True, but let’s not forget (re-stated in that article):
The majority of those hospitalized with severe illness during the Omicron surge are unvaccinated, public health experts say.
It’s largely the unvaccinated who are causing hospital overload, and health system overload in general.
With your conditions, you’ve wisely chosen vaccination and boosting. As have I, at my “advanced” age.
True but currently irrelevant to anyone with conditions that make Covid more severe. Omicron is so contagious and does such a good job of evading immune defenses that it spreads even among the fully vaccinated and boosted.
I think that having been vaccinated/boosted, if you were to catch the omicron, your infection would be much less severe than it would have been otherwise. You’d almost certainly recover and be OK. (Let’s hope that doesn’t have to be put to a test.)
Yeah, I probably wouldn’t die, but I might be hospitalized and get long Covid. Much worse risk than two months ago.
I think the odds are on your side that you’d even be hospitalized. At least I hope not.
Actually, for all we know, you’ve already had it and I’ve already had it, – asymptomatically. I guess there’s no easy way to know.
And because almost anyone can get infected to some degree with Omicron, I guess we’re all at risk for long covid too. At the same time, I think (but can’t recall any factual data) that the risk of long covid increases with disease severity. So with a less severe infection, your risk of that would at least be somewhat mitigated.
Chances that I’ve had asymptomatic Omicron are close to zero since my wife had a negative PCR test a few days after the last time I did anything unmasked indoors.
You be as optimistic as you want. I’m sticking with statistics and rationality.
Some more than others.
One of the four factors researchers identified is the level of coronavirus RNA in the blood early in the infection, an indicator of viral load. Another is the presence of certain autoantibodies — antibodies that mistakenly attack tissues in the body as they do in conditions like lupus and rheumatoid arthritis. A third factor is the reactivation of Epstein-Barr virus, a virus that infects most people, often when they are young, and then usually becomes dormant.
The final factor is having Type 2 diabetes, although the researchers and other experts said that in studies involving larger numbers of patients, it might turn out that diabetes is only one of several medical conditions that increase the risk of long Covid.
I think their model is off. I know of three indoor parties where everyone was boosted and almost everyone got Omicron.
Modeling estimates from a team at USC studying the Omicron wave through mid-January say that L.A. County could have seen 85% fewer infections, and 87% fewer hospital admissions, if everyone eligible had been vaccinated. If everyone eligible had been boosted, there would’ve been 95% fewer coronavirus cases, and 98% fewer hospital admissions.
Misleading news from a right-wing propaganda paper owned by the Moonies.
The authors are economists, not epidemiologists or public health experts. Steve H. Hanke, one of the authors and the founder of the Institute for Applied Economics, Global Health, and the Study of Business Enterprise, which published the article, was a major supply-side promoter in the Reagan administration and is a senior fellow at the right-wing libertarian Cato Institute.
I can’t find a news article that analyzes the article critically. Presumably one will be published soon.
warrior: the tone of the study did strike me as unprofessional and biased. at the same time, johns hopkins is a respected university. i don’t believe anything i read, whether it comes from right-wing propaganda papers like the Washington Times or what i consider left-wing propaganda papers like the NY Times.
I’m not sure the study had anything to do with Johns Hopkins except that Hanke’s institute is based there. The other two authors are a “special advisor at Center for Political Studies in Copenhagen” and a “professor emeritus in economics at Lund University, Sweden.” So they’re not “researchers at Johns Hopkins University” at all. Even calling Hanke a “researcher” is a stretch.
The Washington Times is roughly as full of bullshit as the One America Network. If you think the NY TImes is left-wing the way the Washington Times is right-wing, you don’t know what journalism is.
Seriously - Washington Times - why not Breitbart
The meta-analysis is fine but also clearly stated its limitations and how they came to their conclusion. The Washington Times, on the other hand, obviously cherry picked info from the analysis in writing their article to form their viewpoint.
Essentially, the conclusion of this analysis isn’t anything new. I’ve seen different studies with similar conclusions depending on the methodology used. This analysis only contemplated studies that uses a certain methodology which seems to coincide with findings that aren’t typically favorable to lockdowns compared to say studies that focus on before/after implementation of lockdowns.
Another point of interest that this analysis touched on is the fact that lockdown shouldn’t be taken to suggest that it has no effect. Even without lockdown, many have voluntary behavior changes like dining out less so the effect of lockdowns have less of a dramatic affect as we would think.
This study employed a systematic search and screening procedure in which 18,590 studies are identified that could potentially address the belief posed. After three levels of screening, 34 studies ultimately qualified. Of those 34 eligible studies, 24 qualified for inclusion in the meta-analysis.
Talk about cherry-picking.
Let’s see if I ran my calculator correctly. Hmmmm… “24 qualified for inclusion” out of 18590. Hmmm…that comes up as 0.13%. Hmmmm…
And Mama Mia, 18590 studies! Really? That’s-a lotta studies! They must have been very busy systematically searching and screening for stuff to address the “belief posed”!
But it’s true that there was a tremendous socio-economic burden from the lockdowns.
The good news of the day is that the effective transmissibility number in CA has been steadily decreasing and is now estimated as being less than 1 for almost all of the state. Here’s today’s map, where red is Re > 1, yellow is approximately Re = 0.7 - 0.9, and green is Re < 0.7.
This is from the “Nowcast” on the CDPH website, CalCAT
Another good indicator is that hospitalization admissions due to covid seem to have peaked, and are expected to reach pre-omicron levels about three weeks from now:
On this plot, the peak data point is 2020. The pre-omicron level (early December) was 400. The vertical line is today’s estimated level.
Unfortunately, deaths probably won’t peak until next month.
That’s hard to reconcile with these LA Times charts showing 7-day average cases still higher than previous peaks.
Alameda County:
LA County:
Re > 1 means that new cases are on the rise. Values of Re below but near 1 means that new cases are more or less “stable”. But when Re is low enough, like less than 0.7 (as it is in most of CA) it just means that the number of new cases (due to reduced transmissibility) is most likely falling. Values of Re < 0.7 (roughly) don’t imply that new case numbers are low, just declining.
