Script to calculate mortality rate of COVID-19 in different countries based on data from https://coronavirus.jhu.edu/map.html

covid_mortality.py

#!/usr/bin/env python3
# Little script to calculate the mortality rate of COVID-19 in different
# countries based on data from https://coronavirus.jhu.edu/map.html

# I extracted this from firefox dev tools. Returns country data as JSON
'''
curl 'https://services9.arcgis.com/N9p5hsImWXAccRNI/arcgis/rest/services/Z7biAeD8PAkqgmWhxG2A/FeatureServer/1/query?f=json&where=(Confirmed%20%3E%200)%20AND%20(Deaths%3E0)&returnGeometry=false&spatialRel=esriSpatialRelIntersects&outFields=*&orderByFields=Deaths%20desc%2CCountry_Region%20asc%2CProvince_State%20asc&outSR=102100&resultOffset=0&resultRecordCount=250&cacheHint=true' -H 'User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:75.0) Gecko/20100101 Firefox/75.0' -H 'Accept: */*' -H 'Accept-Language: en-US,en;q=0.5' --compressed -H 'Origin: https://www.arcgis.com' -H 'Connection: keep-alive' -H 'Referer: https://www.arcgis.com/apps/opsdashboard/index.html' -H 'TE: Trailers'
'''

# Build the same request in python
from urllib import request
url = 'https://services9.arcgis.com/N9p5hsImWXAccRNI/arcgis/rest/services/Z7biAeD8PAkqgmWhxG2A/FeatureServer/1/query?f=json&where=(Confirmed%20%3E%200)%20AND%20(Deaths%3E0)&returnGeometry=false&spatialRel=esriSpatialRelIntersects&outFields=*&orderByFields=Deaths%20desc%2CCountry_Region%20asc%2CProvince_State%20asc&outSR=102100&resultOffset=0&resultRecordCount=250&cacheHint=true'
req = request.Request(url)
req.add_header('User-Agent', 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:75.0) Gecko/20100101 Firefox/75.0')
req.add_header('Accept', '*/*')
req.add_header('Origin', 'https://www.arcgis.com')
req.add_header('Referer', 'https://www.arcgis.com/apps/opsdashboard/index.html')
req.add_header('TE', 'Trailers')

# Get the data from the site
resp = request.urlopen(req)
data_string = resp.read().decode('utf-8')

# Load JSON data into dict
import json
data = json.loads(data_string)

# See all data for countries
# [x['attributes'] for x in data['features']]

# Get just the values we need from all states into a list
ccds = [(y['Country_Region'], y['Confirmed'], y['Deaths']) for y in (x['attributes'] for x in data['features'])]

# make defaultdicts for confirmed cases and deaths
# (defaultsdict(int) makes it so you can add to an item that does not exist and
# it will treat it as 0 instead of throwing an exception
from collections import defaultdict
confirmed = defaultdict(int)
deaths = defaultdict(int)

# Sum up state numbers for the countries
for cntry, cnf, dd in ccds:
    confirmed[cntry] += cnf
    deaths[cntry] += dd

# calculate mortality rate from confirmed and deaths
mortality_rate = defaultdict(int)
# doesn't matter which dicts keys we use, we just need the country names
for cntry in confirmed.keys():
    mortality_rate[cntry] = 100.0 *deaths[cntry] / confirmed[cntry]

# Turn dict to list to sort it
rates = list(mortality_rate.items())

# Print results
for y in list(enumerate(sorted(rates, key=lambda x: x[1]), 1)):
    print(y)

output_on_2020-03-21.py

(1, ('Israel', 0.11325028312570781))
(2, ('Thailand', 0.24330900243309003))
(3, ('Austria', 0.2627627627627628))
(4, ('Slovenia', 0.2932551319648094))
(5, ('Bahrain', 0.33557046979865773))
(6, ('Malaysia', 0.33812341504649196))
(7, ('Germany', 0.34774209128229894))
(8, ('Norway', 0.3510531594784353))
(9, ('Russia', 0.3952569169960474))
(10, ('Ireland', 0.43923865300146414))
(11, ('Singapore', 0.46296296296296297))
(12, ('Panama', 0.5))
(13, ('Pakistan', 0.5617977528089888))
(14, ('Croatia', 0.5952380952380952))
(15, ('Serbia', 0.6711409395973155))
(16, ('Slovakia', 0.7299270072992701))
(17, ('Portugal', 0.9375))
(18, ('Switzerland', 0.9487976443644691))
(19, ('Sweden', 0.9656004828002414))
(20, ('Denmark', 0.9803921568627451))
(21, ('Mexico', 0.9852216748768473))
(22, ('Cruise Ship', 1.1235955056179776))
(23, ('Brazil', 1.1258955987717503))
(24, ('Poland', 1.1389521640091116))
(25, ('Korea, South', 1.1592226389362428))
(26, ('Luxembourg', 1.1940298507462686))
(27, ('Canada', 1.2))
(28, ('Taiwan*', 1.3071895424836601))
(29, ('Australia', 1.3307984790874525))
(30, ('Turkey', 1.3432835820895523))
(31, ('India', 1.3605442176870748))
(32, ('US', 1.4183623370247123))
(33, ('United Arab Emirates', 1.4285714285714286))
(34, ('Moldova', 1.5151515151515151))
(35, ('Peru', 1.520912547528517))
(36, ('Ecuador', 1.6431924882629108))
(37, ('Costa Rica', 1.7699115044247788))
(38, ('Tunisia', 1.8518518518518519))
(39, ('Argentina', 1.8987341772151898))
(40, ('Greece', 2.0202020202020203))
(41, ('Bulgaria', 2.112676056338028))
(42, ('Lebanon', 2.1390374331550803))
(43, ('Azerbaijan', 2.272727272727273))
(44, ('Belgium', 2.380106571936057))
(45, ('Albania', 2.6315789473684212))
(46, ('Dominican Republic', 2.7777777777777777))
(47, ('Egypt', 2.807017543859649))
(48, ('Burkina Faso', 3.125))
(49, ('Martinique', 3.125))
(50, ('Japan', 3.4756703078450846))
(51, ('Morocco', 3.488372093023256))
(52, ('Netherlands', 3.570236903570237))
(53, ('France', 3.6118468576932337))
(54, ('Hungary', 3.883495145631068))
(55, ('China', 4.052021037188079))
(56, ('Congo (Kinshasa)', 4.3478260869565215))
(57, ('United Kingdom', 4.465629703963874))
(58, ('Cuba', 4.761904761904762))
(59, ('Spain', 5.418932765823284))
(60, ('Paraguay', 5.555555555555555))
(61, ('Philippines', 6.188925081433225))
(62, ('Jamaica', 6.25))
(63, ('Mauritius', 7.142857142857143))
(64, ('Ukraine', 7.317073170731708))
(65, ('Iran', 7.5497331392527895))
(66, ('Bangladesh', 8.0))
(67, ('Iraq', 8.173076923076923))
(68, ('Guatemala', 8.333333333333334))
(69, ('Indonesia', 8.444444444444445))
(70, ('Italy', 8.574892069500862))
(71, ('San Marino', 9.722222222222221))
(72, ('Algeria', 11.702127659574469))
(73, ('Guyana', 14.285714285714286))
(74, ('Gabon', 25.0))
(75, ('Sudan', 50.0))