EDA - Exploratory Data Analysis

Version: 1.00 - April 3rd, 2020

1. Load Data

2. Worldwide Trend

3. Country-wise growth

4. Looking at provinces

5. Zoom into USA

6. Europe

7. Asia

8. Which countries are recovering now?

9. When will this converge - estimation by sigmoid fitting

In [1]:
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)

Load Data

In [2]:
# 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 3.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
./SIR_example.png
./Untitled.ipynb
./Untitled1.ipynb
./Untitled2.ipynb
./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
./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
./exoTest.csv
./exoTrain.csv
./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
./sample_submission.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
./.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 3-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
./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
./logs\segmentation\train_log\events.out.tfevents.1580402944.SkyNet.13532.23
./logs\segmentation\train_log\events.out.tfevents.1580402960.SkyNet.13532.24
./None\checkpoints\_metrics.json
./None\checkpoints\infer.1.exception_Exception.pth
./test_images\002f507.jpg
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./Untitled Folder 1\Numpy CrashCorse.ipynb
./Untitled Folder 1\.ipynb_checkpoints\Numpy CrashCorse-checkpoint.ipynb
./week 1\submission (2).csv
./week 1\test (2).csv
./week 1\train (2).csv
./week 2\submission (3).csv
./week 2\test (3).csv
./week 2\train (3).csv
./week 3\submission (3).csv
./week 3\test (3).csv
./week 3\train (3).csv
In [8]:
%%time
datadir = Path('./week 3/')

# 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: 32 ms
In [9]:
train
Out[9]:
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
... ... ... ... ... ... ...
22027 32703 NaN Zimbabwe 2020-03-29 7.0 1.0
22028 32704 NaN Zimbabwe 2020-03-30 7.0 1.0
22029 32705 NaN Zimbabwe 2020-03-31 8.0 1.0
22030 32706 NaN Zimbabwe 2020-04-01 8.0 1.0
22031 32707 NaN Zimbabwe 2020-04-02 9.0 1.0

22032 rows × 6 columns

In [10]:
test
Out[10]:
ForecastId Province_State Country_Region Date
0 1 NaN Afghanistan 2020-03-26
1 2 NaN Afghanistan 2020-03-27
2 3 NaN Afghanistan 2020-03-28
3 4 NaN Afghanistan 2020-03-29
4 5 NaN Afghanistan 2020-03-30
... ... ... ... ...
13153 13154 NaN Zimbabwe 2020-05-03
13154 13155 NaN Zimbabwe 2020-05-04
13155 13156 NaN Zimbabwe 2020-05-05
13156 13157 NaN Zimbabwe 2020-05-06
13157 13158 NaN Zimbabwe 2020-05-07

13158 rows × 4 columns

In [11]:
submission
Out[11]:
ForecastId ConfirmedCases Fatalities
0 1 1 1
1 2 1 1
2 3 1 1
3 4 1 1
4 5 1 1
... ... ... ...
13153 13154 1 1
13154 13155 1 1
13155 13156 1 1
13156 13157 1 1
13157 13158 1 1

13158 rows × 3 columns

In [12]:
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 [13]:
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[13]:
date confirmed fatalities new_case
67 2020-03-29 719952.0 33921.0 59411.0
68 2020-03-30 782200.0 37578.0 62248.0
69 2020-03-31 857320.0 42103.0 75120.0
70 2020-04-01 932434.0 46804.0 75114.0
71 2020-04-02 1012981.0 52978.0 80547.0

ww_melt_df = pd.melt(ww_df, id_vars=['date'], value_vars=['confirmed', 'fatalities', 'new_case']) ww_melt_df

Watch this exponential growth. It seems to be doubling each week.

In [15]:
fig = px.line(ww_melt_df, x="date", y="value", color='variable', 
              title="Worldwide Confirmed/Death Cases Over Time")
fig.show()

The purpose of the log-scale is to see the growth of the growth rate. You see that there is a slight increase of this from the beginning of March to the end of March. Despite the Lockdown policy in the US and Europe, the number is still rapidly increasing.

In [16]:
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 [17]:
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 [18]:
country_df = train.groupby(['date', 'country'])[['confirmed', 'fatalities']].sum().reset_index()
country_df.tail()
Out[18]:
date country confirmed fatalities
12955 2020-04-02 Venezuela 146.0 5.0
12956 2020-04-02 Vietnam 233.0 0.0
12957 2020-04-02 West Bank and Gaza 161.0 1.0
12958 2020-04-02 Zambia 39.0 1.0
12959 2020-04-02 Zimbabwe 9.0 1.0
In [19]:
countries = country_df['country'].unique()
print(f'{len(countries)} countries are in dataset:\n{countries}')
180 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' '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' 'Saudi Arabia' 'Senegal' 'Serbia' 'Seychelles'
 'Sierra Leone' 'Singapore' 'Slovakia' 'Slovenia' 'Somalia' 'South Africa'
 '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' 'Zambia' 'Zimbabwe']
In [20]:
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:  2020-04-02
178 countries have more than 1 confirmed cases
148 countries have more than 10 confirmed cases
112 countries have more than 100 confirmed cases
52 countries have more than 1000 confirmed cases
14 countries have more than 10000 confirmed cases
In [21]:
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 [22]:
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 [24]:
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 [25]:
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 [27]:
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()

We see that in terms of fatalities, Europe is more serious than the US right now

Let's look at mortality rate by country

In [28]:
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 [30]:
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()

Let's plot confirmed cases on a mapall_country_df = country_df.query('date == @target_date')

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']

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()

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 [34]:
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 [35]:
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 [36]:
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 [37]:
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 [38]:
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 [39]:
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()

Provinces

Only 8 countries have province data
In [40]:
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 10 provinces: ['French Guiana' 'French Polynesia' 'Guadeloupe' 'Martinique' 'Mayotte'
 'New Caledonia' 'Reunion' 'Saint Barthelemy' 'St Martin' nan]
Country Netherlands has 4 provinces: ['Aruba' '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 10 provinces: ['Anguilla' 'Bermuda' 'British Virgin Islands' 'Cayman Islands'
 'Channel Islands' 'Gibraltar' 'Isle of Man' 'Montserrat'
 'Turks and Caicos Islands' nan]

USA

In [42]:
usa_state_code_df = pd.read_csv('./usa_states2.csv')
In [43]:
# 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 [44]:
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 [45]:
#Clearly NY has the highest, let's look at NY's mortality rate
train_us_latest.sort_values('confirmed', ascending=False)

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 [46]:
#Not that high at about 2% 
#In NY, less than 1000 confirmed cases on March 16, but more than 50k are confirmed by March 30. That's a 50x explosion in 2 weeks.

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 [62]:
# 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 [63]:
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 [64]:
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 [65]:
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 [66]:
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 [67]:
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 [68]:
top_asian_country_df = top30_country_df[top30_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 [69]:
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 [70]:
recovering_country = country_latest.query('new_case_peak_to_now_ratio < 0.5')
major_recovering_country = recovering_country.query('confirmed > 100')
In [71]:
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 [72]:
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 [73]:
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 [74]:
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 [75]:
china_df.query('(province == "Hubei") & (date > "2020-03-10")')
Out[75]:
id province country date confirmed fatalities country_province prev_confirmed new_case
4873 7219 Hubei China 2020-03-11 67773.0 3046.0 China/Hubei 67760.0 13.0
4874 7220 Hubei China 2020-03-12 67781.0 3056.0 China/Hubei 67773.0 8.0
4875 7221 Hubei China 2020-03-13 67786.0 3062.0 China/Hubei 67781.0 5.0
4876 7222 Hubei China 2020-03-14 67790.0 3075.0 China/Hubei 67786.0 4.0
4877 7223 Hubei China 2020-03-15 67794.0 3085.0 China/Hubei 67790.0 4.0
4878 7224 Hubei China 2020-03-16 67798.0 3099.0 China/Hubei 67794.0 4.0
4879 7225 Hubei China 2020-03-17 67799.0 3111.0 China/Hubei 67798.0 1.0
4880 7226 Hubei China 2020-03-18 67800.0 3122.0 China/Hubei 67799.0 1.0
4881 7227 Hubei China 2020-03-19 67800.0 3130.0 China/Hubei 67800.0 0.0
4882 7228 Hubei China 2020-03-20 67800.0 3133.0 China/Hubei 67800.0 0.0
4883 7229 Hubei China 2020-03-21 67800.0 3139.0 China/Hubei 67800.0 0.0
4884 7230 Hubei China 2020-03-22 67800.0 3153.0 China/Hubei 67800.0 0.0
4885 7231 Hubei China 2020-03-23 67800.0 3153.0 China/Hubei 67800.0 0.0
4886 7232 Hubei China 2020-03-24 67801.0 3160.0 China/Hubei 67800.0 1.0
4887 7233 Hubei China 2020-03-25 67801.0 3163.0 China/Hubei 67801.0 0.0
4888 7234 Hubei China 2020-03-26 67801.0 3169.0 China/Hubei 67801.0 0.0
4889 7235 Hubei China 2020-03-27 67801.0 3174.0 China/Hubei 67801.0 0.0
4890 7236 Hubei China 2020-03-28 67801.0 3177.0 China/Hubei 67801.0 0.0
4891 7237 Hubei China 2020-03-29 67801.0 3182.0 China/Hubei 67801.0 0.0
4892 7238 Hubei China 2020-03-30 67801.0 3186.0 China/Hubei 67801.0 0.0
4893 7239 Hubei China 2020-03-31 67801.0 3187.0 China/Hubei 67801.0 0.0
4894 7240 Hubei China 2020-04-01 67802.0 3193.0 China/Hubei 67801.0 1.0
4895 7241 Hubei China 2020-04-02 67802.0 3199.0 China/Hubei 67802.0 0.0

Sigmoid Fitting - When will it converge?

In [76]:
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 [77]:
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 [78]:
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 [79]:
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 134274.90539876837 0.18038109458089624 23.56351555931448
target_country Spain
train_start_date 2020-03-09 00:00:00 offset 47.0 params 149842.09476495802 0.22185966940132248 19.276158543385034
target_country US
train_start_date 2020-03-11 00:00:00 offset 49.0 params 377130.04955405707 0.263807433537375 19.91961020999054
target_country France
train_start_date 2020-03-08 00:00:00 offset 46.0 params 104733.01260334795 0.18082094975904212 23.189425201098807
target_country China
train_start_date 2020-01-25 00:00:00 offset 3.0 params 81292.84436281704 0.21892024500272367 14.810135195103342
target_country Iran
train_start_date 2020-03-02 00:00:00 offset 40.0 params 170812.94187485485 0.0932856958259296 40.32988794897995
target_country United Kingdom
train_start_date 2020-03-14 00:00:00 offset 52.0 params 76014.20645200516 0.20847576961325476 20.07147663629319
target_country Netherlands
train_start_date 2020-03-15 00:00:00 offset 53.0 params 20656.87024402798 0.20784433504435518 13.632511557476867
target_country Germany
train_start_date 2020-03-08 00:00:00 offset 46.0 params 110412.9788090112 0.21476093371040775 19.830734408962012
target_country Belgium
train_start_date 2020-03-16 00:00:00 offset 54.0 params 23570.51221610725 0.22861188658904424 14.194034001346093
target_country Switzerland
train_start_date 2020-03-13 00:00:00 offset 51.0 params 20838.903004440224 0.23371623022640925 11.715727049368564
target_country Turkey
train_start_date 2020-03-22 00:00:00 offset 60.0 params 25727.339753024877 0.3641171410920399 8.702056381642212
target_country Brazil
train_start_date 2020-03-21 00:00:00 offset 59.0 params 145660.51578579872 0.15069819520778582 31.044195216893975
target_country Sweden
train_start_date 2020-03-15 00:00:00 offset 53.0 params 114575.61678967837 0.09777242351702223 48.676452832553025
target_country Portugal
train_start_date 2020-03-20 00:00:00 offset 58.0 params 13256.122832577861 0.2478861168191877 9.987850687365182
target_country Indonesia
WARNING: the data is not enough, use 7 more days...
train_start_date 2020-03-20 00:00:00 offset 58.0 params 2721.078893619251 0.19280301226205065 9.602876711058407
target_country Korea, South
train_start_date 2020-02-26 00:00:00 offset 35.0 params 9195.50969096071 0.22721596714218892 5.770845671887907
target_country Austria
train_start_date 2020-03-16 00:00:00 offset 54.0 params 12743.343226217932 0.2561348280075197 9.585060207166421
target_country Canada
train_start_date 2020-03-21 00:00:00 offset 59.0 params 22790.56441665379 0.2201282368757748 12.284207141617536
target_country Denmark
train_start_date 2020-03-17 00:00:00 offset 55.0 params 68284.55014395516 0.08068597600085278 52.08770586988402
target_country Ecuador
train_start_date 2020-03-24 00:00:00 offset 62.0 params 54441.65172137186 0.12031974756335376 32.507584570460125
target_country Romania
train_start_date 2020-03-26 00:00:00 offset 64.0 params 3446.2603498632607 0.30551536206318575 2.7769659135254
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 9704.392150776921 0.2146755036225501 16.47020854573623
target_country Ireland
train_start_date 2020-03-23 00:00:00 offset 61.0 params 5731.942525350116 0.20544762452702056 6.748670597226258
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 133612.32220433187 0.17928470302002242 33.44080349111839
target_country Japan
train_start_date 2020-03-21 00:00:00 offset 59.0 params 30943.072763872064 0.07884036598942054 43.49924023276361
target_country Dominican Republic
WARNING: the data is not enough, use 7 more days...
train_start_date 2020-03-24 00:00:00 offset 62.0 params 2318.824122597055 0.2488582424144557 7.363737107956261
target_country Poland
train_start_date 2020-03-25 00:00:00 offset 63.0 params 8776.543203445985 0.1594133547458467 12.40117285596989
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 17007.73449684526 0.14548767462963597 25.374712004170846
target_country Greece
WARNING: the data is not enough, use 7 more days...
train_start_date 2020-03-21 00:00:00 offset 59.0 params 3499.964705021285 0.11685986414696668 14.212231610775621
In [80]:
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 [81]:
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 116049.24155287043 0.19624681011167844 21.932582778056542
target_country Spain
train_start_date 2020-03-09 00:00:00 offset 47.0 params 192203.04968296102 0.2129300135798599 21.02688344062311
target_country US
train_start_date 2020-03-11 00:00:00 offset 49.0 params 175903.39785457114 0.338069097245396 15.372948385735894
target_country France
train_start_date 2020-03-08 00:00:00 offset 46.0 params 67921.51988672806 0.20198557301395045 19.39543972666654
target_country China
train_start_date 2020-01-25 00:00:00 offset 3.0 params 81086.5293338359 0.220185174529743 14.776136840950773
target_country Iran
train_start_date 2020-03-02 00:00:00 offset 40.0 params 38402.01626546004 0.14488571824683014 17.41062673806626
target_country United Kingdom
train_start_date 2020-03-14 00:00:00 offset 52.0 params 42137.89189351145 0.22459806796556858 16.285794105250822
target_country Netherlands
train_start_date 2020-03-15 00:00:00 offset 53.0 params 19320.505055904214 0.2051013296094445 13.322081135305734
target_country Germany
train_start_date 2020-03-08 00:00:00 offset 46.0 params 66572.47231340845 0.260279025137504 15.827903641187973
target_country Belgium
train_start_date 2020-03-16 00:00:00 offset 54.0 params 30015.145744345522 0.19102417020707077 17.19878598145747
target_country Switzerland
train_start_date 2020-03-13 00:00:00 offset 51.0 params 16605.120360239885 0.28203138961302965 9.696849961907308
target_country Turkey
WARNING: the data is not enough, use 7 more days...
train_start_date 2020-03-15 00:00:00 offset 53.0 params 9545.406132441793 0.4118578975302187 12.308630121519005
target_country Brazil
WARNING: the data is not enough, use 7 more days...
train_start_date 2020-03-14 00:00:00 offset 52.0 params 3935.9736679556154 0.403829456458752 9.277566453944406
target_country Sweden
train_start_date 2020-03-15 00:00:00 offset 53.0 params 38498.51685417697 0.09843697515488181 36.92796178581892
target_country Portugal
WARNING: the data is not enough, use 7 more days...
train_start_date 2020-03-13 00:00:00 offset 51.0 params 7362.48111732354 0.2995018575985231 13.283864980656999
target_country Indonesia
WARNING: the data is not enough, use 7 more days...
train_start_date 2020-03-20 00:00:00 offset 58.0 params 3230.0614600281424 0.17832139100950412 11.375337559153472
target_country Korea, South
train_start_date 2020-02-26 00:00:00 offset 35.0 params 8713.265716047794 0.27724931492931626 5.195015920814226
target_country Austria
train_start_date 2020-03-16 00:00:00 offset 54.0 params 14804.892635313023 0.2430205592935853 10.671006449649685
target_country Canada
WARNING: the data is not enough, use 7 more days...
train_start_date 2020-03-14 00:00:00 offset 52.0 params 17489.353633782182 0.2696925100142364 16.44830809626875
target_country Denmark
train_start_date 2020-03-17 00:00:00 offset 55.0 params 13291.877938670688 0.08119834095720319 30.327523971351056
target_country Ecuador
WARNING: the data is not enough, use 7 more days...
train_start_date 2020-03-17 00:00:00 offset 55.0 params 1442.6996337367939 0.6013514789193184 4.896178822584779
target_country Romania
WARNING: the data is not enough, use 7 more days...
train_start_date 2020-03-19 00:00:00 offset 57.0 params 2404.1491186318435 0.28144206388305726 7.8989424826179935
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 1003.4804334044295 0.3415169253235475 2.4295133052429536
target_country Ireland
WARNING: the data is not enough, use 7 more days...
train_start_date 2020-03-16 00:00:00 offset 54.0 params 2897.0217229942755 0.3035941599109412 8.413039361837871
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 1317.7513918449376 0.25900916219303854 3.169070992874242
target_country Japan
WARNING: the data is not enough, use 7 more days...
train_start_date 2020-03-14 00:00:00 offset 52.0 params 15412.36218301316 0.051769198251980855 57.50580964244571
target_country Dominican Republic
WARNING: the data is not enough, use 7 more days...
train_start_date 2020-03-24 00:00:00 offset 62.0 params 2233.639833413091 0.27189566266912546 6.688592417155313
target_country Poland
WARNING: the data is not enough, use 7 more days...
train_start_date 2020-03-18 00:00:00 offset 56.0 params 2615.0891254687376 0.25068457690238144 8.558326387023605
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 3569.0160784938325 0.1950728552964126 10.441694103007038
target_country Greece
WARNING: the data is not enough, use 7 more days...
train_start_date 2020-03-21 00:00:00 offset 59.0 params 1233.604160988184 0.2383254971117365 1.0609162094303926
In [82]:
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 [ ]: