>I can only see that being attributed to two things
Two more things: 1) introducing (or the halting thereof) infections to at risk populations (the whole NY nursing/retirement home fiasco, or the Italy hospitals being points of transmission, or dorm style living in Wuhan) and 2) approaching ~50% of herd immunity.
1) When did NY state stop putting exposed/infected patients into those facilities? That's going to have a outsized result in the stats, given the segment vulnerability. Same for Italy hospitals being overwhelmed without PPE.
2) Not saying it's what is at play (yet) here, but infections will naturally decrease as a greater percentage of the population gains immunity. It's what has to happen as the population approaches herd immunity. (So about 30% if herd immunity is 60%, which is plausible, given the recent estimates of 20+% of the NYC population with antibody presence.)
It's why exponential growth (for resource limited kinetics) ends up being an S curve and not a runaway exponential curve. The inflection point might not be at exactly 50% for a number of reasons, but it's good for a rough starting point.
Additionally, it will be difficult to distinguish if the stay-at-home orders are effective due to the entire population participating, or if it's tied to specific population segments. Meaning the percent of the population active vs stay-at-home isn't independent of transmission numbers. The active sub-population might be closer to herd immunity than the stay-at-home crowd.
This gets more complicated as you take into account the risk levels of particular patients.
There's potentially a lot more going on. We need some scientific causation, not just correlation.
Two more things: 1) introducing (or the halting thereof) infections to at risk populations (the whole NY nursing/retirement home fiasco, or the Italy hospitals being points of transmission, or dorm style living in Wuhan) and 2) approaching ~50% of herd immunity.
1) When did NY state stop putting exposed/infected patients into those facilities? That's going to have a outsized result in the stats, given the segment vulnerability. Same for Italy hospitals being overwhelmed without PPE.
2) Not saying it's what is at play (yet) here, but infections will naturally decrease as a greater percentage of the population gains immunity. It's what has to happen as the population approaches herd immunity. (So about 30% if herd immunity is 60%, which is plausible, given the recent estimates of 20+% of the NYC population with antibody presence.)
It's why exponential growth (for resource limited kinetics) ends up being an S curve and not a runaway exponential curve. The inflection point might not be at exactly 50% for a number of reasons, but it's good for a rough starting point.
Additionally, it will be difficult to distinguish if the stay-at-home orders are effective due to the entire population participating, or if it's tied to specific population segments. Meaning the percent of the population active vs stay-at-home isn't independent of transmission numbers. The active sub-population might be closer to herd immunity than the stay-at-home crowd.
This gets more complicated as you take into account the risk levels of particular patients.
There's potentially a lot more going on. We need some scientific causation, not just correlation.