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Sound data from the CDC has been especially lacking on natural immunity from prior Covid infection. On Aug. 25, Israel published the most powerful and scientifically rigorous study on the subject to date. In a sample of more than 700,000 people, natural immunity was 27 times more effective than vaccinated immunity in preventing symptomatic infections.
Despite this evidence, U.S. public health officials continue to dismiss natural immunity, insisting that those who have recovered from Covid must still get the vaccine. Policy makers and public health leaders, and the media voices that parrot them, are inexplicably sticking to their original hypothesis that natural immunity is fleeting, even as at least 15 studies show it lasts.
Meanwhile, employers fire workers with natural immunity who won’t get vaccinated. Schools disenroll students who won’t comply.
The CDC did put out a study on natural immunity last month [3], forcefully concluding that vaccinated immunity was 2.3 times better than natural immunity. The CDC used these results to justify telling those with natural immunity to get vaccinated.
But the rate of infection in each group was less than 0.01%, meaning infections were exceedingly rare in the short two-month time period the agency chose to study. This is odd, given there are more than a year of data available. Moreover, despite having data on all 50 states, the CDC only reported data from Kentucky. Was Kentucky the only state that produced the desired result? Why else exclude the same data from the other 49 states?
Some public health officials are afraid to acknowledge natural immunity because they fear some will choose infection over vaccination. But leaders can encourage all Americans who aren’t immune to get vaccinated and be transparent with the data at the same time.
The CDC shouldn’t fish for data to support outdated hypotheses. Heeding the robust Israeli data on natural immunity could help restore the agency’s credibility and even help vaccination efforts.
Writing in The Atlantic, David Zweig warns us to beware of fishy Covid-19 data [4]. (HT Lyle Albaugh) Three slices:
If you want to make sense of the number of COVID hospitalizations at any given time, you need to know how sick each patient actually is. Until now, that’s been almost impossible to suss out. The federal government requires hospitals to report every patient who tests positive for COVID, yet the overall tallies of COVID hospitalizations, made available on various state and federal dashboards and widely reported on by the media, do not differentiate based on severity of illness. Some patients need extensive medical intervention, such as getting intubated. Others require supplemental oxygen or administration of the steroid dexamethasone. But there are many COVID patients in the hospital with fairly mild symptoms, too, who have been admitted for further observation on account of their comorbidities, or because they reported feeling short of breath. Another portion of the patients in this tally are in the hospital for something unrelated to COVID, and discovered that they were infected only because they were tested upon admission.
…..
The study found that from March 2020 through early January 2021—before vaccination was widespread, and before the Delta variant had arrived—the proportion of patients with mild or asymptomatic disease was 36 percent. From mid-January through the end of June 2021, however, that number rose to 48 percent. In other words, the study suggests that roughly half of all the hospitalized patients showing up on COVID-data dashboards in 2021 may have been admitted for another reason entirely, or had only a mild presentation of disease.
…..
But the study also demonstrates that hospitalization rates for COVID, as cited by journalists and policy makers, can be misleading, if not considered carefully. Clearly many patients right now are seriously ill. We also know that overcrowding of hospitals by COVID patients with even mild illness can have negative implications for patients in need of other care. At the same time, this study suggests that COVID hospitalization tallies can’t be taken as a simple measure of the prevalence of severe or even moderate disease, because they might inflate the true numbers by a factor of two. “As we look to shift from cases to hospitalizations as a metric to drive policy and assess level of risk to a community or state or country,” [Shira] Doron told me, referring to decisions about school closures, business restrictions, mask requirements, and so on, “we should refine the definition of hospitalization. Those patients who are there with rather than from COVID don’t belong in the metric.”
Paper suggests 1/3 kids had alpha. Then a lot more got delta.
Think of all the quarantines and school closures that could be avoided if we counted these kids as immune.
We are not doing anyone any favors by continuing to ignore natural immunity.https://t.co/KyAOSdSiCZ [5]
— Margery Smelkinson, Ph.D. (@MSmelkinsonPhD) September 13, 2021 [6]
Jay Bhattacharya and Martin Kulldorff criticize Biden’s blanket criticisms of unvaccinated Americans [7].
Here’s the abstract of a new paper on science in the age of Covid [10]:
During the COVID-19 pandemic, many governments have adopted responses revolving around the open-ended use of non-pharmaceutical interventions (NPIs), including “lockdowns”, “stay-at-home” orders, travel restrictions, mask-wearing, and regulated social distancing. Initially these were introduced with the stated goals of “flattening the curve” of hospital demand and/or the eradication of the virus from the country (i.e., “zero covid” policies). Over time, these goals have shifted to maintaining sufficient NPIs in place until such time as population-wide vaccination programmes have achieved an appropriate level of herd immunity to allow lifting of these measures without excessive hospital demand. Supporters of this approach have claimed to be “following the science”, insisting that criticism of any aspects of these measures is non-scientific or even “scientific misinformation”. This idea that only one set of scientifically valid opinions on COVID-19 exists has encouraged the media, social media and even scientific journals to suppress and/or dismiss any differing scientific opinions as “erroneous”, “discredited” or “debunked”, resulting in discouragement of open-minded scientific inquiry or discussion. Accordingly, in the current article we identify two distinct scientific paradigms to analysing COVID-19 adopted within the medical and scientific community. Paradigm 1 is primarily model-driven, while Paradigm 2 is primarily empirically-driven. Using these two paradigms we have analysed the epidemiological data for 30 northern hemisphere countries (with a total population of 882 million). Remarkably, we find using each paradigm leads to diametrically opposite conclusions on many policy-relevant issues. We discuss how these conflicting results might be reconciled and provide recommendations for scientists and policymakers.
TANSTAFPFC (There Ain’t No Such Thing As Free Protection From Covid.) [11]
“Quarantine hotels are useless, morally corrupt and must be scrapped” – so argues Annabel Fenwick Elliott [12]. A slice:
What have these ruthlessly damaging restrictions achieved thus far? According to independent research published this week by the Advantage Travel Partnership, based on readily available data from Public Health England, the number of cases featuring ‘high or very high priority VOCs’ identified since international travel was decriminalised in May is nine, among nearly two million arrivals. Nine. That is not a typo; it represents a risk factor of 0.00045%.
Furthermore, as explained by microbiologist and former Public Health England director Professor David Livermore, a variant that might be capable of evading our vaccines is just as likely to develop ‘in Kew as Kathmandu’.