I want to start by saying how much I appreciate what the volunteers
behind Microcovid have done.
Providing a risk calculator has been really helpful to a lot of people.
There’s also been a lot of value in running a centralized estimate of
the impact of different mitigations.
On the other hand, at this point, I think it has become substantially
less useful, and in many cases is leading people to seriously
overestimate the level of precautions that are appropriate for their
particular situation:
Its data on prevalence is stale, because the automatic updater
last ran successfully on 2022-02-07.
To determine how risky things are in your area it uses the
average number of cases from last week. When cases are rising, it
extrapolates to predict growth. On the other hand, when cases are
falling (as they are now here, and have been for a while) it does not
extrapolate and assumes that cases have stayed flat.
It’s models the people you interact with as being equally
likely to currently be infectious, regardless of their vaccination
status. Vaccinated people, however, are less likely to get covid and
are infectious for a shorter time period. [EDIT: possibly it is modeling the “less likely to get covid” piece but not “less likely to transmit if infected” and “infectious for less time” pieces?]
It defaults to a risk budget of 200 microcovids per week, a 1%
annual chance of getting covid, regardless of what you tell it for
your vaccination status. The highest budget it offers is only 10x
that, for people who “can’t avoid risk, but still want to make smart
choices”. This was a reasonable way of looking at things earlier in
the pandemic, but at this stage I think much higher budgets are
generally appropriate for fully vaccinated people: the risk to the
individual is generally extremely low and the risk they impose on
others is relatively low. Altruistically, since most of society is
operating on a much higher risk budget keeping to low one is a lot of sacrifice
for minimal benefit.
For a vaccinated and boosted person I think a better approach is to
use no budget most of the time, and then use a budget of perhaps
10,000 microcovids/week (~50% annual risk) when demand on the medical
system is likely to be especially high.
Even just the first two have a very large impact. For example, it
currently gives a random person in my
county as having a 0.78% chance of having covid (780 cases per
100k). Let’s walk through the basic
method with current data.
For the most recent seven days we’ve averaged 379 cases/day.
Population is 1.6M, so that’s 24/100k. The positive test rate is
2.2%. They have some math I don’t entirely follow the reasoning for,
with:
Using current data moves the risk down 14x, from 780/100k to 54/100k.
Now let’s apply the extrapolation that they do when cases are
rising. The seven days before that averaged 460 cases/day, or
29/100k. Test positivity was 4%, so prevalence_ratio is 2.33, and
estimated true_infections is 68/100k. Dividing 54 by 68
we get 79%, so the extrapolated current number is 79% * 54/100k.
Consistent downward extrapolation brings it down another 20%, from
54/100k to 43/100k, for a total of 18x. This extrapolation is
warranted here, if you look at the (more current) wastewater
numbers.
Microcovid Becoming Less Useful
Link post
I want to start by saying how much I appreciate what the volunteers behind Microcovid have done. Providing a risk calculator has been really helpful to a lot of people. There’s also been a lot of value in running a centralized estimate of the impact of different mitigations.
On the other hand, at this point, I think it has become substantially less useful, and in many cases is leading people to seriously overestimate the level of precautions that are appropriate for their particular situation:
Its data on prevalence is stale, because the automatic updater last ran successfully on 2022-02-07.
To determine how risky things are in your area it uses the average number of cases from last week. When cases are rising, it extrapolates to predict growth. On the other hand, when cases are falling (as they are now here, and have been for a while) it does not extrapolate and assumes that cases have stayed flat.
It’s models the people you interact with as being equally likely to currently be infectious, regardless of their vaccination status. Vaccinated people, however, are less likely to get covid and are infectious for a shorter time period. [EDIT: possibly it is modeling the “less likely to get covid” piece but not “less likely to transmit if infected” and “infectious for less time” pieces?]
It defaults to a risk budget of 200 microcovids per week, a 1% annual chance of getting covid, regardless of what you tell it for your vaccination status. The highest budget it offers is only 10x that, for people who “can’t avoid risk, but still want to make smart choices”. This was a reasonable way of looking at things earlier in the pandemic, but at this stage I think much higher budgets are generally appropriate for fully vaccinated people: the risk to the individual is generally extremely low and the risk they impose on others is relatively low. Altruistically, since most of society is operating on a much higher risk budget keeping to low one is a lot of sacrifice for minimal benefit.
For a vaccinated and boosted person I think a better approach is to use no budget most of the time, and then use a budget of perhaps 10,000 microcovids/week (~50% annual risk) when demand on the medical system is likely to be especially high.
Even just the first two have a very large impact. For example, it currently gives a random person in my county as having a 0.78% chance of having covid (780 cases per 100k). Let’s walk through the basic method with current data.
For the most recent seven days we’ve averaged 379 cases/day. Population is 1.6M, so that’s 24/100k. The positive test rate is 2.2%. They have some math I don’t entirely follow the reasoning for, with:
Whereday_iis number of days since 2020-02-12 (738 as of today). They cite Estimating True Infections Revisited: A Simple Nowcasting Model to Estimate Prevalent Cases in the US, but I’m just going to use it as is:Using current data moves the risk down 14x, from 780/100k to 54/100k.
Now let’s apply the extrapolation that they do when cases are rising. The seven days before that averaged 460 cases/day, or 29/100k. Test positivity was 4%, so
prevalence_ratiois 2.33, and estimatedtrue_infectionsis 68/100k. Dividing 54 by 68 we get 79%, so the extrapolated current number is 79% * 54/100k.Consistent downward extrapolation brings it down another 20%, from 54/100k to 43/100k, for a total of 18x. This extrapolation is warranted here, if you look at the (more current) wastewater numbers.
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