I continue to get a lot of value from microcovid.org, just as is. Partly using it myself and partly using it with friends/family who want help evaluating particular actions. Very grateful for this site.

The main additional feature that would be great for me would be help modeling how much of an update to make from Covid tests (e.g., how much does it help if everyone takes a rapid test before a gathering).

The main thing I've gotten out of microcovid is reduced search costs. Having ballpark figures for the relative effects of situations and interventions, gathered in one place, by a source I consider reasonably trustworthy, makes it much cheaper to estimate which risks are worth taking and which interventions are worth bothering with.

"Trustworthy" in this context means "someone systematically looking for the correct numbers, as opposed to targeting a bottom line chosen for reasons other than correctness." As with most politicized information, the problem isn't that the information is unavailable, but that most sources are acting in bad faith. Noise drowns out the signal until search costs become prohibitive. Your whitepaper in particular is excellent in this respect. Showing your work goes a long way towards demonstrating good faith; sharing your sources is better; sharing how and why you're using them is best of all.

Even just having the available options collected together helped. I didn't know P100s were a thing until I read your whitepaper. I use one to attend otherwise-riskier-than-I'd-like events in relative safety and comfort.

It's a shame that Microcovid's numbers haven't been updated for omicron -- that would be the first item on my wishlist, along with the booster and test numbers mentioned in other comments -- but that doesn't diminish the work you've already done. Your team provided an identifiable signal in the noise, and I love it for that.

[edit: since you ask for dollar values, I'd be willing to contribute say $1k towards getting the numbers updated for omicron, tests, and boosters -- provided that it was done to similar standards and preferably by the same team. That's less because I'd derive that much value out of it personally (at this point I know what I need to) and more that I think this sort of work is an underfunded public good.]

microCOVID has been a game changer for me and many people around me: the ability to get quantitative risk assessments radically improved our ability to efficiently spend risk. We recently stopped using it because of Omicron, and I'm very sad about it.

To me, one of the coolest things about microCOVID has been the proof of concept that a group of smart civilians can put together a useful tool that significantly shifts the efficient frontier for navigating Covid. That alone seems valuable to me, and I'd love to see the project keep going as a testbed for how to make similar projects succeed in future.

But, like most volunteer projects, it seems to be slowly sinking beneath the waves. I don't know what, if anything, could change that. A $50,000 grant from ACX? Providing an easier on-ramp for new volunteers? Some kind of Y Combinator for rationalist projects?

My use of MicroCovid.org so far has probably been very different to most LWers, as I'm based in Sydney, Australia. So far, I've mostly been content to follow public health guidelines and have used MicroCovid.org about 4 times a year for the last 2 years. Each time I used it, I found it very useful for thinking about risk and improving my implicit understanding for how risky different activities were. My usage pattern looks set to change pretty dramatically though, as Australia is in the early stages of Omicron going exponential.

Of all the current features, my favourite is the ability to share the link to a custom scenario you've created. I've used that a few times in group chats to discuss whether an event should go ahead, and if so, how risky the participants should think it to be. It would be nice if the link-share feature provided shortened links, but I understand the backend implementation of this is  probably inconvenient. Given the incoming Omicron burst here, improvement of this feature would be worth $100 to me. This post is my official commitment to donating an extra $100 to MicroCovid.org if this suggestion is implemented (but don't go out of your way to do more than $100 of work if I'm the only one who wants this).

I'd also highly value model data for Omicron and future strains, but I trust that the team is already weighing the pros and cons of adding the information currently known Vs. waiting for higher accuracy.

A massive thank you to anyone involved in MicroCovid.org if you're reading this. It has been very useful to so many people, and has collectively helped us all to reduce, manage, and feel comfortable at our chosen level of risk.

 

In the past, I mostly used the microcovid research page as a reference for me to get numbers that I put into my own Guesstimate model. The reason I didn't use microcovid directly is that I find it easiest to think independently about the numbers "how likely is it that the people I am coming in contact with have COVID", "how many people am I coming into contact with during this activity", and "how likely is a contagious person doing this activity to transmit COVID to me". I like to have each of those in my head and multiply them by myself.

Most urgently, I'd like the bugs around missing vaccination data for SF and NYC (and possibly other locales) to be fixed.  https://github.com/microCOVID/microCOVID/issues/1280

I hope the team is not holding off on fixing critical functionality that is obviously broken / missing (e.g. this bug, adjustment for omicron) while they wait for data in response to this post. 

If the team is resource constrained in some way (money, people with particular skillsets), would love to know how the community can help!

I have not used microcovid much because I am not confident in its predictions and modeling assumptions, or I don't feel they are clearly enough defined to make the tool useful. The change that would be valuable to me (which I have difficultly operationalizing) would be if Microcovid were improved such that I could be much more confident in its modeling assumptions and could use it without having to try to make lots of guesses about which scenarios are well modeled. Maybe it would be sufficient just to explain which types of assumptions make for robust modeling outcomes (maybe this is already somewhere in the documentation). Otherwise, I will continue not to use it.

I think that in general maybe Microvid works well in low-risk situations but breaks down in high-risk situations.

Prior to the recent Omicron surge and post-vaccination, I tended to estimate my covid risks by looking at reported covid case rates in my area, and assuming that as a fully vaccinated person, my risk of getting covid was likely lower than the average person in my area (Ohio), many of whom are not vaccinated, even if I went to restaurants and bars at about the same rate as I did in 2019.

Some examples of my confusion about microcovid's modeling assumptions . . .

Looking at the risk profiles for hypothetical other people, for fully vaccinated people in my state (Ohio):
Average person in your area: 11,000 microcovids
Has 4 close contacts whose risk profile you don't know, in an otherwise closed pod: 6,400
Has 10 close contacts whose risk profile you don't know, in an otherwise closed pod: 19,000

What is the definition of a close contact here? Does this mean somebody who they live with or something like that or just somebody who they regularly hang out with closer than 6 feet? It seems to me that the average person in my area (the mean-risk person since this mean is largely determined by the riskiest people, maybe not the median-risk person) has more or less gone back to normal and would have more than 10 close contacts if you're counting the people they live with, work with, or hang out with regularly. Or at least closer to 10 close contacts than 4.

Microcovid currently predicts that a fully mRNA-vaccinated person with a cloth mask who spends 8 hours in a bar acquires 380,000 microCovids (38% chance of getting covid), assuming that the average person in the bar went to a bar within the last 10 days. (reduced to 240,000 if the average person within 15 feet 10+ feet away rather than 6+ which seems more likely. But why doesn't the model care at all about people 20 feet away?) (As a side note, the default assumption was that most of the people in the bar had the risk of "an average person in your area" which doesn't seem right for a typical bar.)

And furthermore, the risk after 8 hours is equal to the risk after 4 hours, huh? I'd think that in 8 hours more people would be coming in and out, you'd be exposed to more possible infected people.

If this assumption were correct, then over the next week we'd see basically all the bartenders and bar workers here in Ohio getting covid simultaneously. (Or does it just max out at 4 hours so that the covid risk of working at a bar for a week is the same as for 4 hours? That just doesn't make sense.) Even if half or so of these cases were asymptomatic, it would probably be enough that many of the bars would shut down. Seems unlikely, but I guess we could see if it happens.

Likely one of the missing parameters here is the protection from recent infection. I could imagine that the majority of bar workers who haven't had covid in the last 3 months will get it over the next month or so, which wouldn't be enough to shut down many bars.

A one-night stand with somebody who has covid (modeled as kissing for 10 hours) my risk is only 100,000 microcovids. It seems bizarre to me that this risk would be about 1/2 to 1/4 the risk of going to a bar for 2 hours with 15 random people who had been in bars in the last 10 days. Maybe my intuitions are just way off. I suppose at the bar there could be multiple people near me with covid, and one of them might be much more infectious than the average person with covid. But I would think that all of them together wouldn't transmit as many viral particles to me as a single person with covid who I am kissing for 10 hours.
 

I want a way to translate microcovids into expected hours of my life lost, taking into account death, long covid, short term illness and all other effects of covid, if there are any.

Given that the early data I've seen suggests that efficacy of 3 doses vs. omicron is similar to that of 2 doses vs. delta -- probably a bit lower, but at least in the same universe -- I've been using it largely as is, multiplying the final output by 2 to 3 based on what I've seen about the household transmission rate of Omicron relative to Delta. I know some other boosted people who have used it in a similar fashion. There's so much uncertainty in the model assumptions that its best use in my view is to get very broad-strokes order-of-magnitude idea of the risk, which has been extremely useful for friends and relatives who have just wanted a baseline idea of whether the risk of getting COVID when participating in a particular activity is more like .01% or .1% or 1% or 10%. (Note: I doubt that said friends and relatives would have been able to use it in this way without my help, since it requires a little math and they're not math types.) So I guess my main recommendations would be:

- don't get rid of it even if you aren't confident in the Omicron data - if you can produce results that are probably in the right order of magnitude, it's still useful! If you aren't up for a full Omicron overhaul, but you think there's some back-of-the-envelope adjustment that could give results that are probably the correct order of magnitude, I think applying that -- with suitable caveats about accuracy -- would be preferable to taking the site down or leaving it as is.

- It's easy to forget how many people are not math people whatsoever. Best practice in risk communication is often considered to be communicating numbers as percentages, as well as contextualized frequencies -- not just 'X-in-a-million', but something like "X out of Y people (for context, Y is roughly the number of people living in Z)" -- as there are a lot of people who don't really understand percentages and need a little context to understand frequencies. In my ideal world the output would make the chance of getting COVID from this specific activity clear as a percentage and as a contextualized frequency, as well as the chance of getting COVID from this activity in a year under the assumption that you do this activity every N weeks, where N can be entered by the user.