Why there isn’t more ground-up interest: it’s expensive and people can’t easily tell if it’s worth the cost. Also anything where UV is touching you has to overcome people’s safety concerns.
Good question on the large effects are easily measured thing—has to do with the distinction between: 1) in what environments you are cleaning the air, 2) how much you are cleaning the air there, 3) how much pathogen people inhale, and 4) how much pathogen is required to actually make people sick. It’s not just a far-UV problem, it’d be a problem for any air cleaner, it’s just that far-UV is especially expensive to install and especially faces negative “UV” associations.
Far-UV has a large effect on airborne pathogen concentration, and that large effect is in fact easy to measure in a chamber! But once you add it to a room where people are moving around and talking to each other, how much pathogen are they actually inhaling? Is the air in the room well-mixed? Is the far-UV reaching the infectious air before people inhale it? Even if they inhale air that has living pathogens, are they getting sick? If they get sick, did they get sick from that room or from a different environment that they were in previously? Study endpoints matter a lot.
Being able to understand intervention efficacy especially becomes a problem if a disease is largely spread via superspreaders/had high variance in infection sources. COVID, at least early in the pandemic, had very high variance, whereas eg seasonal flu doesn’t usually. Therefore, if your study intervention is installed in public spaces, it’s possible for it not to show much effect on seasonal flu but a large effect on a disease with early-COVID-like dynamics, which means you have to wait for that COVID-like disease to come along to see the effect—but that would be worth it; high-variance diseases are very concerning!
Another way of saying all this is that it’s not the case that the effect will be super hard to measure given enough people and time, it’s that the effect is hard to measure given that you need a lot of people in your study to account for the distinctions listed above, and/or you need a highly controlled environment, and that’s just expensive.
Why there isn’t more ground-up interest: it’s expensive and people can’t easily tell if it’s worth the cost. Also anything where UV is touching you has to overcome people’s safety concerns.
Good question on the large effects are easily measured thing—has to do with the distinction between: 1) in what environments you are cleaning the air, 2) how much you are cleaning the air there, 3) how much pathogen people inhale, and 4) how much pathogen is required to actually make people sick. It’s not just a far-UV problem, it’d be a problem for any air cleaner, it’s just that far-UV is especially expensive to install and especially faces negative “UV” associations.
Far-UV has a large effect on airborne pathogen concentration, and that large effect is in fact easy to measure in a chamber! But once you add it to a room where people are moving around and talking to each other, how much pathogen are they actually inhaling? Is the air in the room well-mixed? Is the far-UV reaching the infectious air before people inhale it? Even if they inhale air that has living pathogens, are they getting sick? If they get sick, did they get sick from that room or from a different environment that they were in previously? Study endpoints matter a lot.
Being able to understand intervention efficacy especially becomes a problem if a disease is largely spread via superspreaders/had high variance in infection sources. COVID, at least early in the pandemic, had very high variance, whereas eg seasonal flu doesn’t usually. Therefore, if your study intervention is installed in public spaces, it’s possible for it not to show much effect on seasonal flu but a large effect on a disease with early-COVID-like dynamics, which means you have to wait for that COVID-like disease to come along to see the effect—but that would be worth it; high-variance diseases are very concerning!
Another way of saying all this is that it’s not the case that the effect will be super hard to measure given enough people and time, it’s that the effect is hard to measure given that you need a lot of people in your study to account for the distinctions listed above, and/or you need a highly controlled environment, and that’s just expensive.