In my coronavirus planning, the crux between different actions is often “how many people are infected (as opposed to symptomatic) on a given day?”
I’d recommend using “how many people are infected, and their case will eventually be severe” (where you could operationalize “severe” as “requires hospitalization”, at least before hospital overcrowding is a problem).
This lets you eliminate the consideration of “what if there are lots of mild cases that we don’t know about yet”, which is one of the biggest uncertainties.
Of course, the chance that you catch COVID-19 is dependent on how many people are infected overall. However, if you think that the severity rate is very low (which predicts higher infection rates and more likelihood of you contracting COVID), you also think that conditional on you contracting COVID, it’s not that bad. Both of these effects are roughly linear and so roughly cancel out.
The underlying thing that’s going on is that you mostly don’t care about contracting mild COVID, and so you mostly don’t care about how many mild COVID cases exist. You can just take the severe cases, and apply the incubation period + doubling time arguments to those to get the probability of you contracting a severe case, which is what you actually care about. Note that if you do this, the “incubation period” is now the time between being infected by COVID, and being hospitalized, which is presumably longer than the incubation time for symptoms, and which we probably have worse data on.
I’ve been thinking of this from the self-interested case; I think the same thing applies to the prosocial case as well but I haven’t thought as much about it.
It seems to me that there are reasons why you would want to track the infection rate separately from severity.
Most notably, if you are in an at risk population, it is more likely that you’ll come down with a severe case, if you catch it. And in that case, you just want to know how many people are infected, even is they only have mild symptoms.
As I’m currently modeling it, you just include the consideration of “what if there are lots of mild cases that we don’t know about yet” in your estimate of the confirmation rate.
Most notably, if you are in an at risk population, it is more likely that you’ll come down with a severe case, if you catch it. And in that case, you just want to know how many people are infected, even is they only have mild symptoms.
Yeah, this is a good point. Intuitively, currently case fatality rate gives you Death given Severity (under the assumption we mostly only detect severe cases), and you need to estimate Severity given Infected. Probably when using my method you’d need to estimate the ratio of your Severity given Infected to the “average” Severity given Infected.
That said, for anyone not significantly at-risk, I’d still recommend working directly with severe cases (and I’d probably still recommend it for people who are at-risk, though the benefits are a lot lower because you have to do the very-uncertain Severity given Infected ratio estimate).
As I’m currently modeling it, you just include the consideration of “what if there are lots of mild cases that we don’t know about yet” in your estimate of the confirmation rate.
Yes, but it introduces a lot of uncertainty / variance in the model’s output, which ideally you could remove.
I’d recommend using “how many people are infected, and their case will eventually be severe” (where you could operationalize “severe” as “requires hospitalization”, at least before hospital overcrowding is a problem).
This lets you eliminate the consideration of “what if there are lots of mild cases that we don’t know about yet”, which is one of the biggest uncertainties.
Of course, the chance that you catch COVID-19 is dependent on how many people are infected overall. However, if you think that the severity rate is very low (which predicts higher infection rates and more likelihood of you contracting COVID), you also think that conditional on you contracting COVID, it’s not that bad. Both of these effects are roughly linear and so roughly cancel out.
The underlying thing that’s going on is that you mostly don’t care about contracting mild COVID, and so you mostly don’t care about how many mild COVID cases exist. You can just take the severe cases, and apply the incubation period + doubling time arguments to those to get the probability of you contracting a severe case, which is what you actually care about. Note that if you do this, the “incubation period” is now the time between being infected by COVID, and being hospitalized, which is presumably longer than the incubation time for symptoms, and which we probably have worse data on.
I’ve been thinking of this from the self-interested case; I think the same thing applies to the prosocial case as well but I haven’t thought as much about it.
It seems to me that there are reasons why you would want to track the infection rate separately from severity.
Most notably, if you are in an at risk population, it is more likely that you’ll come down with a severe case, if you catch it. And in that case, you just want to know how many people are infected, even is they only have mild symptoms.
As I’m currently modeling it, you just include the consideration of “what if there are lots of mild cases that we don’t know about yet” in your estimate of the confirmation rate.
Yeah, this is a good point. Intuitively, currently case fatality rate gives you Death given Severity (under the assumption we mostly only detect severe cases), and you need to estimate Severity given Infected. Probably when using my method you’d need to estimate the ratio of your Severity given Infected to the “average” Severity given Infected.
That said, for anyone not significantly at-risk, I’d still recommend working directly with severe cases (and I’d probably still recommend it for people who are at-risk, though the benefits are a lot lower because you have to do the very-uncertain Severity given Infected ratio estimate).
Yes, but it introduces a lot of uncertainty / variance in the model’s output, which ideally you could remove.