I’ve said it isn’t crazy to consider cutting pandemic deaths via more infection inequality, including via deliberate exposure. Some have said I’m evil to suggest that, while others have said it just can’t work. In this post, I address those latter doubts, by offering specific sim models wherein variance and deliberate exposure save lives.
It's not unthinkable, although I struggle to name an agent acting this way. It should go dormant like a chickenpox, but somehow be able to trick the immune system into losing track of it.
What I meant is that they are infected again in case of re-exposure, being in contact with a carrier of a pathogen. Just as in case of flu.
I think I see where you're going with this question, a fixed chance of reinfection within a certain time range is by no means the best way to model it, but perhaps it can be used as an approximation.
You didn't mention any particular infectious agent in the article, but I personally would be interested to check it for the 15% chance of reinfection within three months.
Good thinking, but given the slow doubling, it's absolutely insane to not just build more (temporary structure) treatment capacity in the coming 200 days instead (and if desired, crash course train immune volunteers from among the naturally early infected to become caregivers - it didn't look like that "infect critical personnel" idea made too much difference anyway)
> The sick who don’t die become immune for rest of the model period.Might be interesting to introduce a greater then zero chance of the relapse into the model (making it SIS instead of SIR) and check whether it affects conclusions.
Right that seems like a good argument for infecting health care workers ASAP.
We could probably do that without too much fuss by just implementing a rotating system for caring for the infected. That could be sold on a simple fairness model (no one has to spend more than x-days being exposed to patients with new disease).
Do not these models require decision making early in the process? The problem is that it's becoming clear that accurate data on infection rates, R0 and injury and fatality rates for different populations and survival rates for different populations as a function of care available are available until some distance into the epidemic. We're still debating this one quite a lot now. In reality, this data would trickle in and face several corrections, possibly both upward and downward, and might not stabilize for a very long time. So you need to simulate based on imperfect knowledge, and also presuming that if you take a policy of deliberate infection and it turns out to be bad, the political consequences are so dire as to make the policymakers demand high levels of certainty which may take a very long time to arrive.
Ok and apparently the worries about inacurrate reporting are overblown.
It's not unthinkable, although I struggle to name an agent acting this way. It should go dormant like a chickenpox, but somehow be able to trick the immune system into losing track of it.
What I meant is that they are infected again in case of re-exposure, being in contact with a carrier of a pathogen. Just as in case of flu.
I think I see where you're going with this question, a fixed chance of reinfection within a certain time range is by no means the best way to model it, but perhaps it can be used as an approximation.
So they are reinfecting themself, not being infected by others?
You didn't mention any particular infectious agent in the article, but I personally would be interested to check it for the 15% chance of reinfection within three months.
Yes, it makes sense to crash train folks to work in ICUs, and to infect them early. Fine to pay them extra for this.
Good thinking, but given the slow doubling, it's absolutely insane to not just build more (temporary structure) treatment capacity in the coming 200 days instead (and if desired, crash course train immune volunteers from among the naturally early infected to become caregivers - it didn't look like that "infect critical personnel" idea made too much difference anyway)
That is easy to add, and it is obvious that doesn't change much when that chance is small. So how large a relapse rate is plausible?
> The sick who don’t die become immune for rest of the model period.Might be interesting to introduce a greater then zero chance of the relapse into the model (making it SIS instead of SIR) and check whether it affects conclusions.
https://www.worldometers.in...
Right that seems like a good argument for infecting health care workers ASAP.
We could probably do that without too much fuss by just implementing a rotating system for caring for the infected. That could be sold on a simple fairness model (no one has to spend more than x-days being exposed to patients with new disease).
Where are you getting those from. I suspect those are largely from China and there is good reason to believe they started to shave the death tolls.
I’m a bit confused about the relevance of the second variation. What us the intervention that produces this outcome?
Earlier decisions can have more leverage, but that doesn't make later decisions useless.
Do not these models require decision making early in the process? The problem is that it's becoming clear that accurate data on infection rates, R0 and injury and fatality rates for different populations and survival rates for different populations as a function of care available are available until some distance into the epidemic. We're still debating this one quite a lot now. In reality, this data would trickle in and face several corrections, possibly both upward and downward, and might not stabilize for a very long time. So you need to simulate based on imperfect knowledge, and also presuming that if you take a policy of deliberate infection and it turns out to be bad, the political consequences are so dire as to make the policymakers demand high levels of certainty which may take a very long time to arrive.
Cases:
Feb 19...75,700.....(+1%)Feb 26...81,829.....(+1%)Feb 27...83,112.....(+2%)Feb 28...84,624.....(+2%)Feb 29...86,613.....(+3%)Mar 5.....98,242.....(+3%)Mar 6....102,049....(+4%) Mar 8....110,664....(+4%)Mar 9....116,059....(+5%)
Deaths:
Jan 29... 170........ 29% increase over the previous dayFeb 4..... 492....... 13%Feb 13... 1,383.... 10%Feb 16... 1,775..... 6%Feb 23....2,618......6%Feb 25....2,763......2%Mar 1.....3,069.......3%Mar 6.....3,494.......3%Mar 7.....3,599.......2%Mar 8.....3,827.......6%Mar 9.....4,025.......5%
Are you saying these models above are too simple to be interesting?