae.me.uk/covid/
-!scp excess_deaths_upto.js neil@ogedei:/var/www/scripts.njae.me.uk/covid/
-!scp excess_deaths.js neil@ogedei:/var/www/scripts.njae.me.uk/covid/
-!scp reported_deaths.js neil@ogedei:/var/www/scripts.njae.me.uk/covid/
-!scp scaling_factor.js neil@ogedei:/var/www/scripts.njae.me.uk/covid/
-!scp projection_length.js neil@ogedei:/var/www/scripts.njae.me.uk/covid/
-!scp s_end.js neil@ogedei:/var/www/scripts.njae.me.uk/covid/
-!scp s_start_date.js neil@ogedei:/var/www/scripts.njae.me.uk/covid/
-!scp uk_end.js neil@ogedei:/var/www/scripts.njae.me.uk/covid/
-!scp last_uk_date.js neil@ogedei:/var/www/scripts.njae.me.uk/covid/
-```
-
-```python
-data_by_date.loc['UK'].to_csv('data_by_day_uk.csv', header=True, index=True)
-```
-
-```python
-ukd = data_since_threshold.replace([np.inf, -np.inf], np.nan).loc[(slice(None), ['UK']), ['deaths', 'deaths_m7']].droplevel(1)
-ax = ukd.deaths.plot.bar(figsize=(12, 8))
-ukd.deaths_m7.plot.line(ax=ax, color='red')
-# ax = data_since_threshold.replace([np.inf, -np.inf], np.nan).loc[(slice(None), ['UK']), 'deaths_m7'].plot.line(figsize=(12, 8), color='red')
-# ax = data_since_threshold.replace([np.inf, -np.inf], np.nan).loc[(slice(None), ['UK']), 'deaths'].plot.bar(ax=ax)
-ax.set_xticks(range(0, 120, 20))
-```
-
-```python
-np.arange(0, 130, 20)
-```
-
-```python
-data_by_date.loc['UK']
-```
-
-```python
-data_by_date.loc['UK'].plot(x='deaths_culm', y='deaths', logx=True, logy=True)
-```
-
-```python
-data_by_date.loc['UK'].plot(x='cases_culm', y='cases')
-```
-
-```python
-ukdbd = data_by_date.loc['UK'].copy()
-ukdbd['deaths_m7'] = ukdbd.deaths.transform(lambda x: x.rolling(7, 1).mean())
-ukdbd['cases_m7'] = ukdbd.cases.transform(lambda x: x.rolling(7, 1).mean())
-ukdbd
-```
-
-```python
-ukdbd.plot(x='deaths_culm', y='deaths_m7', logx=True, logy=True)
-```
-
-```python
-fig, ax = plt.subplots(figsize=(12, 8))
-xmax = 10
-for c in COUNTRIES_CORE:
- if data_since_threshold.loc[(slice(None), c), 'deaths_culm'].max() > xmax:
- xmax = data_since_threshold.loc[(slice(None), c), 'deaths_culm'].max()
- data_since_threshold.loc[(slice(None), c), :].plot(x='deaths_culm', y='deaths_m7', logx=True, logy=True, xlim=(10, xmax * 1.1), label=c, ax=ax)
-```
-
-```python
-data_since_threshold.loc[(slice(None), 'UK'), 'deaths_culm'].max()
-```
-
-```python
-countries.continentExp.unique()
-```
-
-```python
-countries.loc['KW']
-```
-
-```python
-data_by_date.groupby(level=0)['deaths'].shift(-25)
-```
-
-```python
-offset_data = data_by_date.loc[:, ['cases']]
-offset_data['deaths'] = data_by_date.groupby(level=0)['deaths'].shift(-25)
-offset_data['cases_m7'] = offset_data.groupby(level=0)['cases'].transform(lambda x: x.rolling(7, 1).mean())
-offset_data['deaths_m7'] = offset_data['deaths'].dropna().groupby(level=0).transform(lambda x: x.rolling(7, 1).mean())
-offset_data['deaths_per_case'] = offset_data.deaths_m7 / offset_data.cases_m7
-offset_data
-```
-
-```python
-deaths_m7
-```
-
-```python
-offset_deaths_m7 = (offset_data.loc[COUNTRIES_ALL, ['deaths_m7']]
- .unstack().sort_index().xs('deaths_m7', axis=1, drop_level=True)).T.sort_index()
-offset_deaths_m7
-```
-
-```python
-offset_deaths_m7['UK']
-```
-
-```python
-data_since_threshold.loc[(slice(None), 'UK'), :].tail()
-```
-
-```python
-countries.loc['PT']
-```
-
-```python
-ax = cases_m7.iloc[-50:][COUNTRIES_FRIENDS].plot(figsize=(15, 9), title="Cases per day, 7 day moving average", ylim=(-10, 1500))
-ax.set_xlabel(f"Days since {DEATH_COUNT_THRESHOLD} deaths")
-# uk_projection.deaths_m7.plot(ax=ax)
-for c in COUNTRIES_FRIENDS:
- lvi = cases_m7[c].last_valid_index()
- if c != 'ES':
- ax.text(x = lvi + 1, y = cases_m7[c][lvi], s = f"{c}: {cases_m7[c][lvi]:.0f}")
-
-```
-
-```python
-ax = deaths_m7.iloc[-50:][COUNTRIES_FRIENDS].plot(figsize=(15, 9), title="Cases per day, 7 day moving average", ylim=(-10, 100))
-ax.set_xlabel(f"Days since {DEATH_COUNT_THRESHOLD} deaths")
-# uk_projection.deaths_m7.plot(ax=ax)
-for c in COUNTRIES_FRIENDS:
- lvi = deaths_m7[c].last_valid_index()
-# if c != 'ES':
- ax.text(x = lvi + 1, y = deaths_m7[c][lvi], s = f"{c}: {deaths_m7[c][lvi]:.0f}")
-
-```
-
-```python
-
-```
projects / covid19.git / blobdiff