# score-covid-19-policy
[](https://codeocean.com/capsule/0675fd94-870e-4a3c-ab47-6685096bf16d/tree)
Taiwan's data from the dataset was removed by the provider.
WHO is now handling all COVID-19 data:
https://covid19.who.int/WHO-COVID-19-global-data.csv
Takefuji Y. (2022). scorecovid for scoring individual country COVID-19 policies in the world. Software impacts, 14, 100453. https://doi.org/10.1016/j.simpa.2022.100453
DOI: https://doi.org/10.24433/CO.9411531.v1
The paper entitled "Sustainable policy: Don't get infected and don't infect others"
was published in Journal of Hazardous Materials Advances.
https://doi.org/10.1016/j.hazadv.2022.100165
The paper was published in Healthcare Analytics (2021):
SCORECOVID: A Python Package Index for scoring the individual policies against COVID-19
https://doi.org/10.1016/j.health.2021.100005
The paper was published in Journal of Infection and Public Health (2021):
Technological forecasting plays a key role in mitigating the pandemic
https://doi.org/10.1016/j.jiph.2021.09.010
scorecovid has been downloaded by 16757 times worldwide as of Feb.22, 2023.
You may create a file called countries for scoring individual policies of countries.
Country names must be separated by commas.
VSL_data.csv was created by Mr. Kiuchi using data of the following paper:
Viscusi, W., & Masterman, C. (2017). Income Elasticities and Global Values of a Statistical Life. Journal of Benefit-Cost Analysis, 8(2), 226-250. doi:10.1017/bca.2017.12
Taiwan VSL: https://www.cier.edu.tw/public/Data/2018-5.pdf
Taiwan GNI: https://eng.stat.gov.tw/point.asp?index=1
<pre>
$ cat countries
Iceland,South Korea,India,Brazil,France,New Zealand,Taiwan,Sweden,Japan,United States,Canada,United Kingdom,Israel
</pre>
# How to install and run scorecovid
<pre>
$ pip install scorecovid
or
$ pip install scorecovid --force-reinstall --no-cache-dir --no-binary :all:
$ scorecovid
score is created in result.csv
date is 2022-03-19
deaths population score
country
New Zealand 151 4.82 31.3
Taiwan 853 23.82 35.8
Japan 27056 126.48 213.9
Australia 5725 25.50 224.5
South Korea 12428 51.27 242.4
Iceland 91 0.34 267.6
India 516479 1380.00 374.3
Canada 37204 37.74 985.8
Israel 10419 8.66 1203.1
Germany 126920 83.78 1514.9
Sweden 18053 10.10 1787.4
France 140972 65.27 2159.8
United Kingdom 163658 67.89 2410.6
United States 971087 331.00 2933.8
Brazil 657389 212.56 3092.7
</pre>
# Background
There are ways to mitigate pandemics and prevent infections.
We need to learn good policies from countries with high scores.
Scoring tells us the performance of health policies in individual
countries that are coping well with pandemics and those that are not.
# How to handle errors
<pre>
For example, "ImportError: lxml not found, please install it"
$ pip install lxml
</pre>
# How scorecovid can score indivisual policies against covid-19?
Scoring of individual policies against covid-19 is calculated by dividing the total number of deaths due to covid-19 by the population (in millions).
The most recent values for total number of deaths and population are scraped from the Web sites.
# Exercises for students
1. Calculate economic loss of a country due to the total number of deaths due to COVID-19 using a value of statistical life (VSI).
2. Add two columns to the result of scorecovid: VSI and economic loss.
# scorecovid.py
$ cat scorecovid.py
<pre>
import requests,re
import pandas as pd
def main():
url='https://www.worldometers.info/world-population/population-by-country/'
print('scraping population...')
page=requests.get(url)
df = pd.read_html(page.text)[0]
df.columns.values[1]='Country'
df.columns.values[2]='Population'
#df = pd.read_html(page.text,flavor='html5lib')[0]
df.to_csv('pop.csv')
print('pop.csv was created')
print('downloading total_deaths.csv file')
import subprocess as sp
sp.call("wget https://github.com/owid/covid-19-data/raw/master/public/data/jhu/total_deaths.csv",shell=True)
p=pd.read_csv('total_deaths.csv')
date=p['date'][len(p)-1]
#
#from urllib.request import Request, urlopen
#url='https://www.worldometers.info/coronavirus/#nav-today/'
#print('scraping deaths informationi...')
#req = Request(url, headers={'User-Agent': 'Firefox/76.0.1'})
#page = re.sub(r'<.*?>', lambda g: g.group(0).upper(), urlopen(req).read().decode('utf-8') )
#df = pd.read_html(page)[0]
#df.to_csv('deaths.csv')
#print('deaths.csv was created')
#
print('countries file was read...')
d=open('countries').read().strip()
d=d.split(',')
print('scoring the following ',len(d),' countries...')
print(d)
dd=pd.DataFrame(
{
"country": d,
"deaths": range(len(d)),
"population": range(len(d)),
"score": range(len(d)),
})
pp=pd.read_csv('pop.csv')
print('calculating scores of countries\n')
print('score is created in result.csv')
print('date is ',date)
for i in d:
# print(p[i][len(p)-1])
dd.loc[dd.country==i,'deaths']=int(p[i][len(p)-1])
# print(pp.loc[pp.Country==i,'Population'])
dd.loc[dd.country==i,'population']=int(pp.loc[pp.Country==i,'Population']/1000000)
dd.loc[dd.country==i,'score']=int(dd.loc[dd.country==i,'deaths']/dd.loc[dd.country==i,'population'])
dd=dd.sort_values(by=['score'])
dd.to_csv('result.csv',index=False)
dd=pd.read_csv('result.csv',index_col=0)
print(dd)
sp.call("rm total_deaths.csv pop.csv",shell=True)
if __name__ == "__main__":
main()
</pre>
Raw data
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"description": "# score-covid-19-policy\n[](https://codeocean.com/capsule/0675fd94-870e-4a3c-ab47-6685096bf16d/tree)\n\nTaiwan's data from the dataset was removed by the provider.\n\nWHO is now handling all COVID-19 data:\n\nhttps://covid19.who.int/WHO-COVID-19-global-data.csv\n\nTakefuji Y. (2022). scorecovid for scoring individual country COVID-19 policies in the world. Software impacts, 14, 100453. https://doi.org/10.1016/j.simpa.2022.100453\n\nDOI: https://doi.org/10.24433/CO.9411531.v1\n\nThe paper entitled \"Sustainable policy: Don't get infected and don't infect others\"\nwas published in Journal of Hazardous Materials Advances.\n\nhttps://doi.org/10.1016/j.hazadv.2022.100165\n\nThe paper was published in Healthcare Analytics (2021):\nSCORECOVID: A Python Package Index for scoring the individual policies against COVID-19\n\nhttps://doi.org/10.1016/j.health.2021.100005\n\nThe paper was published in Journal of Infection and Public Health (2021):\nTechnological forecasting plays a key role in mitigating the pandemic\n\nhttps://doi.org/10.1016/j.jiph.2021.09.010\n\nscorecovid has been downloaded by 16757 times worldwide as of Feb.22, 2023.\n\nYou may create a file called countries for scoring individual policies of countries.\n\nCountry names must be separated by commas.\n\nVSL_data.csv was created by Mr. Kiuchi using data of the following paper:\nViscusi, W., & Masterman, C. (2017). Income Elasticities and Global Values of a Statistical Life. Journal of Benefit-Cost Analysis, 8(2), 226-250. doi:10.1017/bca.2017.12\n\nTaiwan VSL: https://www.cier.edu.tw/public/Data/2018-5.pdf\n\nTaiwan GNI: https://eng.stat.gov.tw/point.asp?index=1\n\n<pre>\n$ cat countries\nIceland,South Korea,India,Brazil,France,New Zealand,Taiwan,Sweden,Japan,United States,Canada,United Kingdom,Israel\n</pre>\n\n# How to install and run scorecovid\n<pre>\n$ pip install scorecovid\nor\n$ pip install scorecovid --force-reinstall --no-cache-dir --no-binary :all:\n\n$ scorecovid\nscore is created in result.csv\ndate is 2022-03-19\n deaths population score\ncountry\nNew Zealand 151 4.82 31.3\nTaiwan 853 23.82 35.8\nJapan 27056 126.48 213.9\nAustralia 5725 25.50 224.5\nSouth Korea 12428 51.27 242.4\nIceland 91 0.34 267.6\nIndia 516479 1380.00 374.3\nCanada 37204 37.74 985.8\nIsrael 10419 8.66 1203.1\nGermany 126920 83.78 1514.9\nSweden 18053 10.10 1787.4\nFrance 140972 65.27 2159.8\nUnited Kingdom 163658 67.89 2410.6\nUnited States 971087 331.00 2933.8\nBrazil 657389 212.56 3092.7\n\n</pre>\n\n# Background\nThere are ways to mitigate pandemics and prevent infections.\nWe need to learn good policies from countries with high scores.\nScoring tells us the performance of health policies in individual \ncountries that are coping well with pandemics and those that are not.\n\n# How to handle errors\n<pre>\nFor example, \"ImportError: lxml not found, please install it\"\n$ pip install lxml\n\n</pre>\n\n# How scorecovid can score indivisual policies against covid-19?\nScoring of individual policies against covid-19 is calculated by dividing the total number of deaths due to covid-19 by the population (in millions).\nThe most recent values for total number of deaths and population are scraped from the Web sites.\n\n# Exercises for students\n1. Calculate economic loss of a country due to the total number of deaths due to COVID-19 using a value of statistical life (VSI).\n\n2. Add two columns to the result of scorecovid: VSI and economic loss.\n\n# scorecovid.py\n$ cat scorecovid.py\n<pre>\nimport requests,re\nimport pandas as pd\n\ndef main():\n url='https://www.worldometers.info/world-population/population-by-country/'\n print('scraping population...')\n page=requests.get(url)\n df = pd.read_html(page.text)[0]\n df.columns.values[1]='Country'\n df.columns.values[2]='Population'\n #df = pd.read_html(page.text,flavor='html5lib')[0]\n df.to_csv('pop.csv')\n print('pop.csv was created')\n\n print('downloading total_deaths.csv file')\n import subprocess as sp\n sp.call(\"wget https://github.com/owid/covid-19-data/raw/master/public/data/jhu/total_deaths.csv\",shell=True)\n p=pd.read_csv('total_deaths.csv')\n date=p['date'][len(p)-1]\n\n#\n#from urllib.request import Request, urlopen\n#url='https://www.worldometers.info/coronavirus/#nav-today/'\n#print('scraping deaths informationi...')\n#req = Request(url, headers={'User-Agent': 'Firefox/76.0.1'})\n#page = re.sub(r'<.*?>', lambda g: g.group(0).upper(), urlopen(req).read().decode('utf-8') )\n#df = pd.read_html(page)[0]\n#df.to_csv('deaths.csv')\n#print('deaths.csv was created')\n#\n\n\n print('countries file was read...')\n d=open('countries').read().strip()\n d=d.split(',')\n print('scoring the following ',len(d),' countries...')\n print(d)\n\n dd=pd.DataFrame(\n {\n \"country\": d,\n \"deaths\": range(len(d)),\n \"population\": range(len(d)),\n \"score\": range(len(d)),\n })\n\n pp=pd.read_csv('pop.csv')\n print('calculating scores of countries\\n')\n print('score is created in result.csv')\n print('date is ',date)\n\n for i in d:\n # print(p[i][len(p)-1])\n dd.loc[dd.country==i,'deaths']=int(p[i][len(p)-1])\n # print(pp.loc[pp.Country==i,'Population'])\n dd.loc[dd.country==i,'population']=int(pp.loc[pp.Country==i,'Population']/1000000)\n dd.loc[dd.country==i,'score']=int(dd.loc[dd.country==i,'deaths']/dd.loc[dd.country==i,'population'])\n dd=dd.sort_values(by=['score'])\n dd.to_csv('result.csv',index=False)\n dd=pd.read_csv('result.csv',index_col=0)\n print(dd)\n sp.call(\"rm total_deaths.csv pop.csv\",shell=True)\n\nif __name__ == \"__main__\":\n main()\n\n</pre>\n\n\n",
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