I've been reading about Covid19 and listening to a number of professional podcasts about Covid9 -- e.g., "This Week in Virology", which amazingly enough, is entertaining enough not to be a soporific. Reading between the lines, I'm coming to the conclusion that the concept of "R_e" ("effective" R), may be fatally flawed, as it tries to capture some sort of "mean" or "average" R in an inherently exponential setting. Thus, if one person has an R_e of 0.9 and another person has an R_e of 40.0, there isn't a good way to average the two R_e's to compute a composite R_e. What if a vanishingly small fraction of infected people induce the vast majority of cases? We know that spreading is a *network* phenomenon which means that it is highly likely to have a *fat tail* distribution. If I'm correct about this, then the standard epidemic model is also fatally flawed, and hence unreliable for making trillion-dollar decisions. What won a Nobel prize a century ago is today an undergraduate homework exercise. We desperately need a better class of models for today's pandemics. For example, in Massachusetts in the early going, almost 100% of the confirmed cases stemmed from a single meeting of a single company in a single downtown Boston hotel. As another example, in Chicago, most of the early cases stemmed from a single funeral and a single birthday party. So if it is true that there are "super spreaders", both in terms of individual people and/or individual events, there must be a better way to quickly identify and isolate these people and events other than putting the entire world on lockdown. I would imagine that some of the work on fractals should be useful to look at this superspreader phenomenon. Perhaps fractures in glass or metal may have relevance, as it may only take a single flaw in a crystal structure to destroy the entire structure.