<div dir="ltr">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.<div>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.</div><div>And of course not overloading the medical system has its own big impact on the number of casualties.</div><div>Food for thought...</div><div>Trevor</div><div><br></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Sat, May 9, 2020 at 7:48 PM Bevin Brett <<a href="mailto:bevin_brett@hotmail.com">bevin_brett@hotmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">
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<div dir="auto" style="text-align:start">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.</div>
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<div dir="auto" style="text-align:start">I tried varying the likelihood of an interaction transmitting an infection.</div>
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<div dir="auto" style="text-align:start">Provided that the numberOfDailyContacts * likelihoodOfTransmission was greater than 1, there were two differences</div>
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<div dir="auto" style="text-align:start">1) how long it took for 97% of the population to get infected</div>
<div dir="auto" style="text-align:start">2) whether it was 97% or 99% of the population before the virus died out</div>
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<div dir="auto" style="text-align:start">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%.
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<div dir="auto" style="text-align:start">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.</div>
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<div dir="auto" style="text-align:start">Anyone else tried modelling this ? What results did you get?</div>
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