1) How likely are you to catch the virus at all just by being in the same area/frequenting the same shops as somebody infected? My impression from the Western cases so far was that it infections occurred generally with close contacts; this risk changes obviously when more infected people are around, but still should be estimated to decide whether such an app would be worth it.
I imagine the CDC could already have some estimates for this, which they might use in contact tracing. And it might turn out that contact tracing is enough to solve the problem. It seems to be working well right now in the U.S.
But if not, a not-so-educated guess for a general outline of the calculation of risk for the app might be (1) Close contact. You were in the same location where a possibly infectious person spent some time within maybe 10 minutes of them. This is higher risk. (2) Semi-Close Contact. If the virus might live on surfaces for a few hours, the close contact risk distribution tapers off over a few hours and (3) Infectiousness. This isn’t binary so the infectious person’s distribution also peaks sometime around their first symptoms, and tapers off over a few days up until the tail reaches some maximum.
A heat map that changes with time could reflect this information. And any individual’s risk could be calculated by integrating over it. And, if the local situation is suddenly found to have had multiple people in it in the last few weeks, and the level of precision was possible, you could do multiple iterations of this given each user’s (small) risk of contracting the virus from their interactions too, given an estimate of what we think the time might be between when a person contracts the virus to when they become infectious. This is harder, but if everything else works, it’s possible.
Writing this out in words is long, but the actual summation/integral is not too complicated. It’s a combination of science and hack-y guesses. But this seems true to me of almost all engineering.
I imagine the CDC could already have some estimates for this, which they might use in contact tracing. And it might turn out that contact tracing is enough to solve the problem. It seems to be working well right now in the U.S.
But if not, a not-so-educated guess for a general outline of the calculation of risk for the app might be (1) Close contact. You were in the same location where a possibly infectious person spent some time within maybe 10 minutes of them. This is higher risk. (2) Semi-Close Contact. If the virus might live on surfaces for a few hours, the close contact risk distribution tapers off over a few hours and (3) Infectiousness. This isn’t binary so the infectious person’s distribution also peaks sometime around their first symptoms, and tapers off over a few days up until the tail reaches some maximum.
A heat map that changes with time could reflect this information. And any individual’s risk could be calculated by integrating over it. And, if the local situation is suddenly found to have had multiple people in it in the last few weeks, and the level of precision was possible, you could do multiple iterations of this given each user’s (small) risk of contracting the virus from their interactions too, given an estimate of what we think the time might be between when a person contracts the virus to when they become infectious. This is harder, but if everything else works, it’s possible.
Writing this out in words is long, but the actual summation/integral is not too complicated. It’s a combination of science and hack-y guesses. But this seems true to me of almost all engineering.