covid stats 2023

covid2023stats.py

def calculate(country,pop,c,d): 
  print(f"{country}: pop {pop}mil, cases {c}mil, deaths {d}")
  print(f"death per popul %{((d/(pop*1000000))*100):.2f}")
  print(f"death per cases %{((d/(c*1000000))*100):.2f}")

"""
Example output:
  sweden: pop 10.35mil, cases 2.523404mil, deaths 19100
  death per popul %0.18
  death per cases %0.76

Mostly "per cases" can be ignored as this is just an indicator of 
how much the population was testing. In places like the USA where
there was little or no infrastructure for testing the deaths per 
cases will be a lot higher because a lot less testing was done, then
say somewhere like germany that had free rapid testing everywhere.

Perhaps relevant meta data:
- Sweden had a race to heard-immunity policy. 
- Flordia the same but also fined any business that enforced social 
  distancing. 
- Portugal I remember was a place very quick to vaccinate large portion 
  of population 90%.
- Germany barely reached 70% vaccination but had socal distancing and 
  several lockdowns.
- South Korea attempted zero-covid with borders closed until March 2022.

Results:

>>> calculate("sweden",10.35,2.523404,19100)
sweden: pop 10.35mil, cases 2.523404mil, deaths 19100
death per popul %0.18
death per cases %0.76
>>> calculate("germany",83.24,29.3081,142139)
germany: pop 83.24mil, cases 29.3081mil, deaths 142139
death per popul %0.17
death per cases %0.48
>>> calculate("portugal",10.31,5.266454,24359)
portugal: pop 10.31mil, cases 5.266454mil, deaths 24359
death per popul %0.24
death per cases %0.46
>>> calculate("southkorea",51.78,18.602109,24680)
southkorea: pop 51.78mil, cases 18.602109mil, deaths 24680
death per popul %0.05
death per cases %0.13
>>> calculate("usa",329.48,88.849042,1021276)
usa: pop 329.48mil, cases 88.849042mil, deaths 1021276
death per popul %0.31
death per cases %1.15
>>> calculate("flordia",21.78,7.516906,86294)
flordia: pop 21.78mil, cases 7.516906mil, deaths 86294
death per popul %0.40
death per cases %1.15
>>> calculate("newyork",19.84,6.950869,77097)
newyork: pop 19.84mil, cases 6.950869mil, deaths 77097
death per popul %0.39
death per cases %1.11
>>> calculate("newyorkcity",8.46,3.24,44968)
newyorkcity: pop 8.46mil, cases 3.24mil, deaths 44968
death per popul %0.53
death per cases %1.39
>>> calculate("miamidadecounty",2.663,1.53,12283)
miamidadecounty: pop 2.663mil, cases 1.53mil, deaths 12283
death per popul %0.46
death per cases %0.80
>>> calculate("berlin",3.645,1.427574,5531)
berlin: pop 3.645mil, cases 1.427574mil, deaths 5531
death per popul %0.15
death per cases %0.39
>>> 

Data sources:
https://www.statista.com/statistics/1104709/coronavirus-deaths-worldwide-per-million-inhabitants/
https://interaktiv.tagesspiegel.de/lab/karte-sars-cov-2-in-deutschland-landkreise/
"""