In [10]:
import gc
import os
from pathlib import Path
import random
import sys
from tqdm.notebook import tqdm
import numpy as np
import pandas as pd
import scipy as sp
import matplotlib.pyplot as plt
import seaborn as sns
from IPython.core.display import display, HTML
# --- plotly ---
from plotly import tools, subplots
import plotly.offline as py
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
import plotly.express as px
import plotly.figure_factory as ff
import plotly.io as pio
pio.templates.default = "plotly_dark"
# --- models ---
from sklearn import preprocessing
from sklearn.model_selection import KFold
import lightgbm as lgb
import xgboost as xgb
import catboost as cb
# --- setup ---
pd.set_option('max_columns', 50)
--------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) <ipython-input-10-59cc00873ff8> in <module> 31 import lightgbm as lgb 32 import xgboost as xgb ---> 33 import catboost as cb 34 35 # --- setup --- ModuleNotFoundError: No module named 'catboost'
Load Data¶
In [11]:
# Input data files are available in the "../input/" directory.
import os
for dirname, _, filenames in os.walk('./'):
filenames.sort()
for filename in filenames:
print(os.path.join(dirname, filename))
./0001418189999fea7f7cbe3e82703d71c85a6fe5 (1).json ./0001418189999fea7f7cbe3e82703d71c85a6fe5.json ./00016663c74157a66b4d509d5c4edffd5391bbe0 (1).json ./00016663c74157a66b4d509d5c4edffd5391bbe0 (2).json ./00016663c74157a66b4d509d5c4edffd5391bbe0.json ./0005d253951fedc237715a37db147032eea28912.json ./000affa746a03f1fe4e3b3ef1a62fdfa9b9ac52a.json ./000b7d1517ceebb34e1e3e817695b6de03e2fa78.json ./000e754142ba65ef77c6fdffcbcbe824e141ea7b.json ./000eec3f1e93c3792454ac59415c928ce3a6b4ad.json ./001259ae6d9bfa9376894f61aa6b6c5f18be2177.json ./00142f93c18b07350be89e96372d240372437ed9.json ./0015023cc06b5362d332b3baf348d11567ca2fbb.json ./0016983545c217458b711d5aaad5fba583d81171.json ./001916486b2012ccc611fe18d4763f9e81517cbb.json ./001b8a3bcea5b3a810fd9d03a1bc51cf708fe399.json ./0022796bb2112abd2e6423ba2d57751db06049fb.json ./0031e47b76374e05a18c266bd1a1140e5eacb54f.json ./00326efcca0852dc6e39dc6b7786267e1bc4f194 (1).json ./00326efcca0852dc6e39dc6b7786267e1bc4f194.json ./00340eea543336d54adda18236424de6a5e91c9d.json ./00352a58c8766861effed18a4b079d1683fec2ec.json ./0036b28fddf7e93da0970303672934ea2f9944e7.json ./0043d044273b8eb1585d3a66061e9b4e03edc062.json ./0049ba8861864506e1e8559e7815f4de8b03dbed.json ./004f0f8bb66cf446678dc13cf2701feec4f36d76.json ./005c43980edf3fcc2a4d12ee7ad630ddb651ce6e.json ./00623bf2715e25d3acacb3f210d6888ed840e3cb.json ./00676f1131e03ca8defe523e79bc1635cc933909.json ./006be99e337c84b8758591a54f0362353b24dfde.json ./0072159e1ebecc889e9bcabb58bb45c47e18a403.json ./00a00d0edc750db4a0c299dd1ec0c6871f5a4f24.json ./00d16927588fb04d4be0e6b269fc02f0d3c2aa7b.json ./00d4ff0cfe5582b3f2746b2a33d67f6bd4307e98.json ./00e5a723d44eb9f2698c38b518eff85c00f9753b.json ./01297dffaf94c1314ca46088f7b829b8383c2f73.json ./0134717f1b5ea99fc7e5ac305d492f5cedc8cd92.json ./0139ea4ca580af99b602c6435368e7fdbefacb03.json ./013d9d1cba8a54d5d3718c229b812d7cf91b6c89.json ./013d9fb8719d3d3d47738f9f0604f3b643c4df57.json ./01d162d7fae6aaba8e6e60e563ef4c2fca7b0e18.json ./01e3b313e78a352593be2ff64927192af66619b5.json ./02201e4601ab0eb70b6c26480cf2bfeae2625193.json ./0255ea4b2f26a51a3bfa3bd8f3e1978c82c976d5.json ./API_SH.MED.BEDS.ZS_DS2_en_csv_v2_887506 (1).csv ./API_SH.MED.BEDS.ZS_DS2_en_csv_v2_887506.csv ./BTC LEt's DO iT.ipynb ./BTC Mk 2-Copy1.ipynb ./BTC Mk 2.ipynb ./BTC Mk1-Copy1.ipynb ./BTC Mk1.ipynb ./CLouds-Copy1.ipynb ./CLouds.ipynb ./COVID-19 April 11.ipynb ./COVID-19 April 3-Copy1.ipynb ./COVID-19 April 3.ipynb ./COVID-19-RNN.ipynb ./COVID19-Copy1.ipynb ./COVID19-Temperature.ipynb ./COVID19.ipynb ./COVID19_line_list_data (1).csv ./COVID19_line_list_data.csv ./COVID19_open_line_list.csv ./COVID19_open_line_list.csv (1).zip ./COVID19_open_line_list.csv.zip ./COVIDGlobalForecast.ipynb ./Coinbase_BTCUSD_1h.csv ./Coinbase_BTCUSD_d - Coinbase_BTCUSD_d.csv ./Coinbase_BTCUSD_d(2).csv ./Coinbase_BTCUSD_d.csv ./Coinbase_BTCUSD_day.csv ./Coinbase_ETHUSD_1h.csv ./Coinbase_ETHUSD_d.csv ./Life expectancy at birth.csv ./SIR_example.png ./Untitled.ipynb ./Untitled1.ipynb ./Untitled2.ipynb ./Untitled3.ipynb ./Untitled4.ipynb ./Untitled5.ipynb ./Untitled6.ipynb ./WEO.csv ./WEO.csv.zip ./WPP2019_PopulationByAgeSex_Medium.csv ./WPP2019_PopulationByAgeSex_Medium.csv.zip ./best_model.h5 ./bitcoin_price_d - Coinbase_BTCUSD_d.xls ./bitcoin_price_d.csv ./complete_data_new_format (1).csv ./complete_data_new_format.csv ./countries_icu_temp.csv ./covid19_italy_province.csv ./covid19_italy_region.csv ./covid19countryinfo.csv ./covid_19_clean_complete (1).csv ./covid_19_clean_complete (2).csv ./covid_19_clean_complete (3).csv ./covid_19_clean_complete (4).csv ./covid_19_clean_complete.csv ./covid_19_data (1).csv ./covid_19_data.csv ./crypto_tradinds.csv ./cumulative.csv ./enriched_covid_19.csv ./enriched_covid_19_week_2.csv ./exoTest.csv ./exoTrain.csv ./get-pip.py ./historical-data-on-the-trading-of-cryptocurrencies.zip ./json_schema.txt ./metadata.csv.zip ./population_by_country_2020 (1).csv ./population_by_country_2020.csv ./population_data.csv ./sample_submission.csv ./share-of-adults-who-smoke.csv ./submission (1).csv ./submission.csv ./temperature_dataframe.csv ./temperature_dataframe.csv (1).zip ./temperature_dataframe.csv.zip ./test (1).csv ./test.csv ./time_series_covid19_confirmed_global.csv ./time_series_covid19_deaths_global.csv ./time_series_covid19_recovered_global.csv ./time_series_covid_19_confirmed (1).csv ./time_series_covid_19_confirmed.csv ./time_series_covid_19_deaths (1).csv ./time_series_covid_19_deaths.csv ./time_series_covid_19_recovered (1).csv ./time_series_covid_19_recovered.csv ./train (1).csv ./train.csv ./us_covid19_daily.csv ./us_states_covid19_daily.csv ./usa_county_wise.csv.zip ./usa_states.csv ./usa_states2.csv ./.ipynb_checkpoints\BTC LEt's DO iT-checkpoint.ipynb ./.ipynb_checkpoints\BTC Mk 2-Copy1-checkpoint.ipynb ./.ipynb_checkpoints\BTC Mk 2-checkpoint.ipynb ./.ipynb_checkpoints\BTC Mk1-Copy1-checkpoint.ipynb ./.ipynb_checkpoints\BTC Mk1-checkpoint.ipynb ./.ipynb_checkpoints\CLouds-Copy1-checkpoint.ipynb ./.ipynb_checkpoints\CLouds-checkpoint.ipynb ./.ipynb_checkpoints\COVID-19 April 11-checkpoint.ipynb ./.ipynb_checkpoints\COVID-19 April 3-Copy1-checkpoint.ipynb ./.ipynb_checkpoints\COVID-19 April 3-checkpoint.ipynb ./.ipynb_checkpoints\COVID-19-RNN-checkpoint.ipynb ./.ipynb_checkpoints\COVID19-Copy1-checkpoint.ipynb ./.ipynb_checkpoints\COVID19-Temperature-checkpoint.ipynb ./.ipynb_checkpoints\COVID19-checkpoint.ipynb ./.ipynb_checkpoints\COVIDGlobalForecast-checkpoint.ipynb ./.ipynb_checkpoints\Untitled-checkpoint.ipynb ./.ipynb_checkpoints\Untitled1-checkpoint.ipynb ./.ipynb_checkpoints\Untitled2-checkpoint.ipynb ./.ipynb_checkpoints\Untitled3-checkpoint.ipynb ./.ipynb_checkpoints\Untitled4-checkpoint.ipynb ./.ipynb_checkpoints\Untitled5-checkpoint.ipynb ./.ipynb_checkpoints\Untitled6-checkpoint.ipynb ./2020prices\BTC Mk 2.ipynb ./2020prices\.ipynb_checkpoints\BTC Mk 2-checkpoint.ipynb ./input\BTC Mk 2.ipynb ./input\.ipynb_checkpoints\BTC Mk 2-checkpoint.ipynb ./logs\segmentation\log.txt ./logs\segmentation\checkpoints\train.1.exception_BrokenPipeError.pth ./logs\segmentation\checkpoints\train.1.exception_ValueError.pth ./logs\segmentation\train_log\events.out.tfevents.1580400959.SkyNet.13532.0 ./logs\segmentation\train_log\events.out.tfevents.1580401036.SkyNet.13532.1 ./logs\segmentation\train_log\events.out.tfevents.1580401194.SkyNet.13532.2 ./logs\segmentation\train_log\events.out.tfevents.1580401279.SkyNet.13532.3 ./logs\segmentation\train_log\events.out.tfevents.1580401294.SkyNet.13532.4 ./logs\segmentation\train_log\events.out.tfevents.1580401370.SkyNet.13532.5 ./logs\segmentation\train_log\events.out.tfevents.1580401381.SkyNet.13532.6 ./logs\segmentation\train_log\events.out.tfevents.1580401389.SkyNet.13532.7 ./logs\segmentation\train_log\events.out.tfevents.1580401427.SkyNet.13532.8 ./logs\segmentation\train_log\events.out.tfevents.1580401759.SkyNet.13532.9 ./logs\segmentation\train_log\events.out.tfevents.1580401768.SkyNet.13532.10 ./logs\segmentation\train_log\events.out.tfevents.1580401937.SkyNet.13532.11 ./logs\segmentation\train_log\events.out.tfevents.1580402007.SkyNet.13532.12 ./logs\segmentation\train_log\events.out.tfevents.1580402022.SkyNet.13532.13 ./logs\segmentation\train_log\events.out.tfevents.1580402092.SkyNet.13532.14 ./logs\segmentation\train_log\events.out.tfevents.1580402124.SkyNet.13532.15 ./logs\segmentation\train_log\events.out.tfevents.1580402130.SkyNet.13532.16 ./logs\segmentation\train_log\events.out.tfevents.1580402142.SkyNet.13532.17 ./logs\segmentation\train_log\events.out.tfevents.1580402152.SkyNet.13532.18 ./logs\segmentation\train_log\events.out.tfevents.1580402165.SkyNet.13532.19 ./logs\segmentation\train_log\events.out.tfevents.1580402687.SkyNet.13532.20 ./logs\segmentation\train_log\events.out.tfevents.1580402700.SkyNet.13532.21 ./logs\segmentation\train_log\events.out.tfevents.1580402930.SkyNet.13532.22 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In [12]:
%%time
datadir = Path('./week 4/')
# Read in the data CSV files
train = pd.read_csv(datadir/'train.csv')
test = pd.read_csv(datadir/'test.csv')
submission = pd.read_csv(datadir/'submission.csv')
Wall time: 34 ms
In [13]:
train
Out[13]:
| Id | Province_State | Country_Region | Date | ConfirmedCases | Fatalities | |
|---|---|---|---|---|---|---|
| 0 | 1 | NaN | Afghanistan | 2020-01-22 | 0.0 | 0.0 |
| 1 | 2 | NaN | Afghanistan | 2020-01-23 | 0.0 | 0.0 |
| 2 | 3 | NaN | Afghanistan | 2020-01-24 | 0.0 | 0.0 |
| 3 | 4 | NaN | Afghanistan | 2020-01-25 | 0.0 | 0.0 |
| 4 | 5 | NaN | Afghanistan | 2020-01-26 | 0.0 | 0.0 |
| ... | ... | ... | ... | ... | ... | ... |
| 25035 | 35644 | NaN | Zimbabwe | 2020-04-06 | 10.0 | 1.0 |
| 25036 | 35645 | NaN | Zimbabwe | 2020-04-07 | 11.0 | 2.0 |
| 25037 | 35646 | NaN | Zimbabwe | 2020-04-08 | 11.0 | 3.0 |
| 25038 | 35647 | NaN | Zimbabwe | 2020-04-09 | 11.0 | 3.0 |
| 25039 | 35648 | NaN | Zimbabwe | 2020-04-10 | 13.0 | 3.0 |
25040 rows × 6 columns
In [14]:
test
Out[14]:
| ForecastId | Province_State | Country_Region | Date | |
|---|---|---|---|---|
| 0 | 1 | NaN | Afghanistan | 2020-04-02 |
| 1 | 2 | NaN | Afghanistan | 2020-04-03 |
| 2 | 3 | NaN | Afghanistan | 2020-04-04 |
| 3 | 4 | NaN | Afghanistan | 2020-04-05 |
| 4 | 5 | NaN | Afghanistan | 2020-04-06 |
| ... | ... | ... | ... | ... |
| 13454 | 13455 | NaN | Zimbabwe | 2020-05-10 |
| 13455 | 13456 | NaN | Zimbabwe | 2020-05-11 |
| 13456 | 13457 | NaN | Zimbabwe | 2020-05-12 |
| 13457 | 13458 | NaN | Zimbabwe | 2020-05-13 |
| 13458 | 13459 | NaN | Zimbabwe | 2020-05-14 |
13459 rows × 4 columns
In [15]:
submission
Out[15]:
| ForecastId | ConfirmedCases | Fatalities | |
|---|---|---|---|
| 0 | 1 | 1 | 1 |
| 1 | 2 | 1 | 1 |
| 2 | 3 | 1 | 1 |
| 3 | 4 | 1 | 1 |
| 4 | 5 | 1 | 1 |
| ... | ... | ... | ... |
| 13454 | 13455 | 1 | 1 |
| 13455 | 13456 | 1 | 1 |
| 13456 | 13457 | 1 | 1 |
| 13457 | 13458 | 1 | 1 |
| 13458 | 13459 | 1 | 1 |
13459 rows × 3 columns
In [16]:
train.rename({'Country_Region': 'country', 'Province_State': 'province', 'Id': 'id', 'Date': 'date', 'ConfirmedCases': 'confirmed', 'Fatalities': 'fatalities'}, axis=1, inplace=True)
test.rename({'Country_Region': 'country', 'Province_State': 'province', 'Id': 'id', 'Date': 'date', 'ConfirmedCases': 'confirmed', 'Fatalities': 'fatalities'}, axis=1, inplace=True)
train['country_province'] = train['country'].fillna('') + '/' + train['province'].fillna('')
test['country_province'] = test['country'].fillna('') + '/' + test['province'].fillna('')
Worldwide Trend¶
In [17]:
ww_df = train.groupby('date')[['confirmed', 'fatalities']].sum().reset_index()
ww_df['new_case'] = ww_df['confirmed'] - ww_df['confirmed'].shift(1)
ww_df.tail()
Out[17]:
| date | confirmed | fatalities | new_case | |
|---|---|---|---|---|
| 75 | 2020-04-06 | 1344928.0 | 74560.0 | 72986.0 |
| 76 | 2020-04-07 | 1425923.0 | 81859.0 | 80995.0 |
| 77 | 2020-04-08 | 1510928.0 | 88332.0 | 85005.0 |
| 78 | 2020-04-09 | 1595174.0 | 95449.0 | 84246.0 |
| 79 | 2020-04-10 | 1691542.0 | 102519.0 | 96368.0 |
ww_melt_df = pd.melt(ww_df, id_vars=['date'], value_vars=['confirmed', 'fatalities', 'new_case']) ww_melt_df
In [18]:
ww_melt_df = pd.melt(ww_df, id_vars=['date'], value_vars=['confirmed', 'fatalities', 'new_case'])
ww_melt_df
Out[18]:
| date | variable | value | |
|---|---|---|---|
| 0 | 2020-01-22 | confirmed | 554.0 |
| 1 | 2020-01-23 | confirmed | 653.0 |
| 2 | 2020-01-24 | confirmed | 939.0 |
| 3 | 2020-01-25 | confirmed | 1432.0 |
| 4 | 2020-01-26 | confirmed | 2113.0 |
| ... | ... | ... | ... |
| 235 | 2020-04-06 | new_case | 72986.0 |
| 236 | 2020-04-07 | new_case | 80995.0 |
| 237 | 2020-04-08 | new_case | 85005.0 |
| 238 | 2020-04-09 | new_case | 84246.0 |
| 239 | 2020-04-10 | new_case | 96368.0 |
240 rows × 3 columns
Watch this exponential growth. It seems to be doubling each week.¶
the number almost doubled in last 1 week...¶
Confirmed cases reached 593K people, and 27K people already died on March 27.¶
Confirmed cases reached 1M people, and 52K people already died on April 2.¶
Good news! Daily new confirmed cases start to decrease from April 4, I hope this trend will continue..¶
In [19]:
fig = px.line(ww_melt_df, x="date", y="value", color='variable',
title="Worldwide Confirmed/Death Cases Over Time")
fig.show()
In [20]:
fig = px.line(ww_melt_df, x="date", y="value", color='variable',
title="Worldwide Confirmed/Death Cases Over Time (Log scale)",
log_y=True)
fig.show()
The fatalities curve seems to just be a shift of the curve for confirmed cases, meaning that mortality rate is almost constant. Watch as the mortality rate rises from 3% to 5% as of April 3rd, 2020. Europe and the US has been hit hard.¶
In [21]:
ww_df['mortality'] = ww_df['fatalities'] / ww_df['confirmed']
fig = px.line(ww_df, x="date", y="mortality",
title="Worldwide Mortality Rate Over Time")
fig.show()
Country-wide Growth¶
In [22]:
country_df = train.groupby(['date', 'country'])[['confirmed', 'fatalities']].sum().reset_index()
country_df.tail()
Out[22]:
| date | country | confirmed | fatalities | |
|---|---|---|---|---|
| 14715 | 2020-04-10 | Vietnam | 257.0 | 0.0 |
| 14716 | 2020-04-10 | West Bank and Gaza | 267.0 | 2.0 |
| 14717 | 2020-04-10 | Western Sahara | 4.0 | 0.0 |
| 14718 | 2020-04-10 | Zambia | 40.0 | 2.0 |
| 14719 | 2020-04-10 | Zimbabwe | 13.0 | 3.0 |
In [23]:
countries = country_df['country'].unique()
print(f'{len(countries)} countries are in dataset:\n{countries}')
184 countries are in dataset: ['Afghanistan' 'Albania' 'Algeria' 'Andorra' 'Angola' 'Antigua and Barbuda' 'Argentina' 'Armenia' 'Australia' 'Austria' 'Azerbaijan' 'Bahamas' 'Bahrain' 'Bangladesh' 'Barbados' 'Belarus' 'Belgium' 'Belize' 'Benin' 'Bhutan' 'Bolivia' 'Bosnia and Herzegovina' 'Botswana' 'Brazil' 'Brunei' 'Bulgaria' 'Burkina Faso' 'Burma' 'Burundi' 'Cabo Verde' 'Cambodia' 'Cameroon' 'Canada' 'Central African Republic' 'Chad' 'Chile' 'China' 'Colombia' 'Congo (Brazzaville)' 'Congo (Kinshasa)' 'Costa Rica' "Cote d'Ivoire" 'Croatia' 'Cuba' 'Cyprus' 'Czechia' 'Denmark' 'Diamond Princess' 'Djibouti' 'Dominica' 'Dominican Republic' 'Ecuador' 'Egypt' 'El Salvador' 'Equatorial Guinea' 'Eritrea' 'Estonia' 'Eswatini' 'Ethiopia' 'Fiji' 'Finland' 'France' 'Gabon' 'Gambia' 'Georgia' 'Germany' 'Ghana' 'Greece' 'Grenada' 'Guatemala' 'Guinea' 'Guinea-Bissau' 'Guyana' 'Haiti' 'Holy See' 'Honduras' 'Hungary' 'Iceland' 'India' 'Indonesia' 'Iran' 'Iraq' 'Ireland' 'Israel' 'Italy' 'Jamaica' 'Japan' 'Jordan' 'Kazakhstan' 'Kenya' 'Korea, South' 'Kosovo' 'Kuwait' 'Kyrgyzstan' 'Laos' 'Latvia' 'Lebanon' 'Liberia' 'Libya' 'Liechtenstein' 'Lithuania' 'Luxembourg' 'MS Zaandam' 'Madagascar' 'Malawi' 'Malaysia' 'Maldives' 'Mali' 'Malta' 'Mauritania' 'Mauritius' 'Mexico' 'Moldova' 'Monaco' 'Mongolia' 'Montenegro' 'Morocco' 'Mozambique' 'Namibia' 'Nepal' 'Netherlands' 'New Zealand' 'Nicaragua' 'Niger' 'Nigeria' 'North Macedonia' 'Norway' 'Oman' 'Pakistan' 'Panama' 'Papua New Guinea' 'Paraguay' 'Peru' 'Philippines' 'Poland' 'Portugal' 'Qatar' 'Romania' 'Russia' 'Rwanda' 'Saint Kitts and Nevis' 'Saint Lucia' 'Saint Vincent and the Grenadines' 'San Marino' 'Sao Tome and Principe' 'Saudi Arabia' 'Senegal' 'Serbia' 'Seychelles' 'Sierra Leone' 'Singapore' 'Slovakia' 'Slovenia' 'Somalia' 'South Africa' 'South Sudan' 'Spain' 'Sri Lanka' 'Sudan' 'Suriname' 'Sweden' 'Switzerland' 'Syria' 'Taiwan*' 'Tanzania' 'Thailand' 'Timor-Leste' 'Togo' 'Trinidad and Tobago' 'Tunisia' 'Turkey' 'US' 'Uganda' 'Ukraine' 'United Arab Emirates' 'United Kingdom' 'Uruguay' 'Uzbekistan' 'Venezuela' 'Vietnam' 'West Bank and Gaza' 'Western Sahara' 'Zambia' 'Zimbabwe']
In [24]:
target_date = country_df['date'].max()
print('Date: ', target_date)
for i in [1, 10, 100, 1000, 10000]:
n_countries = len(country_df.query('(date == @target_date) & confirmed > @i'))
print(f'{n_countries} countries have more than {i} confirmed cases')
Date: 2020-04-10 184 countries have more than 1 confirmed cases 165 countries have more than 10 confirmed cases 121 countries have more than 100 confirmed cases 64 countries have more than 1000 confirmed cases 19 countries have more than 10000 confirmed cases
In [25]:
ax = sns.distplot(np.log10(country_df.query('date == "2020-03-27"')['confirmed'] + 1))
ax.set_xlim([0, 6])
ax.set_xticks(np.arange(7))
_ = ax.set_xticklabels(['0', '10', '100', '1k', '10k', '100k'])
In [26]:
top_country_df = country_df.query('(date == @target_date) & (confirmed > 1000)').sort_values('confirmed', ascending=False)
top_country_melt_df = pd.melt(top_country_df, id_vars='country', value_vars=['confirmed', 'fatalities'])
It seems that US, Italy, and Spain top the list with even Germany having more confirmed cases than China. Korea is also high relative to its population. I believe this is because Korea executes inspection aggressively.¶
In [27]:
fig = px.bar(top_country_melt_df.iloc[::-1],
x='value', y='country', color='variable', barmode='group',
title=f'Confirmed Cases/Deaths on {target_date}', text='value', height=1500, orientation='h')
fig.show()
Let's check major country's growth by date.¶
In [28]:
top30_countries = top_country_df.sort_values('confirmed', ascending=False).iloc[:30]['country'].unique()
top30_countries_df = country_df[country_df['country'].isin(top30_countries)]
fig = px.line(top30_countries_df,
x='date', y='confirmed', color='country',
title=f'Confirmed Cases for top 30 country as of {target_date}')
fig.show()
We can see coronavirus hit China first, but its trend has gone down and flattened by March. Europe is hit by the second wave in March (Italy, Spain, Germany, France, UK). The third wave hits the US now, and the US has a higher growth rate than China or Europe. Its main spread starts from the ides of March and its speed is greater than that of Italy.¶
In [29]:
top30_countries = top_country_df.sort_values('fatalities', ascending=False).iloc[:30]['country'].unique()
top30_countries_df = country_df[country_df['country'].isin(top30_countries)]
fig = px.line(top30_countries_df,
x='date', y='fatalities', color='country',
title=f'Fatalities for top 30 country as of {target_date}')
fig.show()
In [30]:
top_country_df = country_df.query('(date == @target_date) & (confirmed > 100)')
top_country_df['mortality_rate'] = top_country_df['fatalities'] / top_country_df['confirmed']
top_country_df = top_country_df.sort_values('mortality_rate', ascending=False)
In [29]:
fig = px.bar(top_country_df[:30].iloc[::-1],
x='mortality_rate', y='country',
title=f'Mortality rate HIGH: top 30 countries on {target_date}', text='mortality_rate', height=800, orientation='h')
fig.show()
Italy's mortality rate is at 12% as of 4/3/2020. Overall, the top 30 countries have at least a 3-12x higher rate of mortality than what we are lead to believe (the 0.01% number for major demographics).¶
Maybe we can look at countries whose mortality rates are low for insight. THough, many of these countries simply do not report/measure fatalities properly.¶
In [31]:
fig = px.bar(top_country_df[:30].iloc[::-1],
x='mortality_rate', y='country',
title=f'Mortality rate HIGH: top 30 countries on {target_date}', text='mortality_rate', height=800, orientation='h')
fig.show()
In [32]:
fig = px.bar(top_country_df[-30:],
x='mortality_rate', y='country',
title=f'Mortality rate LOW: top 30 countries on {target_date}', text='mortality_rate', height=800, orientation='h')
fig.show()
We can see Italy, and Europe is pretty high. Same for the US and China. However, I wonder why the Phillipines and Indonesia are high while Malaysia, Thailand, Vietnam, and Australia are low - they're all tropical countries.¶
In [33]:
all_country_df = country_df.query('date == @target_date')
all_country_df['confirmed_log1p'] = np.log10(all_country_df['confirmed'] + 1)
all_country_df['fatalities_log1p'] = np.log10(all_country_df['fatalities'] + 1)
all_country_df['mortality_rate'] = all_country_df['fatalities'] / all_country_df['confirmed']
In [34]:
fig = px.choropleth(all_country_df, locations="country",
locationmode='country names', color="confirmed_log1p",
hover_name="country", hover_data=["confirmed", 'fatalities', 'mortality_rate'],
range_color=[all_country_df['confirmed_log1p'].min(), all_country_df['confirmed_log1p'].max()],
color_continuous_scale="peach",
title='Countries with Confirmed Cases')
# I'd like to update colorbar to show raw values, but this does not work somehow...
# Please let me know if you know how to do this!!
trace1 = list(fig.select_traces())[0]
trace1.colorbar = go.choropleth.ColorBar(
tickvals=[0, 1, 2, 3, 4, 5],
ticktext=['1', '10', '100', '1000','10000', '10000'])
fig.show()
In [35]:
fig = px.choropleth(all_country_df, locations="country",
locationmode='country names', color="fatalities_log1p",
hover_name="country", range_color=[0, 4],
hover_data=['confirmed', 'fatalities', 'mortality_rate'],
color_continuous_scale="peach",
title='Countries with fatalities')
fig.show()
In [36]:
fig = px.choropleth(all_country_df, locations="country",
locationmode='country names', color="mortality_rate",
hover_name="country", range_color=[0, 0.12],
color_continuous_scale="peach",
title='Countries with mortality rate')
fig.show()
If we look at only "New" cases, we find the following trends:¶
China reached its peak in Feb 14, new confirmed cases for China is relatively low¶
Europe and US spread starts mid-March, after China slows down¶
Lock Down Policy in Europe (Italy, Spain, Germany, France) should now come into effect, seems that the number of new confirmed cases in Europe is not as high now as it was in mid-March¶
USA has the highes speed of new confirmed cases¶
In [37]:
n_countries = 20
n_start_death = 10
fatality_top_countires = top_country_df.sort_values('fatalities', ascending=False).iloc[:n_countries]['country'].values
country_df['date'] = pd.to_datetime(country_df['date'])
df_list = []
for country in fatality_top_countires:
this_country_df = country_df.query('country == @country')
start_date = this_country_df.query('fatalities > @n_start_death')['date'].min()
this_country_df = this_country_df.query('date >= @start_date')
this_country_df['date_since'] = this_country_df['date'] - start_date
this_country_df['fatalities_log1p'] = np.log10(this_country_df['fatalities'] + 1)
this_country_df['fatalities_log1p'] -= this_country_df['fatalities_log1p'].values[0]
df_list.append(this_country_df)
tmpdf = pd.concat(df_list)
tmpdf['date_since_days'] = tmpdf['date_since'] / pd.Timedelta('1 days')
In [38]:
fig = px.line(tmpdf,
x='date_since_days', y='fatalities_log1p', color='country',
title=f'Fatalities by country since 10 deaths, as of {target_date}')
fig.add_trace(go.Scatter(x=[0, 21], y=[0, 3], name='Double by 7 days', line=dict(dash='dash', color=('rgb(200, 200, 200)'))))
fig.add_trace(go.Scatter(x=[0, 42], y=[0, 3], name='Double by 14 days', line=dict(dash='dash', color=('rgb(200, 200, 200)'))))
fig.add_trace(go.Scatter(x=[0, 63], y=[0, 3], name='Double by 21 days', line=dict(dash='dash', color=('rgb(200, 200, 200)'))))
fig.show()
China has finished its peak at Feb 14, new confirmed cases are surpressed now.¶
Europe and US spread starts on mid of March, after China slows down.¶
I feel effect of lock down policy in Europe (Italy, Spain, Germany, France) now comes on the figure, the number of new cases are not so increasing rapidly at the end of March.¶
Current US new confirmed cases are the worst speed, recording worst speed at more than 30k people/day at peak.¶
Daily new confirmed cases start to decrease from April 4, I hope this trend will continue..¶
In [39]:
country_df['prev_confirmed'] = country_df.groupby('country')['confirmed'].shift(1)
country_df['new_case'] = country_df['confirmed'] - country_df['prev_confirmed']
country_df['new_case'].fillna(0, inplace=True)
top30_country_df = country_df[country_df['country'].isin(top30_countries)]
fig = px.line(top30_country_df,
x='date', y='new_case', color='country',
title=f'DAILY NEW Confirmed cases world wide')
fig.show()
In [40]:
country_df['date'] = country_df['date'].apply(str)
country_df['confirmed_log1p'] = np.log1p(country_df['confirmed'])
country_df['fatalities_log1p'] = np.log1p(country_df['fatalities'])
fig = px.scatter_geo(country_df, locations="country", locationmode='country names',
color="confirmed", size='confirmed', hover_name="country",
hover_data=['confirmed', 'fatalities'],
range_color= [0, country_df['confirmed'].max()],
projection="natural earth", animation_frame="date",
title='COVID-19: Confirmed cases spread Over Time', color_continuous_scale="portland")
# fig.update(layout_coloraxis_showscale=False)
fig.show()
In [41]:
fig = px.scatter_geo(country_df, locations="country", locationmode='country names',
color="fatalities", size='fatalities', hover_name="country",
hover_data=['confirmed', 'fatalities'],
range_color= [0, country_df['fatalities'].max()],
projection="natural earth", animation_frame="date",
title='COVID-19: Fatalities growth Over Time', color_continuous_scale="portland")
fig.show()
In [42]:
country_df.loc[country_df['new_case'] < 0, 'new_case'] = 0.
fig = px.scatter_geo(country_df, locations="country", locationmode='country names',
color="new_case", size='new_case', hover_name="country",
hover_data=['confirmed', 'fatalities'],
range_color= [0, country_df['new_case'].max()],
projection="natural earth", animation_frame="date",
title='COVID-19: Daily NEW cases over Time', color_continuous_scale="portland")
fig.show()
In [ ]:
In [43]:
for country in countries:
province = train.query('country == @country')['province'].unique()
if len(province) > 1:
print(f'Country {country} has {len(province)} provinces: {province}')
Country Australia has 8 provinces: ['Australian Capital Territory' 'New South Wales' 'Northern Territory' 'Queensland' 'South Australia' 'Tasmania' 'Victoria' 'Western Australia'] Country Canada has 12 provinces: ['Alberta' 'British Columbia' 'Manitoba' 'New Brunswick' 'Newfoundland and Labrador' 'Northwest Territories' 'Nova Scotia' 'Ontario' 'Prince Edward Island' 'Quebec' 'Saskatchewan' 'Yukon'] Country China has 33 provinces: ['Anhui' 'Beijing' 'Chongqing' 'Fujian' 'Gansu' 'Guangdong' 'Guangxi' 'Guizhou' 'Hainan' 'Hebei' 'Heilongjiang' 'Henan' 'Hong Kong' 'Hubei' 'Hunan' 'Inner Mongolia' 'Jiangsu' 'Jiangxi' 'Jilin' 'Liaoning' 'Macau' 'Ningxia' 'Qinghai' 'Shaanxi' 'Shandong' 'Shanghai' 'Shanxi' 'Sichuan' 'Tianjin' 'Tibet' 'Xinjiang' 'Yunnan' 'Zhejiang'] Country Denmark has 3 provinces: ['Faroe Islands' 'Greenland' nan] Country France has 11 provinces: ['French Guiana' 'French Polynesia' 'Guadeloupe' 'Martinique' 'Mayotte' 'New Caledonia' 'Reunion' 'Saint Barthelemy' 'Saint Pierre and Miquelon' 'St Martin' nan] Country Netherlands has 5 provinces: ['Aruba' 'Bonaire, Sint Eustatius and Saba' 'Curacao' 'Sint Maarten' nan] Country US has 54 provinces: ['Alabama' 'Alaska' 'Arizona' 'Arkansas' 'California' 'Colorado' 'Connecticut' 'Delaware' 'District of Columbia' 'Florida' 'Georgia' 'Guam' 'Hawaii' 'Idaho' 'Illinois' 'Indiana' 'Iowa' 'Kansas' 'Kentucky' 'Louisiana' 'Maine' 'Maryland' 'Massachusetts' 'Michigan' 'Minnesota' 'Mississippi' 'Missouri' 'Montana' 'Nebraska' 'Nevada' 'New Hampshire' 'New Jersey' 'New Mexico' 'New York' 'North Carolina' 'North Dakota' 'Ohio' 'Oklahoma' 'Oregon' 'Pennsylvania' 'Puerto Rico' 'Rhode Island' 'South Carolina' 'South Dakota' 'Tennessee' 'Texas' 'Utah' 'Vermont' 'Virgin Islands' 'Virginia' 'Washington' 'West Virginia' 'Wisconsin' 'Wyoming'] Country United Kingdom has 11 provinces: ['Anguilla' 'Bermuda' 'British Virgin Islands' 'Cayman Islands' 'Channel Islands' 'Falkland Islands (Malvinas)' 'Gibraltar' 'Isle of Man' 'Montserrat' 'Turks and Caicos Islands' nan]
USA¶
In [44]:
usa_state_code_df = pd.read_csv('./usa_states2.csv')# Prepare data frame only for US.
train_us = train.query('country == "US"')
train_us['mortality_rate'] = train_us['fatalities'] / train_us['confirmed']
# Convert province column to its 2-char code name,
state_name_to_code = dict(zip(usa_state_code_df['state_name'], usa_state_code_df['state_code']))
train_us['province_code'] = train_us['province'].map(state_name_to_code)
# Only show latest days.
train_us_latest = train_us.query('date == @target_date')
In [46]:
# Prepare data frame only for US.
train_us = train.query('country == "US"')
train_us['mortality_rate'] = train_us['fatalities'] / train_us['confirmed']
# Convert province column to its 2-char code name,
state_name_to_code = dict(zip(usa_state_code_df['state_name'], usa_state_code_df['state_code']))
train_us['province_code'] = train_us['province'].map(state_name_to_code)
# Only show latest days.
train_us_latest = train_us.query('date == @target_date')
In [47]:
fig = px.choropleth(train_us_latest, locations='province_code', locationmode="USA-states",
color='confirmed', scope="usa", hover_data=['province', 'fatalities', 'mortality_rate'],
title=f'Confirmed cases in US on {target_date}')
fig.show()
In [48]:
train_us_latest.sort_values('confirmed', ascending=False)
Out[48]:
| id | province | country | date | confirmed | fatalities | country_province | mortality_rate | province_code | |
|---|---|---|---|---|---|---|---|---|---|
| 21679 | 30860 | New York | US | 2020-04-10 | 172348.0 | 7867.0 | US/New York | 0.045646 | NY |
| 21519 | 30632 | New Jersey | US | 2020-04-10 | 54588.0 | 1932.0 | US/New Jersey | 0.035392 | NJ |
| 20879 | 29720 | Michigan | US | 2020-04-10 | 22434.0 | 1276.0 | US/Michigan | 0.056878 | MI |
| 19359 | 27554 | California | US | 2020-04-10 | 21081.0 | 583.0 | US/California | 0.027655 | CA |
| 20799 | 29606 | Massachusetts | US | 2020-04-10 | 20974.0 | 599.0 | US/Massachusetts | 0.028559 | MA |
| 22159 | 31544 | Pennsylvania | US | 2020-04-10 | 20051.0 | 418.0 | US/Pennsylvania | 0.020847 | PA |
| 20559 | 29264 | Louisiana | US | 2020-04-10 | 19253.0 | 755.0 | US/Louisiana | 0.039215 | LA |
| 20159 | 28694 | Illinois | US | 2020-04-10 | 17887.0 | 597.0 | US/Illinois | 0.033376 | IL |
| 19759 | 28124 | Florida | US | 2020-04-10 | 17531.0 | 390.0 | US/Florida | 0.022246 | FL |
| 22639 | 32228 | Texas | US | 2020-04-10 | 12105.0 | 238.0 | US/Texas | 0.019661 | TX |
| 19839 | 28238 | Georgia | US | 2020-04-10 | 11485.0 | 416.0 | US/Georgia | 0.036221 | GA |
| 19519 | 27782 | Connecticut | US | 2020-04-10 | 10538.0 | 448.0 | US/Connecticut | 0.042513 | CT |
| 23039 | 32798 | Washington | US | 2020-04-10 | 10119.0 | 483.0 | US/Washington | 0.047732 | WA |
| 20719 | 29492 | Maryland | US | 2020-04-10 | 6968.0 | 171.0 | US/Maryland | 0.024541 | MD |
| 20239 | 28808 | Indiana | US | 2020-04-10 | 6907.0 | 300.0 | US/Indiana | 0.043434 | IN |
| 19439 | 27668 | Colorado | US | 2020-04-10 | 6202.0 | 226.0 | US/Colorado | 0.036440 | CO |
| 21919 | 31202 | Ohio | US | 2020-04-10 | 5878.0 | 231.0 | US/Ohio | 0.039299 | OH |
| 22559 | 32114 | Tennessee | US | 2020-04-10 | 4891.0 | 98.0 | US/Tennessee | 0.020037 | TN |
| 22959 | 32684 | Virginia | US | 2020-04-10 | 4509.0 | 121.0 | US/Virginia | 0.026835 | VA |
| 21759 | 30974 | North Carolina | US | 2020-04-10 | 3965.0 | 83.0 | US/North Carolina | 0.020933 | NC |
| 21119 | 30062 | Missouri | US | 2020-04-10 | 3897.0 | 99.0 | US/Missouri | 0.025404 | MO |
| 19199 | 27326 | Arizona | US | 2020-04-10 | 3112.0 | 97.0 | US/Arizona | 0.031170 | AZ |
| 23199 | 33026 | Wisconsin | US | 2020-04-10 | 3068.0 | 128.0 | US/Wisconsin | 0.041721 | WI |
| 22399 | 31886 | South Carolina | US | 2020-04-10 | 3067.0 | 72.0 | US/South Carolina | 0.023476 | SC |
| 19039 | 27098 | Alabama | US | 2020-04-10 | 2947.0 | 80.0 | US/Alabama | 0.027146 | AL |
| 21359 | 30404 | Nevada | US | 2020-04-10 | 2722.0 | 90.0 | US/Nevada | 0.033064 | NV |
| 21039 | 29948 | Mississippi | US | 2020-04-10 | 2469.0 | 82.0 | US/Mississippi | 0.033212 | MS |
| 22719 | 32342 | Utah | US | 2020-04-10 | 2103.0 | 17.0 | US/Utah | 0.008084 | UT |
| 21999 | 31316 | Oklahoma | US | 2020-04-10 | 1794.0 | 88.0 | US/Oklahoma | 0.049052 | OK |
| 22319 | 31772 | Rhode Island | US | 2020-04-10 | 1727.0 | 43.0 | US/Rhode Island | 0.024899 | RI |
| 20479 | 29150 | Kentucky | US | 2020-04-10 | 1693.0 | 90.0 | US/Kentucky | 0.053160 | KY |
| 19679 | 28010 | District of Columbia | US | 2020-04-10 | 1660.0 | 38.0 | US/District of Columbia | 0.022892 | DC |
| 20319 | 28922 | Iowa | US | 2020-04-10 | 1388.0 | 31.0 | US/Iowa | 0.022334 | IA |
| 20079 | 28580 | Idaho | US | 2020-04-10 | 1354.0 | 24.0 | US/Idaho | 0.017725 | ID |
| 20959 | 29834 | Minnesota | US | 2020-04-10 | 1336.0 | 57.0 | US/Minnesota | 0.042665 | MN |
| 19599 | 27896 | Delaware | US | 2020-04-10 | 1326.0 | 32.0 | US/Delaware | 0.024133 | DE |
| 22079 | 31430 | Oregon | US | 2020-04-10 | 1322.0 | 44.0 | US/Oregon | 0.033283 | OR |
| 19279 | 27440 | Arkansas | US | 2020-04-10 | 1171.0 | 21.0 | US/Arkansas | 0.017933 | AR |
| 20399 | 29036 | Kansas | US | 2020-04-10 | 1117.0 | 42.0 | US/Kansas | 0.037601 | KS |
| 21599 | 30746 | New Mexico | US | 2020-04-10 | 1081.0 | 17.0 | US/New Mexico | 0.015726 | NM |
| 21439 | 30518 | New Hampshire | US | 2020-04-10 | 819.0 | 21.0 | US/New Hampshire | 0.025641 | NH |
| 22239 | 31658 | Puerto Rico | US | 2020-04-10 | 725.0 | 39.0 | US/Puerto Rico | 0.053793 | PR |
| 22799 | 32456 | Vermont | US | 2020-04-10 | 679.0 | 24.0 | US/Vermont | 0.035346 | VT |
| 21279 | 30290 | Nebraska | US | 2020-04-10 | 635.0 | 15.0 | US/Nebraska | 0.023622 | NE |
| 20639 | 29378 | Maine | US | 2020-04-10 | 586.0 | 17.0 | US/Maine | 0.029010 | ME |
| 23119 | 32912 | West Virginia | US | 2020-04-10 | 574.0 | 5.0 | US/West Virginia | 0.008711 | WV |
| 22479 | 32000 | South Dakota | US | 2020-04-10 | 536.0 | 6.0 | US/South Dakota | 0.011194 | SD |
| 19999 | 28466 | Hawaii | US | 2020-04-10 | 442.0 | 6.0 | US/Hawaii | 0.013575 | HI |
| 21199 | 30176 | Montana | US | 2020-04-10 | 365.0 | 6.0 | US/Montana | 0.016438 | MT |
| 21839 | 31088 | North Dakota | US | 2020-04-10 | 278.0 | 6.0 | US/North Dakota | 0.021583 | ND |
| 19119 | 27212 | Alaska | US | 2020-04-10 | 246.0 | 7.0 | US/Alaska | 0.028455 | AK |
| 23279 | 33140 | Wyoming | US | 2020-04-10 | 239.0 | 0.0 | US/Wyoming | 0.000000 | WY |
| 19919 | 28352 | Guam | US | 2020-04-10 | 130.0 | 4.0 | US/Guam | 0.030769 | GU |
| 22879 | 32570 | Virgin Islands | US | 2020-04-10 | 50.0 | 1.0 | US/Virgin Islands | 0.020000 | VI |
In [49]:
fig = px.choropleth(train_us_latest, locations='province_code', locationmode="USA-states",
color='mortality_rate', scope="usa", hover_data=['province', 'fatalities', 'mortality_rate'],
title=f'Mortality rate in US on {target_date}')
fig.show()
In [50]:
train_us_march = train_us.query('date > "2020-03-01"')
fig = px.line(train_us_march,
x='date', y='confirmed', color='province',
title=f'Confirmed cases by state in US, as of {target_date}')
fig.show()
Europe¶
In [51]:
# Ref: https://www.kaggle.com/abhinand05/covid-19-digging-a-bit-deeper
europe_country_list =list([
'Austria','Belgium','Bulgaria','Croatia','Cyprus','Czechia','Denmark','Estonia','Finland','France','Germany','Greece','Hungary','Ireland',
'Italy', 'Latvia','Luxembourg','Lithuania','Malta','Norway','Netherlands','Poland','Portugal','Romania','Slovakia','Slovenia',
'Spain', 'Sweden', 'United Kingdom', 'Iceland', 'Russia', 'Switzerland', 'Serbia', 'Ukraine', 'Belarus',
'Albania', 'Bosnia and Herzegovina', 'Kosovo', 'Moldova', 'Montenegro', 'North Macedonia'])
country_df['date'] = pd.to_datetime(country_df['date'])
train_europe = country_df[country_df['country'].isin(europe_country_list)]
#train_europe['date_str'] = pd.to_datetime(train_europe['date'])
train_europe_latest = train_europe.query('date == @target_date')
In [52]:
fig = px.choropleth(train_europe_latest, locations="country",
locationmode='country names', color="confirmed",
hover_name="country", range_color=[1, train_europe_latest['confirmed'].max()],
color_continuous_scale='portland',
title=f'European Countries with Confirmed Cases as of {target_date}', scope='europe', height=800)
fig.show()
In [53]:
train_europe_march = train_europe.query('date >= "2020-03-01"')
fig = px.line(train_europe_march,
x='date', y='confirmed', color='country',
title=f'Confirmed cases by country in Europe, as of {target_date}')
fig.show()
In [54]:
fig = px.line(train_europe_march,
x='date', y='fatalities', color='country',
title=f'Fatalities by country in Europe, as of {target_date}')
fig.show()
In [55]:
train_europe_march['prev_confirmed'] = train_europe_march.groupby('country')['confirmed'].shift(1)
train_europe_march['new_case'] = train_europe_march['confirmed'] - train_europe_march['prev_confirmed']
fig = px.line(train_europe_march,
x='date', y='new_case', color='country',
title=f'DAILY NEW Confirmed cases by country in Europe')
fig.show()
Asia¶
In [56]:
country_latest = country_df.query('date == @target_date')
fig = px.choropleth(country_latest, locations="country",
locationmode='country names', color="confirmed",
hover_name="country", range_color=[1, 50000],
color_continuous_scale='portland',
title=f'Asian Countries with Confirmed Cases as of {target_date}', scope='asia', height=800)
fig.show()
In [57]:
top_asian_country_df = country_df[country_df['country'].isin(['China', 'Indonesia', 'Iran', 'Japan', 'Korea, South', 'Malaysia', 'Philippines'])]
fig = px.line(top_asian_country_df,
x='date', y='new_case', color='country',
title=f'DAILY NEW Confirmed cases world wide')
fig.show()
Recovery¶
In [58]:
max_confirmed = country_df.groupby('country')['new_case'].max().reset_index()
country_latest = pd.merge(country_latest, max_confirmed.rename({'new_case': 'max_new_case'}, axis=1))
country_latest['new_case_peak_to_now_ratio'] = country_latest['new_case'] / country_latest['max_new_case']
In [61]:
recovering_country = country_latest.query('new_case_peak_to_now_ratio < 0.5')
major_recovering_country = recovering_country.query('confirmed > 100')
In [63]:
fig = px.bar(major_recovering_country.sort_values('new_case_peak_to_now_ratio', ascending=False),
x='new_case_peak_to_now_ratio', y='country',
title=f'Mortality rate LOW: top 30 countries on {target_date}', text='new_case_peak_to_now_ratio', height=1000, orientation='h')
fig.show()
In [64]:
fig = px.choropleth(country_latest, locations="country",
locationmode='country names', color="new_case_peak_to_now_ratio",
hover_name="country", range_color=[0, 1],
# color_continuous_scale="peach",
hover_data=['confirmed', 'fatalities', 'new_case', 'max_new_case'],
title='Countries with new_case_peak_to_now_ratio')
fig.show()
In [65]:
china_df = train.query('country == "China"')
china_df['prev_confirmed'] = china_df.groupby('country')['confirmed'].shift(1)
china_df['new_case'] = china_df['confirmed'] - china_df['prev_confirmed']
china_df.loc[china_df['new_case'] < 0, 'new_case'] = 0.
In [66]:
fig = px.line(china_df,
x='date', y='new_case', color='province',
title=f'DAILY NEW Confirmed cases in China by province')
fig.show()
In [67]:
china_df.query('(province == "Hubei") & (date > "2020-03-10")')
Out[67]:
| id | province | country | date | confirmed | fatalities | country_province | prev_confirmed | new_case | |
|---|---|---|---|---|---|---|---|---|---|
| 5409 | 7688 | Hubei | China | 2020-03-11 | 67773.0 | 3046.0 | China/Hubei | 67760.0 | 13.0 |
| 5410 | 7689 | Hubei | China | 2020-03-12 | 67781.0 | 3056.0 | China/Hubei | 67773.0 | 8.0 |
| 5411 | 7690 | Hubei | China | 2020-03-13 | 67786.0 | 3062.0 | China/Hubei | 67781.0 | 5.0 |
| 5412 | 7691 | Hubei | China | 2020-03-14 | 67790.0 | 3075.0 | China/Hubei | 67786.0 | 4.0 |
| 5413 | 7692 | Hubei | China | 2020-03-15 | 67794.0 | 3085.0 | China/Hubei | 67790.0 | 4.0 |
| 5414 | 7693 | Hubei | China | 2020-03-16 | 67798.0 | 3099.0 | China/Hubei | 67794.0 | 4.0 |
| 5415 | 7694 | Hubei | China | 2020-03-17 | 67799.0 | 3111.0 | China/Hubei | 67798.0 | 1.0 |
| 5416 | 7695 | Hubei | China | 2020-03-18 | 67800.0 | 3122.0 | China/Hubei | 67799.0 | 1.0 |
| 5417 | 7696 | Hubei | China | 2020-03-19 | 67800.0 | 3130.0 | China/Hubei | 67800.0 | 0.0 |
| 5418 | 7697 | Hubei | China | 2020-03-20 | 67800.0 | 3133.0 | China/Hubei | 67800.0 | 0.0 |
| 5419 | 7698 | Hubei | China | 2020-03-21 | 67800.0 | 3139.0 | China/Hubei | 67800.0 | 0.0 |
| 5420 | 7699 | Hubei | China | 2020-03-22 | 67800.0 | 3153.0 | China/Hubei | 67800.0 | 0.0 |
| 5421 | 7700 | Hubei | China | 2020-03-23 | 67800.0 | 3153.0 | China/Hubei | 67800.0 | 0.0 |
| 5422 | 7701 | Hubei | China | 2020-03-24 | 67801.0 | 3160.0 | China/Hubei | 67800.0 | 1.0 |
| 5423 | 7702 | Hubei | China | 2020-03-25 | 67801.0 | 3163.0 | China/Hubei | 67801.0 | 0.0 |
| 5424 | 7703 | Hubei | China | 2020-03-26 | 67801.0 | 3169.0 | China/Hubei | 67801.0 | 0.0 |
| 5425 | 7704 | Hubei | China | 2020-03-27 | 67801.0 | 3174.0 | China/Hubei | 67801.0 | 0.0 |
| 5426 | 7705 | Hubei | China | 2020-03-28 | 67801.0 | 3177.0 | China/Hubei | 67801.0 | 0.0 |
| 5427 | 7706 | Hubei | China | 2020-03-29 | 67801.0 | 3182.0 | China/Hubei | 67801.0 | 0.0 |
| 5428 | 7707 | Hubei | China | 2020-03-30 | 67801.0 | 3186.0 | China/Hubei | 67801.0 | 0.0 |
| 5429 | 7708 | Hubei | China | 2020-03-31 | 67801.0 | 3187.0 | China/Hubei | 67801.0 | 0.0 |
| 5430 | 7709 | Hubei | China | 2020-04-01 | 67802.0 | 3193.0 | China/Hubei | 67801.0 | 1.0 |
| 5431 | 7710 | Hubei | China | 2020-04-02 | 67802.0 | 3199.0 | China/Hubei | 67802.0 | 0.0 |
| 5432 | 7711 | Hubei | China | 2020-04-03 | 67802.0 | 3203.0 | China/Hubei | 67802.0 | 0.0 |
| 5433 | 7712 | Hubei | China | 2020-04-04 | 67803.0 | 3207.0 | China/Hubei | 67802.0 | 1.0 |
| 5434 | 7713 | Hubei | China | 2020-04-05 | 67803.0 | 3210.0 | China/Hubei | 67803.0 | 0.0 |
| 5435 | 7714 | Hubei | China | 2020-04-06 | 67803.0 | 3212.0 | China/Hubei | 67803.0 | 0.0 |
| 5436 | 7715 | Hubei | China | 2020-04-07 | 67803.0 | 3212.0 | China/Hubei | 67803.0 | 0.0 |
| 5437 | 7716 | Hubei | China | 2020-04-08 | 67803.0 | 3213.0 | China/Hubei | 67803.0 | 0.0 |
| 5438 | 7717 | Hubei | China | 2020-04-09 | 67803.0 | 3215.0 | China/Hubei | 67803.0 | 0.0 |
| 5439 | 7718 | Hubei | China | 2020-04-10 | 67803.0 | 3216.0 | China/Hubei | 67803.0 | 0.0 |
Sigmoid Fitting - When will it converge?¶
In [68]:
def sigmoid(t, M, beta, alpha, offset=0):
alpha += offset
return M / (1 + np.exp(-beta * (t - alpha)))
def error(x, y, params):
M, beta, alpha = params
y_pred = sigmoid(x, M, beta, alpha)
loss_mse = np.mean((y_pred - y) ** 2)
return loss_mse
def gen_random_color(min_value=0, max_value=256) -> str:
"""Generate random color for plotly"""
r, g, b = np.random.randint(min_value, max_value, 3)
return f'rgb({r},{g},{b})'
In [69]:
def fit_sigmoid(exclude_days=0):
target_country_df_list = []
pred_df_list = []
for target_country in top30_countries:
print('target_country', target_country)
# --- Train ---
target_country_df = country_df.query('country == @target_country')
#train_start_date = target_country_df['date'].min()
train_start_date = target_country_df.query('confirmed > 1000')['date'].min()
train_end_date = pd.to_datetime(target_date) - pd.Timedelta(f'{exclude_days} days')
target_date_df = target_country_df.query('(date >= @train_start_date) & (date <= @train_end_date)')
if len(target_date_df) <= 7:
print('WARNING: the data is not enough, use 7 more days...')
train_start_date -= pd.Timedelta('7 days')
target_date_df = target_country_df.query('(date >= @train_start_date) & (date <= @train_end_date)')
confirmed = target_date_df['confirmed'].values
x = np.arange(len(confirmed))
lossfun = lambda params: error(x, confirmed, params)
res = sp.optimize.minimize(lossfun, x0=[np.max(confirmed) * 5, 0.08, len(confirmed) / 3.], method='nelder-mead')
M, beta, alpha = res.x
# sigmoid_models[key] = (M, beta, alpha)
# np.clip(sigmoid(list(range(len(data), len(data) + steps)), M, beta, alpha), 0, None).astype(int)
# --- Pred ---
pred_start_date = target_country_df['date'].min()
pred_end_date = pd.to_datetime('2020-07-01')
days = int((pred_end_date - pred_start_date) / pd.Timedelta('1 days'))
# print('pred start', pred_start_date, 'end', pred_end_date, 'days', days)
x = np.arange(days)
offset = (train_start_date - pred_start_date) / pd.Timedelta('1 days')
print('train_start_date', train_start_date, 'offset', offset, 'params', M, beta, alpha)
y_pred = sigmoid(x, M, beta, alpha, offset=offset)
# target_country_df['confirmed_pred'] = y_pred
all_dates = [pred_start_date + np.timedelta64(x, 'D') for x in range(days)]
pred_df = pd.DataFrame({
'date': all_dates,
'country': target_country,
'confirmed_pred': y_pred,
})
target_country_df_list.append(target_country_df)
pred_df_list.append(pred_df)
return target_country_df_list, pred_df_list
In [70]:
def plot_sigmoid_fitting(target_country_df_list, pred_df_list, title=''):
n_countries = len(top30_countries)
# --- Plot ---
fig = go.Figure()
for i in range(n_countries):
target_country = top30_countries[i]
target_country_df = target_country_df_list[i]
pred_df = pred_df_list[i]
color = gen_random_color(min_value=20)
# Prediction
fig.add_trace(go.Scatter(
x=pred_df['date'], y=pred_df['confirmed_pred'],
name=f'{target_country}_pred',
line=dict(color=color, dash='dash')
))
# Ground truth
fig.add_trace(go.Scatter(
x=target_country_df['date'], y=target_country_df['confirmed'],
mode='markers', name=f'{target_country}_actual',
line=dict(color=color),
))
fig.update_layout(
title=title, xaxis_title='Date', yaxis_title='Confirmed cases')
fig.show()
In [71]:
target_country_df_list, pred_df_list = fit_sigmoid(exclude_days=0)
target_country Italy train_start_date 2020-02-29 00:00:00 offset 38.0 params 155289.19297058647 0.15784705756779646 25.64939284574629 target_country US train_start_date 2020-03-11 00:00:00 offset 49.0 params 629211.7086629425 0.20922392738534465 24.206110413171142 target_country Spain train_start_date 2020-03-09 00:00:00 offset 47.0 params 170752.05544823824 0.2007733421365847 20.685283003186257 target_country France train_start_date 2020-03-08 00:00:00 offset 46.0 params 199495.88648520567 0.16008636471968418 29.28330348530287 target_country United Kingdom train_start_date 2020-03-14 00:00:00 offset 52.0 params 108798.07287786715 0.18419334140435267 23.362867353304743 target_country Iran train_start_date 2020-03-02 00:00:00 offset 40.0 params 100149.87864006351 0.10736609502139803 31.328753402770534 target_country China train_start_date 2020-01-25 00:00:00 offset 3.0 params 81544.44611086076 0.21736754777344824 14.85184928965209 target_country Belgium train_start_date 2020-03-16 00:00:00 offset 54.0 params 31728.859666973964 0.18970333904706163 17.232896697057413 target_country Germany train_start_date 2020-03-08 00:00:00 offset 46.0 params 134157.19954788944 0.18742159016126503 21.992889514589024 target_country Netherlands train_start_date 2020-03-15 00:00:00 offset 53.0 params 26964.63108744545 0.17175094372698652 16.74126390052387 target_country Brazil train_start_date 2020-03-21 00:00:00 offset 59.0 params 55325.570724255216 0.15438911559167964 23.822710913454465 target_country Turkey train_start_date 2020-03-22 00:00:00 offset 60.0 params 69274.1132054875 0.21972617402547984 15.923198744657242 target_country Switzerland train_start_date 2020-03-13 00:00:00 offset 51.0 params 25451.68750004688 0.18807192791314048 14.088087609224367 target_country Sweden train_start_date 2020-03-15 00:00:00 offset 53.0 params 22065.90242452427 0.11028510604226319 28.285224853943184 target_country Canada train_start_date 2020-03-21 00:00:00 offset 59.0 params 29506.141311078994 0.19560469141259582 14.692344439733452 target_country Portugal train_start_date 2020-03-20 00:00:00 offset 58.0 params 17296.993683436696 0.20016282426203508 12.672731673334388 target_country Austria train_start_date 2020-03-16 00:00:00 offset 54.0 params 13504.833933593814 0.23578414946440346 10.201773773438852 target_country Indonesia train_start_date 2020-03-27 00:00:00 offset 65.0 params 44209.76455806295 0.0892533175595086 41.43913572244895 target_country Ecuador train_start_date 2020-03-24 00:00:00 offset 62.0 params 264623.797744685 0.09911081601322475 54.959071199125646 target_country Ireland train_start_date 2020-03-23 00:00:00 offset 61.0 params 23741.747339800488 0.11294226326128778 24.79734215509842 target_country Romania train_start_date 2020-03-26 00:00:00 offset 64.0 params 7855.432266332298 0.17292159101178506 10.322010501833716 target_country Algeria train_start_date 2020-04-03 00:00:00 offset 72.0 params 7240.223066960547 0.07205715100391677 22.781991527569417 target_country Denmark train_start_date 2020-03-17 00:00:00 offset 55.0 params 17291.861641710733 0.0943198196067776 30.125391215881017 target_country India train_start_date 2020-03-29 00:00:00 offset 67.0 params 14554.10065417047 0.22223953265465735 11.623505429560861 target_country Philippines train_start_date 2020-03-28 00:00:00 offset 66.0 params 4435.2530191723745 0.28335113927066113 3.78020138605345 target_country Korea, South train_start_date 2020-02-26 00:00:00 offset 35.0 params 9754.59123802096 0.17706467320708658 6.53125607951186 target_country Mexico train_start_date 2020-03-31 00:00:00 offset 69.0 params 48746.58062583256 0.12428282519935041 30.635077843173406 target_country Poland train_start_date 2020-03-25 00:00:00 offset 63.0 params 9656.089594373945 0.1615323090240025 13.03047948206494 target_country Peru train_start_date 2020-03-31 00:00:00 offset 69.0 params 150951.59474708766 0.19610787181237338 26.24727545652297 target_country Egypt WARNING: the data is not enough, use 7 more days... train_start_date 2020-03-28 00:00:00 offset 66.0 params 5675.7241849520815 0.11670787266832763 19.398217968455562
In [72]:
plot_sigmoid_fitting(target_country_df_list, pred_df_list, title='Sigmoid fitting with all latest data')
The results I get show me that the number of confirmed cases will slow down and converge in mid-April for most of the USA.¶
Iran, belgium< Sweden, and Denmark will converge later - around May¶
Let's validate the accuracy by checking what it would have predicted last week by excluding data for the past 7 days¶
In [73]:
target_country_df_list, pred_df_list = fit_sigmoid(exclude_days=7)
target_country Italy train_start_date 2020-02-29 00:00:00 offset 38.0 params 136868.20320874284 0.17765214474353921 23.811927252252296 target_country US train_start_date 2020-03-11 00:00:00 offset 49.0 params 429033.6065513191 0.25065790072536387 20.874465825590676 target_country Spain train_start_date 2020-03-09 00:00:00 offset 47.0 params 153654.41681756417 0.21865119353017326 19.517186520124362 target_country France train_start_date 2020-03-08 00:00:00 offset 46.0 params 104055.24404022889 0.1812508277441136 23.123034782166062 target_country United Kingdom train_start_date 2020-03-14 00:00:00 offset 52.0 params 85219.15699573018 0.20292315761712792 20.98432139853092 target_country Iran train_start_date 2020-03-02 00:00:00 offset 40.0 params 160135.3240712628 0.09420311093878417 39.31732345858555 target_country China train_start_date 2020-01-25 00:00:00 offset 3.0 params 81324.78621971796 0.2187236593586685 14.815415561601606 target_country Belgium train_start_date 2020-03-16 00:00:00 offset 54.0 params 24017.360900116226 0.22653032668183323 14.357569256277595 target_country Germany train_start_date 2020-03-08 00:00:00 offset 46.0 params 116937.55330446549 0.20777491055860606 20.406330587453024 target_country Netherlands train_start_date 2020-03-15 00:00:00 offset 53.0 params 21215.69600885961 0.2046607627691383 13.900903827214746 target_country Brazil train_start_date 2020-03-21 00:00:00 offset 59.0 params 148320.0822382808 0.15109031999717748 31.110969698248336 target_country Turkey train_start_date 2020-03-22 00:00:00 offset 60.0 params 29249.396194101508 0.3398443473886603 9.45084526627505 target_country Switzerland train_start_date 2020-03-13 00:00:00 offset 51.0 params 21611.395069033555 0.22554770962692433 12.09874027829159 target_country Sweden train_start_date 2020-03-15 00:00:00 offset 53.0 params 102479.31081398879 0.09959103852282436 46.84675442276723 target_country Canada train_start_date 2020-03-21 00:00:00 offset 59.0 params 23934.575020037213 0.2166650224452155 12.678478756583697 target_country Portugal train_start_date 2020-03-20 00:00:00 offset 58.0 params 13886.526652929035 0.24153253074463188 10.374704595661079 target_country Austria train_start_date 2020-03-16 00:00:00 offset 54.0 params 12833.089751924903 0.2543192076502132 9.65112704489874 target_country Indonesia train_start_date 2020-03-27 00:00:00 offset 65.0 params 5538.409292931685 0.12240192516430251 11.850875387207957 target_country Ecuador train_start_date 2020-03-24 00:00:00 offset 62.0 params 50381.9576575238 0.1194307874532195 32.037191120992446 target_country Ireland train_start_date 2020-03-23 00:00:00 offset 61.0 params 7127.42669643394 0.17986454789628797 9.016752200595212 target_country Romania train_start_date 2020-03-26 00:00:00 offset 64.0 params 5136.784260627357 0.2173605116239273 6.080927509185396 target_country Algeria WARNING: the data is not enough, use 7 more days... train_start_date 2020-03-27 00:00:00 offset 65.0 params 17711.638207541007 0.1671533685884115 22.910947003569824 target_country Denmark train_start_date 2020-03-17 00:00:00 offset 55.0 params 83251.94278047117 0.08152315921316566 54.12780994117452 target_country India WARNING: the data is not enough, use 7 more days... train_start_date 2020-03-22 00:00:00 offset 60.0 params 69491.96627113386 0.17073364955512504 30.86874281158353 target_country Philippines WARNING: the data is not enough, use 7 more days... train_start_date 2020-03-21 00:00:00 offset 59.0 params 7448.924360208792 0.22519220719006322 14.635625475511459 target_country Korea, South train_start_date 2020-02-26 00:00:00 offset 35.0 params 9269.113384465261 0.22003161565504042 5.865122104713922 target_country Mexico WARNING: the data is not enough, use 7 more days... train_start_date 2020-03-24 00:00:00 offset 62.0 params 2321.8831572385125 0.2376550809601527 7.432306629568103 target_country Poland train_start_date 2020-03-25 00:00:00 offset 63.0 params 32650.52744194722 0.13345964216685124 25.265336260789567 target_country Peru WARNING: the data is not enough, use 7 more days... train_start_date 2020-03-24 00:00:00 offset 62.0 params 7789.878261285809 0.15412749939896492 18.72852309504909 target_country Egypt WARNING: the data is not enough, use 7 more days... train_start_date 2020-03-28 00:00:00 offset 66.0 params 8109.176779226719 0.1009685992191213 25.94009947748825
In [74]:
plot_sigmoid_fitting(target_country_df_list, pred_df_list, title='Sigmoid fitting without last 7days data')
It seems as though sigmoid fitting tends to UNDERESTIMATE the curve, and its actual value tends to be more than the sigmoid curve fit estimation.¶
In [ ]: