COVID-19 Data Sources and Analysis

jupyter feeding charts grafics https://dasn.herokuapp.com/covid19

this work in references:

Source: World-cases, Age, Gender, Travel History

Data Feed: https://docs.google.com/spreadsheets/d/1itaohdPiAeniCXNlntNztZ_oRvjh0HsGuJXUJWET008/edit#gid=0
[] B Xu, M Kraemer, Open access epidemiological data from the COVID-19 outbreak, www.thelancet.com › article › PIIS1473-3099(20)30119-5 › Feb 19, 2020
Curators: University of Oxford , Bo Xu,Bernardo Gutierrez,Sumiko Mekaru,Kara Sewalk,Alyssa Loskill,Lin Wang,Emily Cohn,Sarah Hill,Alexander Zarebski,Sabrina Li,Chieh-His Wu,Erin Hulland,Julia Morgan,Samuel Scarpino,John Brownstein,Oliver Pybus,David Pigott,Moritz Kraemer

Source: World Stats Evolution per Coutry & Status

Source: China Summary Live Update

Data Feed, Isaac Lin: API format
Infectious Estimation of R0, Y. Wang
Li, Q., Guan, X., et al. (2020, January 29). Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia. The New England Journal of Medicine.

Note to contributors: aiming to add links closest to the original source or curation, (to avoid duplicated sources)

may16

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" bz
data-datasets="693,649,539,626,346,268,210,627,494,428,384,468"uk
data-datasets="236,197,257,199,193,112,108,353,294,257,290,278"mx
data-datasets="236,369,274,243,194,165,179,172,195,262,242,153"it
data-datasets="100,89,94,87,100,75,72,96,112,98,125,131"pe
data-datasets="95,86,88,98,104,88,94,107,96,93,113,119"ru
data-datasets="127,92,104,96,116,111,82,121,136,98,104,118"in
data-datasets="185,244,213,229,179,143,123,176,184,217,138,104"es
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"be
data-datasets="0,282,117,118,39,20,92,77,123,23,13,41"de
data-datasets="59,64,57,48,50,47,55,53,58,55,48,41"tk
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 ir

brief summary

 ##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

import pandas as pd
df=pd.read_csv('over50.csv')
df.set_index('Country/Region',inplace=True)  # .T
type(df)
cols20 = df.columns[-13:].tolist()
idx = cols20
df.sort_values(by=[df.columns[-1]],ascending=False)[cols20]
for i in range(1,13):
         df0[df0.columns[-i]] =    (df0[df.columns[-i]]-df0[df0.columns[-i-1]]) # casualties last day

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) )

=====================================================================================

  curl -kOL https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv   #1
  
  <time_series_covid19_deaths_global.csv  perl -pe 's|(\d)/(\d)/20|200${1}0${2}|g; s|(\d)/(\d\d)/20|200${1}${2}|g' >dt #2
  
  nf=$( grep , -o <(head -1 time_series_covid19_deaths_global.csv) | wc -l ) #3
  
  COLS=60 ;cut -d, -f2,$(($nf-$COLS+2))-$(($nf+1)) dt | sort -k$((COLS+1)) -nr -t, | head -21 > dt.csv #4
  
import pandas as pd
df0=pd.read_csv('dt.csv')
df0.set_index('Country/Region',inplace=True)  # .T
type(df0)
#df.sort_values(by=[df.columns[-1]],ascending=False)
for i in range(1,13):
   df0[df0.columns[-i]] = df0[df0.columns[-i]] - df0[df0.columns[-i-1]]
st='\ndata-datasets="'
st='\ns/num%.0f/"'
print(st)
print(  (st % y).join(','.join('%.0f' %x for x in y) for y in df9.values) )

  now sed

notes:

cp /c/Users/ma*/Down*/JH-calculate-daily*13?.ipynb JH-calculate-daily.ipynb
 ls -ltr /c/Users/ma*/Down*/*nb |tail
cp /c/Users/ma*/Down*/updates*6?.ipynb updates.ipynb

infchg/COVID-19

COVID-19 Data Sources and Analysis

Source: World-cases, Age, Gender, Travel History

Source: World Stats Evolution per Coutry & Status

Source: China Summary Live Update