4 ms·

Just a quick search on scholar shows me at least 5 studies into the impacts for a more rigorous analysis. Plus anecdotally I'm in NYC and since the stay at hom

by Grimm1 6y ago

Just a quick search on scholar shows me at least 5 studies into the impacts for a more rigorous analysis. Plus anecdotally I'm in NYC and since the stay at home order reached full strength the 3 day rolling average of infections has been decreasing. I can only see that being attributed to two things -- mask wearing and stay at home. Mask wearing came later in our lockdown so I'm going to say stay at home has been effective especially with what I've seen the infection number for COVID, I believe that's the R0 (Rt?) number, was something like 2.5 to much higher.

jtbayly 6y ago
I did a search but didn't find anything. Not sure exactly what to search for. Can you link to some of them?
- Grimm1 6y ago
  https://arxiv.org/ftp/arxiv/papers/2004/2004.06098.pdf https://arxiv.org/ftp/arxiv/papers/2004/2004.06098.pdf https://arxiv.org/pdf/2004.14621.pdf https://arxiv.org/pdf/2004.14621.pdf That's two, I think the second one is better though as it uses more recent data and from more states and they raise a point that I think speaks to why the studies are not more prevalent and that is that many stay at home orders went into effect middle to end of March so the effects would only really be seen at the end of April. I imagine you'll see more of these popping up in the coming weeks.
beerandt 6y ago
>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.

by Grimm1 6y ago

jtbayly 6y ago

I did a search but didn't find anything. Not sure exactly what to search for. Can you link to some of them?

Grimm1 6y ago
https://arxiv.org/ftp/arxiv/papers/2004/2004.06098.pdf https://arxiv.org/ftp/arxiv/papers/2004/2004.06098.pdf https://arxiv.org/pdf/2004.14621.pdf https://arxiv.org/pdf/2004.14621.pdf That's two, I think the second one is better though as it uses more recent data and from more states and they raise a point that I think speaks to why the studies are not more prevalent and that is that many stay at home orders went into effect middle to end of March so the effects would only really be seen at the end of April. I imagine you'll see more of these popping up in the coming weeks.

beerandt 6y ago

>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.