I can't stress enough how important this factor is... without widespread randomized testing of the population, we have no idea what the true infection rate is o

I can't stress enough how important this factor is... without widespread randomized testing of the population, we have no idea what the true infection rate is or how widespread the virus really is. Our current stats have a huge selection bias problem. This is why it is so important that we take social distancing, self hygiene, and shelter-in-place measures really seriously in order to protect those among us who may be more susceptible to severe infection. The silver lining: the more widespread the virus, and hence the number of asymptomatic cases... lower the actual the severity statistics are. i.e. we know "number of deaths", "number of hospitalizations", and "number of positive cases". Right now, when we calculate our various severity rates, we are putting "number of positive cases" in the denominator because we don't know the number of actual infections. There is an unknown multiplier (>= 1) relating positive cases to infections. All our severity statistics need to be divided by this unknown multiplier. But right now, since we don't know what it is... the responsible thing to do for reporting and policy decisions is assume it is 1 (worst case). It is not 1... it is greater than 1... we all know that. But how much is a tough question to answer properly without proper statistical sampling of the population. There are modelling studies and estimates that suggest value ranges for the multiplier, but I don't want to spread potentially false information on a public forum.