[chbot] Modelling virus spread...

[Trevor Wignall]

An interesting result, but it makes several assumptions that impact the
conclusion significantly. One is that once people have recovered from the
virus, they are immune. This has not been shown. If immunity is lost after
a period and the virus is still circulating, it keeps going and keeps
killing people.The rate of new infections will then determine the rate it
kills people.
Another assumption is that medical research doesn't find anything useful.
If a vaccine is developed within the period above, the number of new cases
will fall and with it the number of deaths from the virus. A treatment may
also be found, reducing the number of deaths per case. A number of
promising treatments have already been identified and even a delay of a few
months may make a big difference in the number of casualties - and on the
impact on the survivors. Treatments can also reduce the infectiousness of a
patient and therefore can help reduce the number of new cases, and some of
these treatments can also be given to prevent people from being infected -
useful for medical staff and first responders for example.
And of course not overloading the medical system has its own big impact on
the number of casualties.
Food for thought...
Trevor


On Sat, May 9, 2020 at 7:48 PM Bevin Brett <bevin_brett at hotmail.com> wrote:

> I built a computer simulation of the spread of a virus through a group of
> people who had three tiers of interactions - basically those they met
> daily, weekly, and yearly.
> I tried varying the likelihood of an interaction transmitting an infection.
> Provided that the numberOfDailyContacts * likelihoodOfTransmission was
> greater than 1, there were two differences
> 1) how long it took for 97% of the population to get infected
> 2) whether it was 97% or 99% of the population before the virus died out
> So, while I don't claim my model matches a particular virus, I do not
> agree with Carl Bergstrom's argument that flattening the curve has a
> demonstrable significant benefit other than not swamping the medical system
> - in particular in my model it took 150 days to infect 99% or (with a
> tighter lockdown) 750 days to infect 97%.
> Two year lock-down to save 3% (the mortality rate) of 2% (the number that
> flattening the curve stopped from getting infected) of the population from
> dying a few years earlier is not worth it. There are lots of much cheaper
> ways to achieve the same effect, that we have already said we are not
> willing to do.
> Anyone else tried modelling this ? What results did you get?
>
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