Online app, based on a mathematical model created by MIT professors Martin Z. Bazant and John Bush, designed to improve upon current social distancing guidelines.
Mention that epsilon tolerance can also reflect the prevalence of infected people int he population. (For example, if p_i is the infected probability in the local population, then we might set epsilon = 1/N*p_i although this can lead to eps>>1 which we don’t wants since the calculation has not included the possibility of many infections (which is in the SI of the paper). The main point is that if p_i is near 0, then we can not have such a low tolerance; although my strategy in the guideline was to bound the conditional probability of transmission if an infected person arrives, since that seems. Like the best way to be safe, and to control spreading at the population level.
Also mention that small epsilon can also help to mitigate the possibility of enhanced short range exposure risk, above the mean in the room (as discussed in the “beyond well mixed room” part of the paper)
Shouldn't be too hard, but again the description can't get too bloated. More detailed clarifications belong in the Advanced tab.
Per Bazant's email:
Shouldn't be too hard, but again the description can't get too bloated. More detailed clarifications belong in the Advanced tab.