(lower prio) Instead of filling in only one mask for “their mask“ specify how many people around you have which type of mask. Especially useful for public transportation where half of the people don’t wear their mask properly.
This particular example is designed to illustrate a way of arriving at a reasonable estimate of this risk without going through each individual component, which is that the largest risk tends to dominate. Zvi summarizes this as “Risks Follow Power Laws”, which is just as true when evaluating the decomposed risks of a single activity as it is for evaluating a set of distinct activities. Not all activities will follow this pattern of a single dominant risk component since it’s very possible to have many components which each contribute a fairly inconsequential risk but in aggregate the risk is enough to matter. However, starting with the biggest risk factor lets you come to a decent estimate quickly. This is especially convenient when you have a clear decision criteria (e.g. “I’ll take the bus if the risk is 10 microcovids or less, and drive otherwise”), since if the highest risk factor is above this then you’re done. If it’s quite a bit below, you’re also done, since the other factors are unlikely to get you there (e.g. if you mentally decompose the activity into 3 parts, and the one you expect to be the biggest risk is 1 microcovid, then you’d also be done unless your mental model of risk is way off).
One way of bounding the risk would be to estimate the risk from the maskless and masked independently and then add their risk together. For instance, if you’re using their “going to work” scenario, you could decompose that profile into the various sub-activities that it’s made up of, which might be “going to work” with 1 person within 15 feet wearing an N95 and silent, 2 people within 15 feet wearing a cloth mask snugly, and 1 person within 15 feet wearing no mask and yelling at the conductor. That gives 3.5 + 14 + 450 microcovids, for an approximate total of 470.
This particular example is designed to illustrate a way of arriving at a reasonable estimate of this risk without going through each individual component, which is that the largest risk tends to dominate. Zvi summarizes this as “Risks Follow Power Laws”, which is just as true when evaluating the decomposed risks of a single activity as it is for evaluating a set of distinct activities. Not all activities will follow this pattern of a single dominant risk component since it’s very possible to have many components which each contribute a fairly inconsequential risk but in aggregate the risk is enough to matter. However, starting with the biggest risk factor lets you come to a decent estimate quickly. This is especially convenient when you have a clear decision criteria (e.g. “I’ll take the bus if the risk is 10 microcovids or less, and drive otherwise”), since if the highest risk factor is above this then you’re done. If it’s quite a bit below, you’re also done, since the other factors are unlikely to get you there (e.g. if you mentally decompose the activity into 3 parts, and the one you expect to be the biggest risk is 1 microcovid, then you’d also be done unless your mental model of risk is way off).