I’m never going to get ahead of myself on this and I’m just going to have to accept that. Things are heating up in just about every state that avoided a first wave. There is reason to hope, but I’m pairing that with a sort of steely determination to be prepared for bad news and difficult numbers. And probably I should be on Twitter less and writing more.
Not sure if 10 cases per 100k is a tipping point, or just where you can start seeing the effects of policies and behaviors more readily.
What really matters is the R value at any given time, which is how many other people an infected person infects, on average. The is a function of the policies and behaviors of a given region or population (R0) and the percentage of people susceptible. Let's say both NY and TX implement policies that lead to behaviors that increase R0 to 1.5, say, opening restaurants, but in NY, only 50% of restaurant workers and patrons are still susceptible, but in TX, 97% are, because there was never a real outbreak in TX. Well, in that situation, cases in NY will shrink, and cases in TX will grow exponentially, even though the behavior of the two populations is identical.
So if you have R-values that are pretty-close-to-but-still-above 1.0, and very few cases, you won't see much in the data. At small numbers, daily fluctuations look indistinguishable from random noise. But maybe when case counts get up to 10 per 100k, that low-exponent exponential growth gets easier to see.
Also, on data sources, I find outbreak.info to be very helpful, as it allows you to look at trends by MSA in addition to state and county.
Not sure if 10 cases per 100k is a tipping point, or just where you can start seeing the effects of policies and behaviors more readily.
What really matters is the R value at any given time, which is how many other people an infected person infects, on average. The is a function of the policies and behaviors of a given region or population (R0) and the percentage of people susceptible. Let's say both NY and TX implement policies that lead to behaviors that increase R0 to 1.5, say, opening restaurants, but in NY, only 50% of restaurant workers and patrons are still susceptible, but in TX, 97% are, because there was never a real outbreak in TX. Well, in that situation, cases in NY will shrink, and cases in TX will grow exponentially, even though the behavior of the two populations is identical.
So if you have R-values that are pretty-close-to-but-still-above 1.0, and very few cases, you won't see much in the data. At small numbers, daily fluctuations look indistinguishable from random noise. But maybe when case counts get up to 10 per 100k, that low-exponent exponential growth gets easier to see.
Also, on data sources, I find outbreak.info to be very helpful, as it allows you to look at trends by MSA in addition to state and county.