COVID-19 Daily Analysis Table¶

This-document: https://github.com/infchg/COVID-19/JH-calculate-daily.ipynb
HOW-TO-USE: see results or re-calculate on your own Notebook (Jupyter, Mybinder, Azure, ... )
RESULTS: WARNING ON COUNTRIES BY COVID DAILY DEATH RATES
- rankings and trends on the worst daily deaths by country evaluating last 7 days
References:
- Web charts: https://dasn.herokuapp.com/covidzoom and https://dasn.herokuapp.com/covid19
- wolfram & azure notebooks
- Data-source: John Hopkins, Stats by Country, https://github.com/CSSEGISandData/
LOOKING FOR REGIONAL SOURCES (Estado, Staat, CCAA, Canton, ...) IN ORDER TO CALCULATE BY AREA

In [16]:

try:  ## This table sumarizes the daily deaths by country (calculations below)   %store df9  
    display(df9.transpose().tail(3) )
    print("worst daily deaths by country evaluating last 7 days:")
    display(df9.transpose().tail(9).head(9).mean(axis=0).sort_values(ascending=False).head(7) )
except NameError:
    print("Click Run on this cell only after the RUN ALL completes all calculations")

Country/Region	US	Brazil	United Kingdom	Mexico	Italy	Peru	Russia	India	Spain	Ecuador	Canada	Belgium	Germany	Turkey	Canada	Iran
5/14/20	1779	759	428	257	262	98	93	98	217	4	131	60	23	55	32	71
5/15/20	1632	963	384	290	242	125	113	104	138	256	50	56	13	48	24	48
5/16/20	1224	700	468	278	153	131	119	118	104	94	82	46	41	41	37	35

worst daily deaths by country evaluating last 7 days:

Country/Region
US                1454.666667
Brazil             719.111111
United Kingdom     427.888889
Mexico             231.555556
Italy              200.555556
Spain              165.888889
Ecuador            114.888889
dtype: float64

How is this daily Calculus performed : https://github.com/infchg/COVID-19 ¶

calculations in iPython at https://github.com/infchg/COVID-19/JH-calculate-daily.ipynb
- data source: John Hopkins https://github.com/CSSEGISandData/COVID-19/
- thanks to resources by http://Jupyter.org http://MyBinder.org http://Heroku.com and above
LOOKING FOR REGIONAL SOURCES (Estado, Staat, CCAA, Canton, ...) IN ORDER TO CALCULATE BY AREA

In [2]:

!pip install pandas seaborn
# initial installations 

WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip.
Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue.
To avoid this problem you can invoke Python with '-m pip' instead of running pip directly.
Collecting pandas
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In [3]:

 ##JH Source THIS IS THE SOURCE OF ALL CALCULATIONS IN THIS PAGE
! curl -OL https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv

  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   213  100   213    0     0   1704      0 --:--:-- --:--:-- --:--:--  1704
100 79863  100 79863    0     0   301k      0 --:--:-- --:--:-- --:--:-- 4306k

In [4]:

# ! egrep -e ',[0-9]{3,}$' -e ^Provi  -e ',[6-9].$' tim*dea*csv > over50.csv
! egrep -e ',[^,][^,][^,]+$' -e ^Provi   tim*dea*csv > over50.csv
import pandas as pd
df=pd.read_csv('over50.csv')
df.set_index('Country/Region',inplace=True)  # .T
type(df)

Out[4]:

pandas.core.frame.DataFrame

In [5]:

cols20 = df.columns[-13:].tolist()
idx = cols20 # cols21 = df.columns[-14:-1].tolist()
#display( )idx=['Country/Region']+cols20   #no need with set index
display(df.sort_values(by=[df.columns[-1]],ascending=False)[cols20] )

	5/4/20	5/5/20	5/6/20	5/7/20	5/8/20	5/9/20	5/10/20	5/11/20	5/12/20	5/13/20	5/14/20	5/15/20	5/16/20
Country/Region
US	68922	71064	73455	75662	77180	78795	79526	80682	82356	84119	85898	87530	88754
United Kingdom	28734	29427	30076	30615	31241	31587	31855	32065	32692	33186	33614	33998	34466
Italy	29079	29315	29684	29958	30201	30395	30560	30739	30911	31106	31368	31610	31763
Spain	25428	25613	25857	26070	26299	26478	26621	26744	26920	27104	27321	27459	27563
France	25168	25498	25772	25949	26192	26271	26341	26604	26951	27032	27381	27485	27483
...	...	...	...	...	...	...	...	...	...	...	...	...	...
Malaysia	105	106	107	107	107	108	108	109	109	111	112	112	113
Kuwait	40	40	42	44	47	49	58	65	75	82	88	96	107
Bulgaria	78	80	84	84	86	90	91	93	95	96	99	102	105
Luxembourg	96	96	98	100	100	101	101	101	102	103	103	104	104
Slovenia	97	98	99	99	100	101	102	102	102	103	103	103	103

68 rows × 13 columns

In [6]:

#df['LastDay']=df[df.columns[-1]]-df[df.columns[-2]] 
df0=df[idx].copy()  #()
type(df)
#for (coln, cold) in df.iteritems():
#    print(coln, '->', cold)
for i in range(1,13):
         df0[df0.columns[-i]] =    (df0[df.columns[-i]]-df0[df0.columns[-i-1]]) # casualties last day

COVID Daily deaths¶

May 9th warning on Brazil Mexico in addition to cited March-30th: US UK Italy Spain France

In [7]:

df0.pop( df0.columns[0] )   # 0 
df9=df0.sort_values(by=[df.columns[-1]],ascending=False).head(16)
display(df9) 

	5/5/20	5/6/20	5/7/20	5/8/20	5/9/20	5/10/20	5/11/20	5/12/20	5/13/20	5/14/20	5/15/20	5/16/20
Country/Region
US	2142	2391	2207	1518	1615	731	1156	1674	1763	1779	1632	1224
Brazil	571	650	602	827	639	467	530	808	779	759	963	700
United Kingdom	693	649	539	626	346	268	210	627	494	428	384	468
Mexico	236	197	257	199	193	112	108	353	294	257	290	278
Italy	236	369	274	243	194	165	179	172	195	262	242	153
Peru	100	89	94	87	100	75	72	96	112	98	125	131
Russia	95	86	88	98	104	88	94	107	96	93	113	119
India	127	92	104	96	116	111	82	121	136	98	104	118
Spain	185	244	213	229	179	143	123	176	184	217	138	104
Ecuador	0	49	36	50	13	410	18	182	7	4	256	94
Canada	118	112	121	94	61	142	85	118	89	131	50	82
Belgium	92	323	76	106	60	75	51	54	82	60	56	46
Germany	0	282	117	118	39	20	92	77	123	23	13	41
Turkey	59	64	57	48	50	47	55	53	58	55	48	41
Canada	59	55	47	58	61	25	37	64	31	32	24	37
Iran	63	78	68	55	48	51	45	48	50	71	48	35

In [8]:

print(','.join('Mr%.0f' %x for x in range(18,30+1))  ,'\ndata-datasets="')
print('"\ndata-datasets="'.join(','.join('%.0f' %x for x in y) for y in df9.values) )

Mr18,Mr19,Mr20,Mr21,Mr22,Mr23,Mr24,Mr25,Mr26,Mr27,Mr28,Mr29,Mr30 
data-datasets="
2142,2391,2207,1518,1615,731,1156,1674,1763,1779,1632,1224"
data-datasets="571,650,602,827,639,467,530,808,779,759,963,700"
data-datasets="693,649,539,626,346,268,210,627,494,428,384,468"
data-datasets="236,197,257,199,193,112,108,353,294,257,290,278"
data-datasets="236,369,274,243,194,165,179,172,195,262,242,153"
data-datasets="100,89,94,87,100,75,72,96,112,98,125,131"
data-datasets="95,86,88,98,104,88,94,107,96,93,113,119"
data-datasets="127,92,104,96,116,111,82,121,136,98,104,118"
data-datasets="185,244,213,229,179,143,123,176,184,217,138,104"
data-datasets="0,49,36,50,13,410,18,182,7,4,256,94"
data-datasets="118,112,121,94,61,142,85,118,89,131,50,82"
data-datasets="92,323,76,106,60,75,51,54,82,60,56,46"
data-datasets="0,282,117,118,39,20,92,77,123,23,13,41"
data-datasets="59,64,57,48,50,47,55,53,58,55,48,41"
data-datasets="59,55,47,58,61,25,37,64,31,32,24,37"
data-datasets="63,78,68,55,48,51,45,48,50,71,48,35

In [9]:

dxy=df0.transpose()
display(dxy.tail(4)) #pivot('Country/Region')

Country/Region	Afghanistan	Algeria	Argentina	Austria	Bangladesh	Belarus	Belgium	Bolivia	Bosnia and Herzegovina	Brazil	...	Slovenia	South Africa	Spain	Sweden	Switzerland	Turkey	Ukraine	United Arab Emirates	United Kingdom	US
5/13/20	5	7	10	1	19	4	82	14	3	779	...	1	13	184	147	3	58	14	3	494	1763
5/14/20	4	7	24	2	14	5	60	10	2	759	...	0	19	217	69	2	55	17	2	428	1779
5/15/20	17	7	3	2	15	5	56	12	6	963	...	0	9	138	117	6	48	20	2	384	1632
5/16/20	15	6	7	1	16	4	46	1	1	700	...	0	14	104	28	1	41	21	4	468	1224

4 rows × 68 columns

In [10]:

#Worst 7 countries in daily deaths last week 
df9.transpose().tail(8).head(7).mean(axis=0).sort_values(ascending=False).head(7)    #1 Average for each row :

Out[10]:

Country/Region
US                1478.571429
Brazil             706.428571
United Kingdom     393.857143
Mexico             229.571429
Italy              201.285714
Spain              165.714286
Ecuador            127.142857
dtype: float64

In [11]:

import seaborn as sns
%matplotlib inline
sns.heatmap(df0)#, annot=True

Out[11]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f3da041b890>

Daily casualties Heat Map¶

in order of daily casualties : Italy Spain US France UK Iran Netherlands

In [12]:

print( len(dxy.columns) ,dxy['Austria'].tail(20).mean() ) #   with ()   sns.heatmap(dxy)
drel=dxy.copy()
for r in drel.columns:
    #print (r,    drel[r].tail(5).mean() )
    drel[r] =  drel[r] / drel[r].tail(11).mean()
    
dinc=df0.copy()  #increase 
Days2 = dxy.tail(2).mean() 
Days4 = dxy.tail(5).head(2).mean() 
increaserate=round(Days2/Days4,1)
#display( increaserate )
   
for i in range(1,6):
         dinc[df0.columns[-i]] =    (df0[df0.columns[-i]]+df0[df0.columns[-i-1]])  /  (df0[df0.columns[-i-4]]+df0[df0.columns[-i-3]])  
        
sns.heatmap(dinc.transpose().tail(2))    

68 2.4166666666666665

Out[12]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f3d86f1e090>

In [13]:

print("By worst aspect due exponential increase: Sweden UK US Belgium Germany \n Strangely constant : Iran , Recovered: China ")
#drel.loc[['3/27/20']].transpose().sort_values(by=[df.columns[-1]],ascending=False)
drel.iloc[[-1]].transpose().sort_values(by=[df.columns[-1]],ascending=False)

By worst aspect due exponential increase: Sweden UK US Belgium Germany 
 Strangely constant : Iran , Recovered: China

Out[13]:

	5/16/20
Country/Region
Iraq	2.315789
Afghanistan	2.260274
Chile	2.034247
Morocco	2.000000
Kuwait	1.805970
...	...
Luxembourg	0.000000
Pakistan	0.000000
Norway	0.000000
France	-0.011083
China	NaN

68 rows × 1 columns

In [14]:

%matplotlib inline
#dxy.plot(xticks=dxy.index,yticks=dxy.Austria)

In [15]:

!pip install chart-studio urllib3 # plotly

WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip.
Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue.
To avoid this problem you can invoke Python with '-m pip' instead of running pip directly.
Collecting chart-studio
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Installing collected packages: retrying, plotly, chart-studio
Successfully installed chart-studio-1.1.0 plotly-4.7.1 retrying-1.3.3

In [ ]:

COVID-19 Daily Analysis Table¶

How is this daily Calculus performed : https://github.com/infchg/COVID-19¶

COVID Daily deaths¶

Daily casualties Heat Map¶

How is this daily Calculus performed : https://github.com/infchg/COVID-19 ¶