Coronavirus in NYC

It was reported last week that that 37 NYPD members died of covid-19. I assume that they were way below 65. It is known that the coronovirus death rate for people below 65 is a quarter of the total death rate. That means, 37 people in NYPD correspond to at least 150 people in general. Assuming that the mortality rate of coronavirus is 1 percent, the number of infected NYPD members a month ago was 15000.

By now, it could be that more than half of NYPD was infected.

NYPD members have to communicate with people a lot due to the nature of their work. That means they are more prone to being infected. At the same time, they transmit more than people in many other professions.

I can conclude, that about half of the people that are high transmitters in NY have antibodies by now. Assuming they are immune, the covid transmission rate in NY has to be down.

Assuming the immunity stays with people for a while, the second wave in NY can’t be as bad as the first one.



  1. Lev Lipkin:

    Recently published data: “13.9 percent of the population of New York state — about 2.7 million people — have at some point been infected with the coronavirus.” Pandemia stops at 70%, I had read. 60K died in USA a few days ago.
    Assuming same percent infected across country, it means 300K dead. Most likely on average infection rate is half of NY state. Then 600K. Very sad.

  2. Lev Lipkin:

    I meant “60K died in USA as of a few days ago.” Sorry for missing typo.

  3. tanyakh:

    Lev, I think if we count separately people who work from home and people who have to interact with other people for their work, then the model might become more optimistic.

  4. Lev Lipkin:


  5. Robert Clark:

    Thanks for your observations. I wanted to discuss with you a topic that I thought you as a highly regarded mathematician could help with. It’s in regard to the COVID-19 crisis. I would have thought it obvious that one approach to address a search for cures is to look for medications in the collected medical histories of COVID-19 patients either for medications absent from that list, suggesting they may be protective, or ones appearing in common in patients with positive outcomes, suggesting they may be curative.

     Yet, with now over 1 million cases and tens of thousands of deaths in the U.S. this still has not been done. Back in March there were only a few tens of thousands of cases, and a few hundred deaths. If this had been done then likely an answer could have been found within a matter of days. And in an outbreak in its exponential growth phase, every day is vital. If it had been done then the number of deaths could have been limited to a few hundred, rather than tens of thousands as it is now.

     I have been arguing for this to be done since March, but I am only a lone voice in the wilderness, and not a very loud voice. I thought you with a much louder voice could make it clear this in an obvious, low cost, and quick means of searching for cures.
     Some discussions on the topic:

    Big Data to fight COVID-19 and Other Diseases.
     This searches for cures in the reverse sense, by looking for medications absent from the collected medical histories of patients.


     This searches for cures in the direct sense, by looking for medications that patients with positive outcomes have in common.

      Thank You,

         Robert Clark

  6. Lev Lipkin:

    Hi Robert, your ideas are great, however there are a few complications in my opinion. One is privacy issues. Please read how Dr. Helen Chu in WA state tried to trace covid infections very earlier using vast set of flu swaps, but were ordered to stop as permissions were from patients for the flu test, not for the covid test. She was able to detect covid positive patient(s) before the order, but were told not to use those results. Many people in that state might had been saved if violation was allowed due to extraordinary situation.
    Next issue. If one traces significant number of drugs, some of them might look beneficial just by random. Moreover I do not see how to tell if the improvement (if detected) came from the drug and not from the biology of underlying conditions which caused drug to be used in the first place. Other drugs (like anti-lice medication) are not in widespread use (at least, in US orally), but were winners in lab dish against virus culture. Those might be missed.
    Classifying outcome as positive might have many ways: surviving, need for ventilator, hospitalization, or time patient felt sick, etc. I see another chance for pursuing statistical flukes.
    All that said, having combined database of cases with patient history, medication before and during disease, lab results, and outcomes might give chance for breakthrough which might be hard to achieve without such db. Best Luck!

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