What would be really useful is the probability of dying from COVID-19 in a specific place at a specific time for a specific age considering coexisting diseases.
It seems that there is enough raw data for this. Maybe it exists somewhere and we can play with it ourselves?
For example, there is a population density map. Of course, New York has a density of 10,000 people per square kilometer. In our area, it’s 700 people/sq. km. In Milan – 2,000 people per sq. km.
What we have
* A = unknown ratio of all sick/total population, and it is definitely much less than 1
* B = unknown ratio of known sick/all sick, and it is definitely much less than 1
* C = known ratio of deceased/known sick, and for my age it turns out to be 0.02%, which means every 500th out of the known infected.
If B = 0.5 (meaning, half of the infected do not know it’s COVID-19), then every thousandth dies. If B=0.25, then every two thousandth of the sick and so on. How to calculate B is unclear, but there is some methodology available at the link. https://m.habr.com/ru/post/491974/
More about the spread. Understandably, it spreads fastest in hypothetical metro during rush hour. Closing the metro is difficult, it will lead to rampant growth. But how quickly does it spread in places with a density 10 times lower than New York? where there is no metro. Like our places. It is likely that the virus will not work with the necessary speed in such places to maintain the rates, and the rates will drop. But where is the actual threshold of density above which to worry?
Then, there is no distribution by diseases anywhere. Let’s suppose a healthy person of my age gets infected and another similar one but with diabetes. It is claimed that diabetes greatly increases the likelihood of a fatal outcome. How then to calculate the probability? How many of the deceased had comorbidities and how many did not? Maybe we should have calculated mortality separately for healthy and unhealthy? Maybe for a healthy person, mortality is lower than that of the flu?
Hi, I'm Rauf Aliev. 