Model estimating the number of infected persons in the bay area

[-]Unnamed6y160

I also made an estimate of the number of cases in the bay area, based on deaths and estimated death rate. My calculations are in this spreadsheet.

[-]Scott Alexander6y150

I tried to answer the same question here and got very different numbers - somewhere between 500 and 2000 cases now.

I can't see your images or your spreadsheet, so I can't tell exactly where we diverged. One possible issue is that AFAIK most people start showing symptoms after 5 days. 14 days is the preferred quarantine period because it's almost the maximum amount of time the disease can incubate asymptomatically; the average is much lower.

[-]Eli Tyre6y20

most people start showing symptoms after 5 days.

That seems like a very likely divergence point. The difference between an incubation period of 5 days and of 14 days is an extra 1.3 to 2.5 doubling times. I might do up the same calculations with variable incubation period tomorrow.

The google sheet is shared now. It really is kind of messy though, more like scratch paper than a published document.

[-]Eli Tyre6y*40

Alright, when I change the incubation time to 5 days, and correct a formula error, I get the following:

Upper bound (assuming a 3.5 day doubling time and a 5% confirmation rate): 6,837 infected persons.

Lower bound (assuming a 7 day doubling time and a 70% confirmation rate): 384 infected persons.

...which are a lot closer to your estimates.

[-]Eli Tyre6y20

Actually, I was previously assuming that people are diagnosed the day they develop symptoms, which is probably an unrealistic expectation. If I add a term for that, and assume that there's a 5 day lag, my numbers jump up again:

Upper bound: 17,100

Lower bound: 585

[-]habryka6y20

I just fixed the images for Eli.

[-]Eli Tyre6y40

Thanks Oli.

[-]Elizabeth6y80

My model for predicting prevalence from deaths.

[-]Bird Concept6y60

Whereas the local steps are fairly clear, after a quick read I found it moderately confusing what this model was doing at a high-level, and think some distillation could be helpful.

[-]Eli Tyre6y20

Makes sense. I'm not going to try and fix that today, at least.

[-]Bucky6y40

I've heard that the doubling time for COVID-19 is between 3.5 and 7 days.

In the early days after COVID-19 breaks containment in a given country the doubling period is 2-3 days. I'll try to publish something showing this later today.

(This is based on a fairly arbitrary "breaking containment" criteria of 40 total cases and then seeing exponential-ish growth)

[-]Bucky6y40

More details now posted.

[-]Rohin Shah6y30

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.

[-]Eli Tyre6y60

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.

[-]Rohin Shah6y20

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.

[-]Bird Concept6y*30

I made a new version of your spreadsheet where you can select your location (from the John Hopkins list), instead of just looking at the Bay area.

[-]Eli Tyre6y20

This seems fine for knowing the number of cases in specific countries, but the problem is that metropolitan areas (like the Bay area), are defined by a list of counties, and not by a country.

[-]habryka6y20

[I don't know why my images are tiny right now. Mods, help?]

Looks like you hosted your images on some external site that doesn't allow other people to access them. Looks like they are currently linking to private Google photos from what I can tell.

If you send us the images on Intercom, I can upload them for you and fix the post.