A very rough draft of a plan to test prophylactics for airborne illnesses.
Start with a potential superspreader event. My ideal is a large conference, many of whom travelled to get there, in enclosed spaces with poor ventilation and air purification, in winter. Ideally >=4 days, so that people infected on day one are infectious while the conference is still running.
Call for sign-ups for testing ahead of time (disclosing all possible substances and side effects). Split volunteers into control and test group. I think you need ~500 sign ups in the winter to make this work.
Splitting controls is probably the hardest part. You’d like the control and treatment group to be identical, but there are a lot of things that affect susceptibility. Age, local vs. air travel, small children vs. not, sleep habits… it’s hard to draw the line
Make it logistically trivial to use the treatment. If it’s lozenges or liquids, put individually packed dosages in every bathroom, with a sign reminding people to use them (color code to direct people to the right basket). If it’s a nasal spray you will need to give everyone their own bottle, but make it trivial to get more if someone loses theirs.
Follow-up a week later, asking if people have gotten sick and when.
If the natural disease load is high enough this should give better data than any paper I’ve found.
Top contenders for this plan:
zinc lozenge
salt water gargle
enovid
betadine gargle
zinc gargle
I’d really like to do humming but haven’t yet figured out the logisitics of reminding the treatment group to hum without ruining the control group.
This sounds like a bad plan because it will be a logistics nightmare (undermining randomization) with high attrition, and extremely high variance due to between-subject design (where subjects differ a ton at baseline, in addition to exposure) on a single occasion with uncontrolled exposures and huge measurement error where only the most extreme infections get reported (sometimes). You’ll probably get non-answers, if you finish at all. The most likely outcome is something goes wrong and the entire effort is wasted.
Since this is a topic which is highly repeatable within-person (and indeed, usually repeats often through a lifetime...), this would make more sense as within-individual and using higher-quality measurements.
One good QS approach would be to exploit the fact that infections, even asymptomatic ones, seem to affect heart rate etc as the body is damaged and begins fighting the infection. HR/HRV is now measurable off the shelf with things like the Apple Watch, AFAIK. So you could recruit a few tech-savvy conference-goers for measurements from a device they already own & wear. This avoids any ‘big bang’ and lets you prototype and tweak on a few people—possibly yourself? - before rolling it out, considerably de-risking it.
There are some people who travel constantly for business and going to conferences, and recruiting and managing a few of them would probably be infinitely easier than 500+ randos (if for no reason other than being frequent flyers they may be quite eager for some prophylactics), and you would probably get far more precise data out of them if they agree to cooperate for a year or so and you get eg 10 conferences/trips out of each of them which you can contrast with their year-round baseline & exposome and measure asymptomatic infections or just overall health/stress. (Remember, variance reduction yields exponential gains in precision or sample-size reduction. It wouldn’t be too hard for 5 or 10 people to beat a single 250vs250 one-off experiment, even if nothing whatsoever goes wrong in the latter. This is a case where a few hours writing simulations to do power analysis on could be very helpful. I bet that the ability to detect asymptomatic cases, and run within-person, will boost statistical power a lot more than you think compared to ad hoc questionnaires emailed afterwards which may go straight to spam...)
I wonder if you could also measure the viral load as a whole to proxy for the viral exposome through something like a tiny air filter, which can be mailed in for analysis, like the exposometer? Swap out the exposometer each trip and you can measure load as a covariate.
A very rough draft of a plan to test prophylactics for airborne illnesses.
Start with a potential superspreader event. My ideal is a large conference, many of whom travelled to get there, in enclosed spaces with poor ventilation and air purification, in winter. Ideally >=4 days, so that people infected on day one are infectious while the conference is still running.
Call for sign-ups for testing ahead of time (disclosing all possible substances and side effects). Split volunteers into control and test group. I think you need ~500 sign ups in the winter to make this work.
Splitting controls is probably the hardest part. You’d like the control and treatment group to be identical, but there are a lot of things that affect susceptibility. Age, local vs. air travel, small children vs. not, sleep habits… it’s hard to draw the line
Make it logistically trivial to use the treatment. If it’s lozenges or liquids, put individually packed dosages in every bathroom, with a sign reminding people to use them (color code to direct people to the right basket). If it’s a nasal spray you will need to give everyone their own bottle, but make it trivial to get more if someone loses theirs.
Follow-up a week later, asking if people have gotten sick and when.
If the natural disease load is high enough this should give better data than any paper I’ve found.
Top contenders for this plan:
zinc lozenge
salt water gargle
enovid
betadine gargle
zinc gargle
I’d really like to do humming but haven’t yet figured out the logisitics of reminding the treatment group to hum without ruining the control group.
This sounds like a bad plan because it will be a logistics nightmare (undermining randomization) with high attrition, and extremely high variance due to between-subject design (where subjects differ a ton at baseline, in addition to exposure) on a single occasion with uncontrolled exposures and huge measurement error where only the most extreme infections get reported (sometimes). You’ll probably get non-answers, if you finish at all. The most likely outcome is something goes wrong and the entire effort is wasted.
Since this is a topic which is highly repeatable within-person (and indeed, usually repeats often through a lifetime...), this would make more sense as within-individual and using higher-quality measurements.
One good QS approach would be to exploit the fact that infections, even asymptomatic ones, seem to affect heart rate etc as the body is damaged and begins fighting the infection. HR/HRV is now measurable off the shelf with things like the Apple Watch, AFAIK. So you could recruit a few tech-savvy conference-goers for measurements from a device they already own & wear. This avoids any ‘big bang’ and lets you prototype and tweak on a few people—possibly yourself? - before rolling it out, considerably de-risking it.
There are some people who travel constantly for business and going to conferences, and recruiting and managing a few of them would probably be infinitely easier than 500+ randos (if for no reason other than being frequent flyers they may be quite eager for some prophylactics), and you would probably get far more precise data out of them if they agree to cooperate for a year or so and you get eg 10 conferences/trips out of each of them which you can contrast with their year-round baseline & exposome and measure asymptomatic infections or just overall health/stress. (Remember, variance reduction yields exponential gains in precision or sample-size reduction. It wouldn’t be too hard for 5 or 10 people to beat a single 250vs250 one-off experiment, even if nothing whatsoever goes wrong in the latter. This is a case where a few hours writing simulations to do power analysis on could be very helpful. I bet that the ability to detect asymptomatic cases, and run within-person, will boost statistical power a lot more than you think compared to ad hoc questionnaires emailed afterwards which may go straight to spam...)
I wonder if you could also measure the viral load as a whole to proxy for the viral exposome through something like a tiny air filter, which can be mailed in for analysis, like the exposometer? Swap out the exposometer each trip and you can measure load as a covariate.
All of the problems you list seem harder with repeated within-person trials.