Fair enough. When I was thinking about “broad covid risk”, I was referring more to geographical breadth—something more along the lines of “is this gonna be a big uncontained pandemic” than “is coronavirus a bad thing to get.” I grant that the latter could have been a valid consideration (after all, it was with H1N1) and that claiming that it makes “no implication” about broader covid risk was a mis-statement on my part.
That being said, I wouldn’t really consider it an alarm bell (and when I read it, it wasn’t one for me). The top answer, Connor Flexman, states:
Tl;dr long-term fatigue and mortality from other pneumonias make this look very roughly 2x as bad to me as the mortality-alone estimates.
It’s less precise than looking at CoVs specifically, but we can look at long-term effects just from pneumonia.
For me personally:
A 2x increase in how bad Covid19 was in February was not cause for much alarm in general. I just wasn’t that worried worried about a pandemic
The answer is based long-term effects of pneumonia, not covid itself (which isn’t measurable). If I read something that said “hey you have a surprisingly high likelihood of getting pneumonia this year”, I would be alarmed. This wasn’t really that post
I was already kind of expecting that Covid could cause pneumonia based on typical coverage of the virus—I wasn’t surprised by the post in the way I’d expect to be if it was an alarm bell
I’ll give the post some points for pointing out a useful, valuable and often-neglected consideration but I dunno. At that time I saw “you are in danger of getting coronavirus” posts as different from “coronavirus can cause bad things to happen” posts. And the former would’ve been alarm bells and the latter wouldn’t’ve been.
I’ve been playing with the Kinsa Health weathermap data to get a sense of how effective US lockdowns have been at reducing US fever. The main thing I am interested in is the question of whether lockdown has reduced coronavirus’s r0 below 1 (stopping the spread) or not (reducing spread-rate but not stopping it). I’ve seen evidence that Spain’s complete lockdown has not worked so my expectation is that this is probably the case here. Also, Kinsa’s data has two important caveats:
People who own smart thermometers are more likely to be health conscious which makes them more likely to be health conscious than the overall population. Kinsa may therefore overstate the effect of the lockdown by not effectively sampling the health apathetic people more likely to get the virus.
Kinsa data cannot separate coronavirus fever symptoms with flu fever symptoms. At the early stages of coronavirus spread, seasonal flu illness dominates coronavirus illness and seasonal flu r0 is between 1-2. This means that a lockdown can easily eliminate symptoms caused by seasonal flu illness by reducing flu r0 below zero without reducing coronavirus’s r0 below zero.
I’m addressing this by comparing the largest amounts of observed atypical illness over the last month in different locations with their current total illness to get a conservative estimate of how much coronavirus %ill have changed.
With this in mind, my overall conclusion is that the Kinsa data does not disconfirm the possibility that we’ve reduced r0 below 1. Within the population of people who use smart thermometer’s, we’ve probably stopped the spread but it may/may not have stopped in the overall population. Here are my specific observations:
The overall US %ill weakly suggests we may have reduced r0 below 1. It maxed out at around 5.1% ill compared to a range of 3.7-4.7 %ill . This indicates that 0.4-1.4% of overall illness was due to coronavirus and currently total illness is only 0.88%. This means that, for many values in that range, our lockdowns are actually cutting into the percent of people getting coronavirus and therefore that the virus is not growing.
New York county NY %ill weakly suggests that we may have reduced r0 below 1. It maxed out at 6.4 %ill compared to a typical range of 2.75-4.32, indicating that 2.1-3.65% of people had coronavirus. Currently, total illness is 2.56%. Again, for most values in that range, it looks like we’re reducing the absolute amount of coronavirus.
Cook county IL (Chicago) %ill is very weakly positive on reducing r0 below 1. It maxed out at 5.4 %ill with a range of 2.8-4.9 indicating that 0.5-2.6% of people had coronavirus. Currently the total is 0.92% which suggests we’ve likely cut into coronavirus illness. The range of typical values is so large though that its hard to reach a conclusion
Essex country NJ (Newark) %ill doesn’t say much about r0. It maxed out at 6.1 compared to a typical range of 2.9-4.5 which implies a range of coronavirus %ill of 1.6-3.2 The current value is 2.63% which is closer to the higher end of the range so there’s no evidence that we’ve reduced the amount of coronavirus. Still %ill is continuing to trend down so this may change in the future.
I also considered looking at Santa Clara County CA, Los Angeles County CA, and Orleans Parish LA (New Orleans) but their %ill never exceeded the atypical value by a large enough amount for me to perform comparison.
On Mar28, the overall US %ill changed from a steep linear drop of ~-0.3%ill/day to a weaker linear drop of ~-0.1%ill/day. Also, on Mar28, both Newark’s and New York’s fast linear drop is broken with a slight increase in illness and it looks like we’re on our second leg down there now. Similar on Mar27, Chicago’s fast linear drop is broken with a a brief plateau and second leg down. No idea why this happened.