Browse the project source code in my github repository: https://github.com/rnowotniak/covid19-analysis

covid19 global spread analysis

Coronavirus spread analysis

Contact: Robert Nowotniak «rnowotniak@gmail.com»

Last updated: Apr 5th, 2020

Data sources from John Hopkins University:

https://github.com/CSSEGISandData/COVID-19
Global confirmed time series: https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv
Global deaths time series: https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv
Global recovered time series: https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv

Tools to reproduce and to rerun this analysis:

docker pull jupyter/scipy-notebook
docker run -p 8888:8888 -v “$PWD:/home/jovyan/work” –rm jupyter/scipy-notebook

import pandas as pd, numpy as np
import matplotlib.pyplot as plt, matplotlib as mpl
plt.rcParams['figure.figsize'] = [12,8]

List of countries to include in the analysis:

COUNTRIES = ['United Kingdom', 'Poland','China','US','Italy','Canada', 'Spain', 'Germany']

Load data from John Hopkins University:

CONFIRMED_URL='https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'
DEATHS_URL='https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv'
RECOVERED_URL='https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv'

confirmed = pd.read_csv(CONFIRMED_URL)
deaths    = pd.read_csv(DEATHS_URL)
recovered = pd.read_csv(RECOVERED_URL)

confirmed = confirmed.groupby(by='Country/Region').sum().transpose()[2:].applymap(lambda x: int(x))
deaths    =    deaths.groupby(by='Country/Region').sum().transpose()[2:].applymap(lambda x: int(x))
recovered = recovered.groupby(by='Country/Region').sum().transpose()[2:].applymap(lambda x: int(x))

confirmed.tail(10)[COUNTRIES]

Country/Region	United Kingdom	Poland	China	US	Italy	Canada	Spain	Germany
3/26/20	11812	1221	81782	83836	80589	4042	57786	43938
3/27/20	14745	1389	81897	101657	86498	4682	65719	50871
3/28/20	17312	1638	81999	121478	92472	5576	73235	57695
3/29/20	19780	1862	82122	140886	97689	6280	80110	62095
3/30/20	22453	2055	82198	161807	101739	7398	87956	66885
3/31/20	25481	2311	82279	188172	105792	8527	95923	71808
4/1/20	29865	2554	82361	213372	110574	9560	104118	77872
4/2/20	34173	2946	82432	243453	115242	11284	112065	84794
4/3/20	38689	3383	82511	275586	119827	12437	119199	91159
4/4/20	42477	3627	82543	308850	124632	12978	126168	96092

Total confirmed cases (cumulative)

axs = confirmed[COUNTRIES].plot(subplots=True,layout=(4,2),figsize=(16,24),
                               title=COUNTRIES, sharex=False, rot=45, grid=True)
for row in axs:
    for ax in row:
        ax.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))

png

Total confirmed cases (cumulative) - one chart

ax = confirmed[COUNTRIES].plot(grid=True)
ax.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))
plt.title('Total confirmed cases')

Text(0.5, 1.0, 'Total confirmed cases')

png

Active cases

axs = (confirmed[COUNTRIES] - (recovered[COUNTRIES] + deaths[COUNTRIES])).\
plot(subplots=True,layout=(4,2),figsize=(16,24), title=COUNTRIES, sharex=False, rot=45, grid=True)
for row in axs:
    for ax in row:
        ax.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))

png

Active cases - one chart

ax = (confirmed[COUNTRIES] - (recovered[COUNTRIES] + deaths[COUNTRIES])).plot(grid=True)
ax.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))

png

Logarithmic scale charts

Total confirmed cases (cumulative) - log scale

axs = confirmed[COUNTRIES].plot(subplots=True,layout=(4,2),figsize=(16,24),
                               title=COUNTRIES, sharex=False, rot=45, grid=True, logy=True)
for row in axs:
    for ax in row:
        ax.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))

png

Total confirmed cases (cumulative) - one chart, log scale

ax = confirmed[COUNTRIES].plot(grid=True,logy=True)
ax.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))
plt.title('Total confirmed cases')

Text(0.5, 1.0, 'Total confirmed cases')

png

Active cases - log scale

axs = (confirmed[COUNTRIES] - (recovered[COUNTRIES] + deaths[COUNTRIES])).\
plot(subplots=True,layout=(4,2),figsize=(16,24), title=COUNTRIES, sharex=False, rot=45, grid=True,logy=True)
for row in axs:
    for ax in row:
        ax.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))

png

Active cases - one chart, log scale

ax = (confirmed[COUNTRIES] - (recovered[COUNTRIES] + deaths[COUNTRIES])).plot(grid=True,logy=True)
ax.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))

png