Nearly three years after the Covid-19 pandemic shut down much of the world, we still don’t know how it started.
But the Department of Energy is ready to submit its best guess. In a new report based on fresh intelligence, the agency has concluded that Covid-19 most likely spread to humans as a result of a mistake at a Chinese laboratory (aka the “lab leak” theory), the WSJ reports.
Important note: In making this determination, the Energy Dept. is about as self-assured as any Michael Cera character—it reportedly has “low confidence” that this theory is correct.
Also, why would the Energy Dept. have information about a pandemic’s origins? Little-known fact: The Energy Dept. oversees a network of 17 national laboratories, and some of those labs do advanced bioresearch. The agency frequently leverages this lab network to gather information, rather than relying on typical intelligence operations, according to the NYT.
But there’s still no consensus
In endorsing the lab leak theory, the Energy Dept. joins the FBI, which has concluded with “moderate confidence” that Covid originated accidentally from a Chinese lab: the Wuhan Institute of Virology. The two agencies reportedly arrived at this conclusion via different methods.
However, four other US agencies and the National Intelligence Council have concluded that Covid originated through natural transmission from an infected animal. But they, too, have low confidence their conclusions are correct.
One piece of evidence that’s missing from the natural transmission theory? The animal that hypothetically did the infecting hasn’t been identified. Given all this uncertainty, two other US agencies haven’t reached a conclusion on Covid’s beginnings yet.
So, if you’re doing the math at home: Four US agencies believe it was natural transmission, two say lab leak, and two are undecided.
Zoom out: Scientists say it’s important to make every effort to learn how Covid-19, a pandemic that’s caused nearly 7 million deaths globally, began, so we can better prevent the next one.
But with the Chinese government (Joe and Hunter’s best buds) thwarting investigations by global authorities, there may only be so much information the US can gather. And it might never be able to confidently answer the question: How did Covid begin? Edited.
Side effects include a multitude of things the shot should NOT be doing.
Visits: 20
When a COVID infection wave hit the most and least vaccinated states in 2022, the most vaccinated state had the higher COVID case rates and the higher relative death peaks.
So just for fun, I thought I’d see how these states fared when there was a huge COVID infection outbreak at the start of 2022 that affected both these states.
It turned out that the least vaccinated state had the lower rise in all-cause mortality (1.25 vs. 1.42) vs. avg mortality for the year.
In other words, vaccination appeared to increase all-cause mortality when COVID hit.
However, it might be the case that Rhode Island simply was “hit harder” by the COVID wave with twice as many COVID infections per capita. Or was Rhode Island hit harder because more people were vaccinated and thus more susceptible to infection which is what the Cleveland Clinic study showed very clearly?
By looking at a younger age group, we see a 3X disparity between the two states. The least vaccinated state came out on top.
We have further, and more conclusive, confirmation from an extensive study done by Josh Stirling. There is simply no way for anyone to explain those results which looked at every county in the US.
34% increase in Covid deaths in most vaxxed states vs least vaxxed. In yet ANOTHER study. How many reports have to be published before the truth can no longer be shouted down?
The bottom line: higher vaccination —> higher deaths for all age groups. That’s why 15-year-olds with heart attacks are now the new normal when they were non-existent before the vaccines rolled out.
Methodology
COVID waves are when the CDC would expect the most vaccinated states to do the best compared to the average death rate for the year. So we’d expect a smaller rise in deaths during a COVID infection wave compared to the deaths over the year.
The biggest COVID death peak is at the start of 2022.
So the method is pretty simple: compare the worst four weeks at the start of 2022 with the average death rate for the year in that state. The winner should have the lowest ratio.
Let’s try the next age group down which is 45-64 over the same “deadly” period.
Weeks 1-4 avg for Wyoming=(24+23+22+16)/4=21.25 Average for 2022=19.25 Ratio: 21.25/19.25= 1.1
Weeks 1-4 avg for Rhode Island: (50+46+44+31)/4=42.75 Avg for 2022=32.6 Ratio: 42.75/32.6=1.31
Again, Wyoming had the smaller spike among that age group.
A possible explanation for the discrepancy
The only way for a pro-vaxxer to attack this result is to claim that Rhode Island had a COVID infection wave in January 2022, but Wyoming missed the wave.
Let’s check that out…
These are the COVID cases for Rhode Island:
Here are the COVID cases for Wyoming:
Isn’t this interesting? The pre-vax peak is relatively close to the post-vax peak.
So it was a fair test. Both states had their largest COVID peaks in January. So that was fair.
However, you could also argue that Rhode Island was “harder hit” by the COVID wave than Wyoming and that accounts for the greater all-cause mortality.
Let’s see if that is true.
The population is 1.1M in Rhode Island vs 578K in Wyoming, so Rhode Island is 1.9X larger, but they had 4X as many cases!
So for the 65-74-year-old age group, the most vaccinated state did slightly better since it was hit harder than the ratios could account for! But see the next section…
However, when we look at the 45-64 year age group, we have a spike that is 3X higher in Rhode Island. That’s hard to explain since there is only a 2X per capita increase in cases.
Did Rhode Island have more cases because it is more vaccinated?
For some insights into this, look at the ratio between the pre-vax peak in Wyoming vs. the peak around Jan 2022… it’s only about 50% higher. But the pre-vax vs. post-vax peak in Rhode Island is more than 4X higher!!!Did Rhode Island simply get unlucky and have an 8-fold increase (=4/.5) in the relative sizes of the COVID infection peaks?
I looked at the next two states on the list: Alabama (least vaccinated) and Vermont (most vaccinated). I compared the pre-vax and post-vax peaks and found the same ratio! The higher vaxxed state had a greater infection ratio pre- vs. post-vax (2564/248=10.3X) than the less vaxxed state (17106/4221=4.05).
Isn’t that interesting? In short, it appears the more vaccinated the state, the greater the COVID infection rate on a per capita basis.
What’s the right answer here?
Josh Stirling looked at how cities in the US did in 2022 vs. 2021. He did a longitudinal study where you compare the city with itself one year ago. This is the best way to see what is going on… did your mortality increase or decrease?
Check this out: cities with higher vaccination had larger all-cause mortality increases than cities with lower vaccination rates. In other words, the line goes the “wrong way.”
The line goes the “wrong way.”
This is devastating for the narrative, but of course consistent with what the death reports are saying.
The R2 doesn’t need to be .9 for this to be convincing. They are correlated and it’s the slope of the line that is significant. The slope goes the wrong way. That’s the point.
Could someone argue that this could be caused by other factors? Sure.
But this result is consistent with other analyses, such as the Devil’s Advocate global analysis by Martin Neil and Norman Fenton showing a similar effect (though not longitudinal).
And how is anyone going to explain why we can’t seem to find any nursing home where the death rates went DOWN after the vaccine program was rolled out?
For example, I know one nursing home in Melbourne, Australia with around 90 people who had close to 30 deaths within 12 months after the vaccines rolled out. So we know it wasn’t COVID that killed people in such huge numbers since they were all vaccinated. So I wonder how they died? I have a video of their death announcements.
Finally, a large geriatric practice (around 1,000 patients, 75% over 65, had just 4 COVID deaths (vaccinated) and 1 COVID death (unvaxxed). Population was 85% vaxxed. More important was that in 2022, instead of the normal 11 deaths, they had 39 deaths. They attributed the excess to the vaccine. So it would be difficult for anyone to explain that data. But I’m open to hearing it!
[Several charts shown in a previous article have been omitted for space reasons. The original article is HERE:]
Summary
If the vaccine really worked, the state with the lowest vaccination rate should have the highest spike in all-cause mortality during a COVID wave. That wasn’t the case for the 45-64 age group and it’s arguably not true for the 65-74 age group if you believe that the higher cases are due to higher vaccination rates.
Josh Stirling’s analysis of all cities in the US makes it clear that it’s more likely than not that the vaccines have resulted in a net increase in deaths and thus were a very dumb intervention.
California also has some of the strictest gun control laws in the country. And in the aftermath of those four mass shootings, new House Speaker Kevin McCarthy — who represents a district in southern California — took the opportunity to poke at the state’s firearms restrictions, saying in a press conference that federal gun control legislation would not be an automatic response to these tragedies because such laws “apparently … did not work in this situation.”
So, did California’s gun laws succeed at making it one of the safest states … or did they fail to stop a string of mass shootings? Questions about the efficacy of gun laws have gotten easier to answer in recent years as changes to federal policy have helped to bring money and people back to the field of gun violence research. But decades of neglect mean there are still lots of blank spaces — policies that don’t yet have good quality data backing them up. A recent report from the Rand Corporation that reviewed the evidence behind a variety of gun policies found just three that were supported by evidence that met the report’s quality standards.1
That fact, however, doesn’t mean other gun laws don’t work — just that the research proving it doesn’t yet exist. Scientists I spoke to saw it as an “absence of evidence” problem, stemming from long-standing, intentional roadblocks in the path of gun violence research. Even the authors of the Rand report say lawmakers should still be putting policies aimed at preventing gun violence into practice now — regardless of what the science does or doesn’t say.
“I think that the goal of the lawmaker is to pick laws that they have a reasonable hope will be better than the status quo,” said Andrew Morral, a senior behavioral scientist at the Rand Corporation. “And there’s lots of ways of persuading oneself that that may be true, that don’t have to do with appealing to strict scientific evidence.”
California doesn’t just have some of the nation’s strictest gun laws and lowest gun death rates, it’s also maybe the best state to study gun laws in, said Dr. Garen Wintemute, director of the Violence Prevention Research Program at University of California, Davis Medical Center. That’s because of both the way the state makes data available to researchers and its willingness to work with researchers to further the science. Wintemute is currently part of a team that is working on a randomized controlled trial of one particular California gun law — an initiative that tracks legal gun owners over time and dispatches authorities to remove their weapons if those people later break a law or develop a condition that would make them ineligible to own guns in the state.
It’s hard to oversell what a big deal this is. Frequently referred to as the “gold standard” of evidence-based medicine, randomized controlled trials split participants randomly (natch) into groups of people who get the treatment and groups that don’t. Because of that, it’s easier for researchers to figure out if a medication is actually working — or if it just appears to be working because of some other factor the people in the study happen to share. These kinds of studies are crucial, but almost impossible to do with public policy because, after all, how often can you randomly apply a law?
But California has been willing to try. It took cooperation from many different levels of state leadership, Wintemute said. The government was always going to slowly expand this particular program statewide, but in this case legislators were willing to work with scientists and randomize that expansion across more than 1,000 communities, so that some randomly became part of the program earlier and some later. When the study finally concludes, researchers will be able to compare these two groups and see how joining the program affected gun violence in those places with a high level of confidence.
Most of the time, however, the scientists who study gun laws aren’t working with the kind of research methodology like this that produces strong results. Morral, along with his Rand colleague, economist Rosanna Smart, have reviewed the vast majority of the research on gun control policies done between 1995 and 2020. Their research synthesis found that a lot of what is out there are cross-sectional studies — observational research that basically just compares gun violence statistics at one point in time in a state that has a specific law to those in a state that doesn’t. That type of study is prone to mixing up correlation and causation, Smart said. There could be lots of reasons why California has lower rates of gun violence than Alabama, but studies like this don’t try to tease apart what’s going on. They end up being interpreted by the public as proof a law works when all they’ve really done is identified differences between states.
The Rand analysis threw out these kinds of studies and only looks at research that is, at least, quasi-experimental — studies that tracked changes in outcomes over time between comparison groups. Even then, the analysis ranked some studies as lower quality than others, based on factors such as how broadly the results could be applied. For instance, a study that only looked at the effects of minimum age requirements for gun ownership in one state would be ranked lower than a study that looked at those effects in every state where a law like that existed.
New York Gov. Kathy Hochul. (AP Photo/Yuki Iwamura)
Stand-your-ground laws “appear” to increase firearm homicides.
Following these rules, the Rand team found just three policies that have strong evidence supporting outcomes — and two of these are about the negative outcomes of policies that increase gun access. Stand-your-ground laws, which allow gun owners to use deadly force without trying to leave or deescalate a situation, appear to increase firearm homicides. Meanwhile, conceal-carry laws, which allow gun owners to carry a gun in public places, appear to increase the number of all homicides and increase the number of firearm homicides, specifically. The only laws restricting gun ownership that have this level of evidence behind them are child-access prevention laws, which have been shown to reduce firearm suicide, unintentional self-injuries and death, and homicides among young people.
That makes gun control laws seem flimsy, but it shouldn’t, Morral said. Instead, the lack of evidence ought to be understood as a product of political decisions that have taken the already challenging job of social science and made it even harder. The Dickey Amendment, first attached to the 1996 omnibus spending bill, for example, famously prevented the Centers for Disease Control and Prevention from funding gun violence studies for decades. A new interpretation of that amendment in 2018 changed that, but Dickey wasn’t the only thing making it hard to study gun violence.
Instead, the researchers told me, the biggest impediment to demonstrating whether gun control policies work is the way politicians have intentionally blocked access to the data that would be necessary to do that research.
“So for instance, the federal government has this massive, great survey of behavioral risk indicators that they do every year in every state,” Morral said. “And you can get fantastic information on Americans’ fruit juice consumption as a risk factor for diabetes. But you can’t get whether or not they own guns.” Not knowing gun ownership rates at the state level makes it hard to evaluate causality of some gun control policies, he explained. “And it’s not because anyone thinks [gun ownership] is not a risk factor for various outcomes. It’s because it’s guns.”
The missing data problem also includes the 2003 Tiahrt Amendment that prevents the sharing of data tracing the origins of guns used in crimes with researchers, said Cassandra Crifasi, co-director of the Center for Gun Violence Solutions at Johns Hopkins University. “So now all we can see are these sort of aggregate-level state statistics,” she said. “We can no longer look at things like, when a gun is recovered in a crime, was the purchaser the same person who was in possession of the gun at the time of the crime?”
Recently, researchers have even been missing basic crime data that used to be reported by the FBI’s Uniform Crime Reporting program. Law enforcement agencies and states were supposed to be shifting to the relatively new, much more detailed National Incident-Based Reporting System, but the transition has been a catastrophe, with some of the biggest law enforcement agencies in the country not yet making the switch because of financial and logistical complications, Smart said. “The FBI has not been able to report for the last eight quarters whether homicide rates are up or down,” Morral added.
But much of the data that’s not available at a national level is available in California, Wintemute said. “Unlike researchers in any other state, we have access to individual firearm purchaser records,” he told me — the very data the Tiahrt Amendment blocks at the national level. “We do studies involving 100,000 gun purchasers, individually known to us, and we follow them forward in time to look for evidence of criminal activity or death or whatever the outcome might be that we’re studying,” Wintemute said.
Unfortunately, because the data is only available in California, the results of those studies would only be applicable to California — making it data that wouldn’t be considered high-quality in the Rand report. Wintemute can demonstrate if a policy is working in his home state, but not whether it works in a big, broad, existential sense. It wouldn’t count towards expanding the number of policies Rand has found evidence to support. This is something researchers like Crifasi see as a flaw in the Rand analysis, but it’s also a reason why Morral and Smart don’t think the evidence-based policy is a good standard to apply to gun control to begin with.
It’s useful to know what there is evidence to support, Morral said. “But we don’t at all believe that legislation should rest on strong scientific evidence,” he said. Instead, the researchers from Rand described scientific evidence as a luxury that legislators don’t yet have.
“There’s always gonna be somebody who’s the first person to implement the law,” said Smart. “And they’re going to have to derive their decision based on theory and other considerations that are not empirical scientific evidence.”
Maggie Koerth is a senior science writer for FiveThirtyEight. Part of ABC.
Viral epidemics or pandemics of acute respiratory infections (ARIs) pose a global threat. Examples are influenza (H1N1) caused by the H1N1pdm09 virus in 2009, severe acute respiratory syndrome (SARS) in 2003, and coronavirus disease 2019 (COVID‐19) caused by SARS‐CoV‐2 in 2019. Antiviral drugs and vaccines may be insufficient to prevent their spread. This is an update of a Cochrane Review last published in 2020. We include results from studies from the current COVID‐19 pandemic.
Objectives
To assess the effectiveness of physical interventions to interrupt or reduce the spread of acute respiratory viruses.
Search methods
We searched CENTRAL, PubMed, Embase, CINAHL, and two trials registers in October 2022, with backwards and forwards citation analysis on the new studies.
Selection criteria
We included randomised controlled trials (RCTs) and cluster‐RCTs investigating physical interventions (screening at entry ports, isolation, quarantine, physical distancing, personal protection, hand hygiene, face masks, glasses, and gargling) to prevent respiratory virus transmission.
Data collection and analysis
We used standard Cochrane methodological procedures.
Main results
We included 11 new RCTs and cluster‐RCTs (610,872 participants) in this update, bringing the total number of RCTs to 78. Six of the new trials were conducted during the COVID‐19 pandemic; two from Mexico, and one each from Denmark, Bangladesh, England, and Norway. We identified four ongoing studies, of which one is completed, but unreported, evaluating masks concurrent with the COVID‐19 pandemic.
Many studies were conducted during non‐epidemic influenza periods. Several were conducted during the 2009 H1N1 influenza pandemic, and others in epidemic influenza seasons up to 2016. Therefore, many studies were conducted in the context of lower respiratory viral circulation and transmission compared to COVID‐19. The included studies were conducted in heterogeneous settings, ranging from suburban schools to hospital wards in high‐income countries; crowded inner city settings in low‐income countries; and an immigrant neighbourhood in a high‐income country. Adherence with interventions was low in many studies.
The risk of bias for the RCTs and cluster‐RCTs was mostly high or unclear.
Medical/surgical masks compared to no masks
We included 12 trials (10 cluster‐RCTs) comparing medical/surgical masks versus no masks to prevent the spread of viral respiratory illness (two trials with healthcare workers and 10 in the community). Wearing masks in the community probably makes little or no difference to the outcome of influenza‐like illness (ILI)/COVID‐19 like illness compared to not wearing masks (risk ratio (RR) 0.95, 95% confidence interval (CI) 0.84 to 1.09; 9 trials, 276,917 participants; moderate‐certainty evidence. Wearing masks in the community probably makes little or no difference to the outcome of laboratory‐confirmed influenza/SARS‐CoV‐2 compared to not wearing masks (RR 1.01, 95% CI 0.72 to 1.42; 6 trials, 13,919 participants; moderate‐certainty evidence). Harms were rarely measured and poorly reported (very low‐certainty evidence).
N95/P2 respirators compared to medical/surgical masks
We pooled trials comparing N95/P2 respirators with medical/surgical masks (four in healthcare settings and one in a household setting). We are very uncertain on the effects of N95/P2 respirators compared with medical/surgical masks on the outcome of clinical respiratory illness (RR 0.70, 95% CI 0.45 to 1.10; 3 trials, 7779 participants; very low‐certainty evidence). N95/P2 respirators compared with medical/surgical masks may be effective for ILI (RR 0.82, 95% CI 0.66 to 1.03; 5 trials, 8407 participants; low‐certainty evidence). Evidence is limited by imprecision and heterogeneity for these subjective outcomes. The use of a N95/P2 respirators compared to medical/surgical masks probably makes little or no difference for the objective and more precise outcome of laboratory‐confirmed influenza infection (RR 1.10, 95% CI 0.90 to 1.34; 5 trials, 8407 participants; moderate‐certainty evidence). Restricting pooling to healthcare workers made no difference to the overall findings. Harms were poorly measured and reported, but discomfort wearing medical/surgical masks or N95/P2 respirators was mentioned in several studies (very low‐certainty evidence).
One previously reported ongoing RCT has now been published and observed that medical/surgical masks were non‐inferior to N95 respirators in a large study of 1009 healthcare workers in four countries providing direct care to COVID‐19 patients.
Hand hygiene compared to control
Nineteen trials compared hand hygiene interventions with controls with sufficient data to include in meta‐analyses. Settings included schools, childcare centres and homes. Comparing hand hygiene interventions with controls (i.e. no intervention), there was a 14% relative reduction in the number of people with ARIs in the hand hygiene group (RR 0.86, 95% CI 0.81 to 0.90; 9 trials, 52,105 participants; moderate‐certainty evidence), suggesting a probable benefit. In absolute terms this benefit would result in a reduction from 380 events per 1000 people to 327 per 1000 people (95% CI 308 to 342). When considering the more strictly defined outcomes of ILI and laboratory‐confirmed influenza, the estimates of effect for ILI (RR 0.94, 95% CI 0.81 to 1.09; 11 trials, 34,503 participants; low‐certainty evidence), and laboratory‐confirmed influenza (RR 0.91, 95% CI 0.63 to 1.30; 8 trials, 8332 participants; low‐certainty evidence), suggest the intervention made little or no difference. We pooled 19 trials (71, 210 participants) for the composite outcome of ARI or ILI or influenza, with each study only contributing once and the most comprehensive outcome reported. Pooled data showed that hand hygiene may be beneficial with an 11% relative reduction of respiratory illness (RR 0.89, 95% CI 0.83 to 0.94; low‐certainty evidence), but with high heterogeneity. In absolute terms this benefit would result in a reduction from 200 events per 1000 people to 178 per 1000 people (95% CI 166 to 188). Few trials measured and reported harms (very low‐certainty evidence).
We found no RCTs on gowns and gloves, face shields, or screening at entry ports.
Authors’ conclusions
The high risk of bias in the trials, variation in outcome measurement, and relatively low adherence with the interventions during the studies hampers drawing firm conclusions. There were additional RCTs during the pandemic related to physical interventions but a relative paucity given the importance of the question of masking and its relative effectiveness and the concomitant measures of mask adherence which would be highly relevant to the measurement of effectiveness, especially in the elderly and in young children.
There is uncertainty about the effects of face masks. The low to moderate certainty of evidence means our confidence in the effect estimate is limited, and that the true effect may be different from the observed estimate of the effect. The pooled results of RCTs did not show a clear reduction in respiratory viral infection with the use of medical/surgical masks. There were no clear differences between the use of medical/surgical masks compared with N95/P2 respirators in healthcare workers when used in routine care to reduce respiratory viral infection. Hand hygiene is likely to modestly reduce the burden of respiratory illness, and although this effect was also present when ILI and laboratory‐confirmed influenza were analysed separately, it was not found to be a significant difference for the latter two outcomes. Harms associated with physical interventions were under‐investigated.
There is a need for large, well‐designed RCTs addressing the effectiveness of many of these interventions in multiple settings and populations, as well as the impact of adherence on effectiveness, especially in those most at risk of ARIs.
The PICO model is widely used and taught in evidence-based health care as a strategy for formulating questions and search strategies and for characterizing clinical studies or meta-analyses. PICO stands for four different potential components of a clinical question: Patient, Population or Problem; Intervention; Comparison; Outcome.
Do physical measures such as hand‐washing or wearing masks stop or slow down the spread of respiratory viruses?
Key messages We are uncertain whether wearing masks or N95/P2 respirators helps to slow the spread of respiratory viruses based on the studies we assessed.
Hand hygiene programmes may help to slow the spread of respiratory viruses.
How do respiratory viruses spread? Respiratory viruses are viruses that infect the cells in your airways: nose, throat, and lungs. These infections can cause serious problems and affect normal breathing. They can cause flu (influenza), severe acute respiratory syndrome (SARS), and COVID‐19.
People infected with a respiratory virus spread virus particles into the air when they cough or sneeze. Other people become infected if they come into contact with these virus particles in the air or on surfaces on which they land. Respiratory viruses can spread quickly through a community, through populations and countries (causing epidemics), and around the world (causing pandemics).
Physical measures to try to prevent respiratory viruses spreading between people include:
· washing hands often;
· not touching your eyes, nose, or mouth;
· sneezing or coughing into your elbow;
· wiping surfaces with disinfectant;
· wearing masks, eye protection, gloves, and protective gowns;
· avoiding contact with other people (isolation or quarantine);
· keeping a certain distance away from other people (distancing); and
· examining people entering a country for signs of infection (screening).
What did we want to find out? We wanted to find out whether physical measures stop or slow the spread of respiratory viruses from well‐controlled studies in which one intervention is compared to another, known as randomised controlled trials.
What did we do? We searched for randomised controlled studies that looked at physical measures to stop people acquiring a respiratory virus infection.
We were interested in how many people in the studies caught a respiratory virus infection, and whether the physical measures had any unwanted effects.
What did we find? We identified 78 relevant studies. They took place in low‐, middle‐, and high‐income countries worldwide: in hospitals, schools, homes, offices, childcare centres, and communities during non‐epidemic influenza periods, the global H1N1 influenza pandemic in 2009, epidemic influenza seasons up to 2016, and during the COVID‐19 pandemic. We identified five ongoing, unpublished studies; two of them evaluate masks in COVID‐19. Five trials were funded by government and pharmaceutical companies, and nine trials were funded by pharmaceutical companies.
No studies looked at face shields, gowns and gloves, or screening people when they entered a country.
We assessed the effects of:
· medical or surgical masks;
· N95/P2 respirators (close‐fitting masks that filter the air breathed in, more commonly used by healthcare workers than the general public); and
· hand hygiene (hand‐washing and using hand sanitiser).
We obtained the following results:
Medical or surgical masks
Ten studies took place in the community, and two studies in healthcare workers. Compared with wearing no mask in the community studies only, wearing a mask may make little to no difference in how many people caught a flu‐like illness/COVID‐like illness (9 studies; 276,917 people); and probably makes little or no difference in how many people have flu/COVID confirmed by a laboratory test (6 studies; 13,919 people). Unwanted effects were rarely reported; discomfort was mentioned.
N95/P2 respirators
Four studies were in healthcare workers, and one small study was in the community. Compared with wearing medical or surgical masks, wearing N95/P2 respirators probably makes little to no difference in how many people have confirmed flu (5 studies; 8407 people); and may make little to no difference in how many people catch a flu‐like illness (5 studies; 8407 people), or respiratory illness (3 studies; 7799 people). Unwanted effects were not well‐reported; discomfort was mentioned.
Hand hygiene
Following a hand hygiene programme may reduce the number of people who catch a respiratory or flu‐like illness, or have confirmed flu, compared with people not following such a programme (19 studies; 71,210 people), although this effect was not confirmed as statistically significant reduction when ILI and laboratory‐confirmed ILI were analysed separately. Few studies measured unwanted effects; skin irritation in people using hand sanitiser was mentioned.
What are the limitations of the evidence? Our confidence in these results is generally low to moderate for the subjective outcomes related to respiratory illness, but moderate for the more precisely defined laboratory‐confirmed respiratory virus infection, related to masks and N95/P2 respirators. The results might change when further evidence becomes available. Relatively low numbers of people followed the guidance about wearing masks or about hand hygiene, which may have affected the results of the studies.
How up to date is this evidence? We included evidence published up to October 2022.
Authors’ conclusions
Implications for practice
The evidence summarised in this review on the use of masks is largely based on studies conducted during traditional peak respiratory virus infection seasons up until 2016. Two relevant randomised trials conducted during the COVID‐19 pandemic have been published, but their addition had minimal impact on the overall pooled estimate of effect. The observed lack of effect of mask wearing in interrupting the spread of influenza‐like illness (ILI) or influenza/COVID‐19 in our review has many potential reasons, including: poor study design; insufficiently powered studies arising from low viral circulation in some studies; lower adherence with mask wearing, especially amongst children; quality of the masks used; self‐contamination of the mask by hands; lack of protection from eye exposure from respiratory droplets (allowing a route of entry of respiratory viruses into the nose via the lacrimal duct); saturation of masks with saliva from extended use (promoting virus survival in proteinaceous material); and possible risk compensation behaviour leading to an exaggerated sense of security (Ammann 2022; Brosseau 2020; Byambasuren 2021; Canini 2010; Cassell 2006; Coroiu 2021; MacIntyre 2015; Rengasamy 2010; Zamora 2006).Our findings show that hand hygiene has a modest effect as a physical intervention to interrupt the spread of respiratory viruses, but several questions remain. First, the high heterogeneity between studies may suggest that there are differences in the effect of different interventions. The poor reporting limited our ability to extract the information needed to assess any ‘dose response’ relationship, and there are few head‐to‐head trials comparing hand hygiene materials (such as alcohol‐based sanitiser or soap and water). Second, the sustainability of hand hygiene is unclear where participants in some studies achieved 5 to 10 hand‐washings per day, but adherence may have diminished with time as motivation decreased, or due to adverse effects from frequent hand‐washing. Third, there is little evidence about the effectiveness of combinations of hand hygiene with other interventions, and how those are best introduced and sustained. Finally, some interventions were intensively implemented within small organisations, and involved education or training as a component, and the ability to scale these up to broader interventions is unclear.
Our findings with respect to hand hygiene should be considered generally relevant to all viral respiratory infections, given the diverse populations where transmission of viral respiratory infections occurs. The participants were adults, children and families, and multiple congregation settings including schools, childcare centres, homes, and offices. Most respiratory viruses, including the pandemic SARS‐CoV‐2, are considered to be predominantly spread via respiratory particles of varying size or contact routes, or both (WHO 2020c). Data from studies of SARS‐CoV‐2 contamination of the environment based on the presence of viral ribonucleic acid and infectious virus suggest significant fomite contamination (Lin 2022; Onakpoya 2022b; Ong 2020; Wu 2020). Hand hygiene would be expected to be beneficial in reducing the spread of SARS‐CoV‐2 similar to other beta coronaviruses (SARS‐CoV‐1, Middle East respiratory syndrome (MERS), and human coronaviruses), which are very susceptible to the concentrations of alcohol commonly found in most hand‐sanitiser preparations (Rabenau 2005; WHO 2020c). Support for this effect is the finding that poor hand hygiene, despite the use of full personal protective equipment (PPE), was independently associated with an increased risk of SARS‐CoV‐2 transmission to healthcare workers in a retrospective cohort study in Wuhan, China in both a high‐risk and low‐risk clinical unit for patients infected with COVID‐19 (Ran 2020). The practice of hand hygiene appears to have a consistent effect in all settings, and should be an essential component of other interventions.
The highest‐quality cluster‐RCTs indicate that the most effect on preventing respiratory virus spread from hygienic measures occurs in younger children. This may be because younger children are least capable of hygienic behaviour themselves (Roberts 2000), and have longer‐lived infections and greater social contact, thereby acting as portals of infection into the household (Monto 1969). Additional benefit from reduced transmission from them to other members of the household is broadly supported by the results of other study designs where the potential for confounding is greater.
Routine long‐term implementation of some of the interventions covered in this review may be problematic, particularly maintaining strict hygiene and barrier routines for long periods of time. This would probably only be feasible in highly motivated environments, such as hospitals. Many of the trial authors commented on the major logistical burdens that barrier routines imposed at the community level. However, the threat of a looming epidemic may provide stimulus for their inception.
Implications for research
Public health measures and physical interventions can be highly effective to interrupt the spread of respiratory viral infections, especially when they are part of a structured and co‐ordinated programme that includes instruction and education, and when they are delivered together and with high adherence. Our review has provided important insights into research gaps that need to be addressed with respect to these physical interventions and their implementation and have been brought into a sharper focus as a result of the COVID‐19 pandemic. The 2014 WHO document ‘Infection prevention and control of epidemic ‐ and pandemic‐prone acute respiratory infections in health care’ identified several research gaps as part of their GRADE assessment of their infection prevention and control recommendations, which remain very relevant (WHO 2014). Research gaps identified during the course of our review and the WHO 2014 document may be considered from the perspective of both general and specific themes.A general theme identified was the need to provide outcomes with explicitly defined clinical criteria for acute respiratory infections (ARIs) and discrete laboratory‐confirmed outcomes of viral ARIs using molecular diagnostic tools which are now widely available. Our review found large disparities between studies with respect to the clinical outcome events, which were imprecisely defined in several studies, and there were differences in the extent to which laboratory‐confirmed viruses were included in the studies that assessed them. Another general theme identified was the lack of consideration of sociocultural factors that might affect adherence with the interventions, especially those employed in the community setting. A prime example of this latter point was illustrated by the observations of the use of masks versus mask mandates during the COVID‐19 pandemic. In addition, the cost and resource implications of the physical interventions employed in different settings would have important relevance for low‐ to middle‐income countries. Resources have been a major issue with the COVID‐19 pandemic, with global shortages of several components of PPE. Several specific research gaps related to physical interventions were identified within the WHO 2014 document and are congruent with many of the findings of this 2022 update, including the following: transmission dynamics of respiratory viruses from patients to healthcare workers during aerosol‐generating procedures; a continued lack of precision with regards to defining aerosol‐generating procedures; the safety of cohorting of patients with the same suspected but unconfirmed diagnosis in a common unit or ward with patients infected with the same known pathogen in healthcare settings; the optimal duration of the use of physical interruptions to prevent spread of ARI viruses; use of spatial separation or physical distancing (in healthcare and community settings, respectively) alone versus spatial separation or physical distancing with the use of other added physical interventions coupled with examining discrete distance parameters (e.g. one metre, two metres, or > two metres); the effectiveness of respiratory etiquette (i.e. coughing/sneezing into tissues or a sleeved bent elbow); the effectiveness of triage and early identification of infected individuals with an ARI in both hospital and community settings; the utility of entrance screening to healthcare facilities; use of frequent disinfection techniques appropriate to the setting (high‐touch surfaces in the environment, gargling with oral disinfectants, and virucidal tissues or clothing) alone or in combination with facial masks and hand hygiene; the use of visors, goggles or other eyewear; the use of ultraviolet light germicidal irradiation for disinfection of air in healthcare and selected community settings; the use of air scrubbers and /or high‐efficiency particulate absorbing filters and the use of widespread adherence with effective vaccination strategies.
There is a clear requirement to conduct large, pragmatic trials to evaluate the best combinations in the community and in healthcare settings with multiple respiratory viruses and in different sociocultural settings. Randomised controlled trials (RCTs) with a pragmatic design, similar to the Luby 2005 trial or the Bundgaard 2020 trial, should be conducted whenever possible. Similar to what has been observed in pharmaceutical interventions where multiple RCTs were rapidly and successfully completed during the COVID‐19 pandemic, proving they can be accomplished, there should be a deliberate emphasis and directed funding opportunities provided to conduct well‐designed RCTs to address the effectiveness of many of the physical interventions in multiple settings and populations, especially in those most at risk, and in very specific well‐defined populations with monitoring of the adherence to the interventions.
Several specific research gaps deserve expedited attention and may be highlighted within the context of the COVID‐19 pandemic. The use of face masks in the community setting represents one of the most pressing needs to address, given the polarised opinions around the world, and the increasing concerns over widespread microplastic pollution from the discarding of masks (Shen 2021). Both broad‐based ecological studies, adjusting for confounding and high quality RCTs, may be necessary to determine if there is an independent contribution to their use as a physical intervention, and how they may best be deployed to optimise their contribution. The type of fabric and weave used in the face mask is an equally pressing concern, given that surgical masks with their cotton‐polypropylene fabric appear to be effective in the healthcare setting, but there are questions about the effectiveness of simple cotton masks. In addition, any masking intervention studies should focus on measuring not only benefits but also adherence, harms, and risk compensation if the latter may lead to a lower protective effect. In addition, although the use of medical/surgical masks versus N95 respirators demonstrates no differences in clinical effectiveness to date, their use needs to be further studied within the context of a well‐designed RCT in the setting of COVID‐19, and with concomitant measurement of harms, which to date have been poorly studied. The recently published Loeb RCT conducted over a prolonged course in the current pandemic has provided the only evidence to date in this area (Loeb 2022).
Physical distancing represents another major research gap which needs to be addressed expediently, especially within the context of the COVID‐19 pandemic setting as well as in future epidemic settings. The use of quarantine and screening at entry ports needs to be investigated in well‐designed, high‐quality RCTs given the controversies related to airports and travel restrictions which emerged during the COVID‐19 pandemic. We found only one RCT investigating quarantine, and no trials of screening at entry ports or physical distancing. Given that these and other physical interventions are some of the primary strategies applied globally in the face of the COVID‐19 pandemic, future trials of high quality should be a major global priority to be conducted within the context of this pandemic, as well as in future epidemics with other respiratory viruses of less virulence.
The variable quality and small scale of some studies is known from descriptive studies (Aiello 2002; Fung 2006; WHO 2006b), and systematic reviews of selected interventions (Meadows 2004). In summary, more high‐quality RCTs are needed to evaluate the most effective strategies to implement successful physical interventions in practice, both on a small scale and at a population level. It is very unfortunate that more rigorous planning, effort and funding was not provided during the current COVID‐19 pandemic towards high‐quality RCTs of the basic public health measures. Finally, we emphasise that more attention should be paid to describing and quantifying the harms of the interventions assessed in this review, and their relationship with adherence.
Side effects include a multitude of things the shot should NOT be doing.
Visits: 31
That means that over 40 million women in the United States have had their menstrual cycles affected by the C19 jab.
Project Veritas released another breaking story last night (Feb. 2) featuring Pfizer executive Jordan Trishton Walker. This time, he was caught on camera openly admitting concern about women’s cycles and their fertility. As a result, #Pfertility is trending on Twitter.
BREAKING: @Pfizer Director Concerned Over Women's Reproductive Heath After COVID-19 Vaccinations
"There is something irregular about their menstrual cycles...concerning...The vaccine shouldn't be interfering with that...It has to be affecting something hormonal..."#Pfertilitypic.twitter.com/XAuMPJNShD
In light of Project Veritas’ latest bombshell, we have compiled an array of respected voices speaking out about menstrual and fertility concerns. Doctors, scientists, thought leaders, and women across the globe have been screaming from the rooftops on this subject for years now.
(In the interest of brevity, I’ll just post the videos from Vigalent Fox [there are quite a few]. The full article, with Twitter comments, can be found HERE , Some may be out of order —TPR)
Basically, Pfizer knew that there would be problems, they knew that lipid nanoparticles such as in the clot shot accumulated in the ovaries, and Bill Gates (bless his heart) was studying ways to interfere with reproduction.
BY Steve Kirsch Founder, Vaccine Safety Research Foundation (vacsafety.org) Updated 1/31/23
If the CDC was honest, this is what their new ads should look like!
If the CDC was honest, this is what their new ads should look like!
Using a novel analysis technique, anyone can now prove that there is no longer any doubt that the vaccines are SHORTENING the lifespans of EVERYONE who takes them. They should be immediately stopped.
Update at 12pm PST 1/31/23
This critique is convincing, but wrong. If everyone was last vaccinated just 10 days before the end of 2022, it would still be a .5 ratio if the vaccines were perfectly safe because the death rate in the final 10 days would be spread evenly over time.
I realized I made an error in some of the formulas so I’m re-doing the numbers.
Also, because the unvaxxed transition to the vaccinated, there are fewer unvaccinated to die in later months so there will be fewer unvaccinated deaths which will skew the ratio for the vaccinated to be lower than .5.
I’m currently using the date of last vaccination as the starting point and I believe it may be more correct to use the date of first vaccination. Still mulling that over.
Executive summary
This is the most important article I have ever written in my life.
It shows a novel method that anyone can use to prove that the COVID vaccines are leading to premature death in anyone who takes them, no matter what age. So you don’t have to believe me. You can collect the data yourself and do the same analysis I did. It’s very easy. It took me about an hour to collect the data and analyze it.
The methodology is both technically sound and objective. Anyone can collect their own data including any state in the US and many foreign governments. I predict no one will look. That tells you everything you need to know.
I asked UK Professor Norman Fenton to critique the method I used here. More about him in the text below. Bottom line: he loved the method I used (which he hadn’t seen before), he validated the calculations in the figure below, and he wasn’t aware of any way the conclusion could be legitimately challenged. There are always all sorts of hand-waving arguments such as “your study wasn’t IRB approved” or “your study is unethical because you are looking at deaths from the COVID vaccine” but they are just that: hand-waving.
To further prove my article cannot be challenged, I am pioneering a unique approach to that as well that is fair, thorough, and transparent. I’m publicly offering 10X your wager to anyone who believes that the data actually shows the opposite of what I claimed. See details of the offer in the text below. If you think I got it wrong, you can turn $25K into $250K in days!
This article describes how a simple objective analysis of objective death data (age, date died, date of last COVID vaccination) can be used to prove beyond a reasonable doubt that the COVID vaccines are shortening lifespans and should be immediately halted.
This explains why all the world’s health authorities are keeping their data secret; their data would reveal that all world governments have been killing millions of people worldwide. No government wants that disclosed. They won’t debate me on this. They will try to censor this article because they can’t hide from the truth. Or they will try to create FUD by arguing the survey is biased without describing the bias.
I predict that this article will be ignored by the mainstream press and the medical community. The longer they ignore me, the worse it will look for them. The first rule of holes is that when you find yourself in a hole, stop digging.
Unless there is a serious error in my methodology or someone can explain precisely how surveying “my followers” creates a biased sample that shifts the numbers for the vaccinated or shows us a more comprehensive, trustable data set, the game is now over.
If the vaccines are safe, the CDC should have produced this analysis using statewide data long ago. It is trivial to do. Why didn’t they? The answer is simple: because they know it would blow the narrative and prove to the world that they are incompetent fools.
If you want to prove me wrong, let’s get the statewide data from all states and make it public. All we need is Age, date of death, date of last COVID vaccine. That does not violate HIPAA or a dead person’s privacy because there is no PII.
But states will refuse to release that data because they know if they did, they are finished.
So in the meantime, they will say, “Your survey is biased.” But nobody can explain the “bias” that explains the result because my readers DO NOT CONTROL THE DATE THAT THEIR FRIENDS WERE VACCINATED, their age, or the DATE they died.
My readers may be more affluent than the average American so that’s a bias. But if the vaccine is killing affluent people, we have a problem. My readers might be more intelligent than the average American, so that’s a bias. They may have more intelligent friends. So this survey, it could be argued, just shows that intelligent people are being killed by the vaccine. That SHOULD be a stopping condition.
Or you could argue that my readers are less intelligent than the average person. And once again, unless you are trying to cull a society, that should be a stopping condition as unethical.
ANYONE CAN REPLICATE MY SURVEY if you think it is “biased.” The New York Times could replicate my survey and prove I’m wrong.
But they won’t.
And that tells you everything you need to know, doesn’t it?
If they want to argue with this article, THEY need to show us THEIR data and not engage in hand-waving arguments to create FUD that have no evidentiary basis.
The game is over. We have won. You cannot hide from the truth any longer.
We’ll see if anyone wants to challenge this article and get paid 10X their wager if they are right. Bring it on!
In this article, I show a clever new method for analyzing the death/vax records that is simple and objective; it relies on just a simple division of two time measurements.
The survey
A month ago, on December 25, 2022, I announced the survey below.
The survey asked people if they knew anyone who died in 2020, 2021, or 2022.
If they did know someone, simply report objective facts about the death: age, date died, and if vaccinated, the date most recently vaccinated.
If people knew >1 person who died in the period, just report the person whose details you are most familiar with (e.g., family member vs. friend).
As of January 29, 2023, I received 1,634 responses. The analysis here looks at the responses.
We only consider OBJECTIVE data and our analysis is OBJECTIVE. It’s all math.
If the vaccines are causing death, the analysis will pick it up.
Methodology
The analysis is done by looking at “days in category before death” divided by “days possible in category if you had lived to the end of the observation period.”
We do this for both vaxxed and unvaxxed people… across all ages, and also in various age ranges which I arbitrarily chose. You can choose your own if you don’t like the age categories I chose. It won’t change the result.
Here’s how the method works (credit to Clare Craig who suggested this wording):
Imagine a timeline for 2021 and 2022. For the unvaccinated we would expect an even distribution of deaths over time except for seasonal differences. For each person, we can compare how long they did live in that period with how long they could have lived. A few who died early would have lived for only a tiny fraction of their potential and a few that died late for a large fraction. However, most will be in between and the mean will be 0.5.
For the vaccinated, we start the clock on their date of their last vaccine. The timeline will therefore vary for each person but with a harmless vaccine we would still expect exactly the same distribution – a few early, a few late and most in the middle with a mean of 0.5.
If the vaccine killed people we would end up with more deaths early on. The mean ratio of life lived compared with life that could have been lived will fall below .5.
Given ratio=((time in category)/(time possible in category)) and knowing that the person died sometime in Jan 2021-Dec 2022, we have:
If the intervention (i.e., the vax) does nothing, ratio = .5
If the invention shortens life, ratio <.5
If the intervention increases lifespan, ratio > .5
It’s that simple. The important thing is that the ratio tells us if the intervention is helpful, neutral, or harmful.
The analysis is independent of the rates people die. The fact that older people die faster than younger people is immaterial. Pre-existing conditions, etc. do not matter.
There is an argument to be made that people who got vaccinated first were more vulnerable and were more likely to die, and thus the rate in a category changes over time, but that effect isn’t very large. I’ve run the numbers for those who died and were last vaccinated in 2022 and the numbers are all less than .5. You are welcome to prove me wrong, but you’ll need to do it with evidence, i.e., actual queries and not hand-waving arguments. Numbers talk.
To date, everyone who thinks they can debunk this has produced only handwaving arguments and no analysis.
Sorry, but that’s not very convincing.
Limitations
My survey includes reporters from all over the world, but all the readers speak English and 70% are in the US. The data can be analyzed just for the US and for specific vaccines as well, but below I include all the records to show that I’m not cherry picking and also to get more stability in the numbers (fewer data points creates more noise).
The people who answered are my followers and are most unvaccinated themselves. They are reporting deaths of the person they know the best, whether vaxxed or unvaxxed. I invite fact checkers to validate that people were true to the direction they were given. There are more vaccinated deaths reported simply because 75% of the US population is vaccinated.
The percentage of unvaccinated to total deaths was 29% (222/(222+542)).
So you might think “Ah ha! That proves that the unvaxxed are dying at a higher rate than the vaxxed because it should be only 25% of the deaths that should be vaccinated so this PROVES the vaccines are saving lives!”
No, it just proves that unvaccinated people hang around other unvaxxed people and are slightly more likely to report their deaths.
This is very helpful for our survey for two big reasons:
It gives us enough data in both the vaxxed and unvaxxed buckets so we can do meaningful comparisons between the two buckets
I can’t be accused of bias, e.g., you anti-vaxxers are just reporting vaccinated deaths to make the vax look bad. Clearly this isn’t the case… they are reporting disproportionately more unvaccinated deaths. So it looks very credible because it’s consistent with what you expect to see.
Note that the mix of vaxxed/unvaxxed deaths is immaterial to this analysis. Each cohort is examined independently. If I had 50% vaxxed and 50% unvaxxed deaths, the results would be exactly the same.
It’s important to note that my followers cannot determine the date of death of unvaccinated or vaccinated individuals (unless they have God-like powers). And I have contact info for all the records so they can be “spot checked” to validate that people followed my instructions to report the person they are most familiar with.
There is a recall bias in that people are more likely to report deaths that happened more recently. This shifts the average death time to the right. This is why unvaxxed are > .5 (more about that later).
For vaccinated people, there is also a healthy patient bias. If you are going to die in days due to a fatal cancer, most people would not get vaccinated.
There is some amount of seasonality in deaths that might skew things somewhat. It’s minimal for those <60, and small for the elderly. But we’re looking at a 2 year period so it shouldn’t be much different between vaxxed and unvaxxed.
Gaming
It wasn’t possible to game the survey because nobody, including myself, knew how I was going to analyze the data until after the data was collected.
There was one person who put in a bogus entry (record #260) but that was easily spotted and removed.
The analysis cut off time was before this article was written so anyone trying to pollute the data will be unsuccessful since any new records aren’t included in the analysis.
Transparency
The database has been in public view the entire time that the data has been gathered. When a record is submitted, it appears in the public view.
Verifications
No submissions were deleted (other than record 260 which was clearly gamed) or modified which can be verified by the changelog of the data. The database is hosted by a third party firm.
There is an “integrity check” field indicating which records passed simply sanity check such as date vaccinated < date died. Only those records were processed.
I have the contact information for each reporter. I am looking forward to being contacted by any mainstream “fact check” organization who is willing to be recorded on video as we discuss the article. I’m happy to supply contact info for any line(s) in the survey so the fact checker can verify every record is legitimate.
Expectations
People who die within 2021 to 2022 should be expected to die evenly throughout the period (there is some seasonality so it isn’t flat over the calendar months). Therefore, with no biases, we’d expect that the average days of life is 1 year in any 2 year observation period. So a ratio of .5. The seasonality cancels out.
But due to recall bias (since we are asking people to recall deaths rather than using government records), we’d expect the number to be skewed to dying more recently so maybe we’d see a ratio of .55 for the unvaccinated.
The vaccinated benefit from both recall bias and the healthy patient bias, so it might be .58 or more.
If the vaccines are safe and effective, the ratio of the vaccinated > ratio of the unvaccinated due to the healthy patient bias.
If the vaccines are killing people, the ratio of the vaccinated <= ratio of the unvaccinated (since the healthy patient bias would give the vaccinated an advantage).
If the vaccines are killing people, the ratio will be <0.5.
The ratio for the vaccinated is .31 or less for every age range with > 5 records.
For the unvaccinated, the ratios are .52 or better for every age range with >5 records
The data is remarkably consistent when there are enough records for the range (generally 10 or more records per the uV# or V # columns).
The values in red are unreliable due to a lack of sufficient data points.
Values in red have too few records to compute an accurate ratio. Ratios >.5 are expected for a safe intervention. Ratios <.5 mean something is killing these people prematurely.
For the unvaccinated, my Airtable filter looked like this and I used the unVaxxed days alive/days possible columns:
For the vaccinated, my Airtable filter looked like this and I used the Vaxxed days died/days available columns.
NOTE: The “Integrity check” is NOT complete. But when coupled with the restrictions of the two filtering conditions, invalid records are all filtered out of the final result.
inal result.
Is my analysis wrong?
This is an Occam’s razor analysis. You could get fancier but it wouldn’t change the result. The signal is very very strong that the vaccines should be immediately stopped.
If I have made a mistake, I’d be grateful to see the correct analysis of the data using the same methodology. So if you object, show us the proper analysis.
The data is remarkably consistent for each age range. But there is a huge difference between the vaxxed (.3) and the unvaxxed (.58). This is exactly what I expected to see; no surprises. But it’s IMPOSSIBLE for the blue-pilled medical community to explain how this could possibly happen if the vaccine is so safe since it was supposed to be the other way around.
A simple look at the Notes field confirms the role of the vaccine in these deaths. That’s subjective proof. It shows that the vaccines are not as safe as claimed.
As far as confidence intervals, the numbers are remarkably consistent so the confidence intervals appear to be small. I’ve asked Professor Fenton for the correct way to ascertain these. He’s thinking about it. I’ll update this when I hear back.
But there’s more confirmation…
Failure anecdotes » success anecdotes
Is this analysis consistent with reliable evidence? Yes.
As it turns out, it’s easy to find failure anecdotes for the COVID vaccines. The anecdotes we generally find show STRONG failures.
By contrast, it is nearly impossible to find a “success anecdote,” even a weak success. I always ask doctors who will talk to me and they’ve never mentioned a single success story. I do this constantly on Twitter Spaces in full public view and NONE of the DOCTORS will EVER be able to cite an example. In fact, I have not found any medical doctor who has ever been able to cite a single geriatric practice or nursing home where deaths dropped after the vaccines rolled out.
If the vaccines were saving lives, there should be THOUSANDS of “poster elderly” success stories, yet there are none. All the anecdotes are strongly negative. That’s simply impossible if the vaccines are saving “tens of millions of lives” as Neil deGrasse Tyson said on YouTube. When I called Neil to ask him for a success anecdote, he hung up the phone on me.
So we have a pretty good sense just from the failure to find a success that the vaccines are an utter disaster. We didn’t even need to do any numerical calculations!
Lots of things confirm our hypothesis:
Lack of success anecdotes, but failure anecdotes easy to find
People switch from pro- to anti- but not the reverse.
Nobody can explain the 15,000 excess deaths in VAERS for the COVID vaccines. It’s not there for other vaccines, the deaths are all consistent with vaccine deaths. What killed all these people if it wasn’t the vaccine?
Ed Dowd’s book “Cause Unknown” contains tons of data. Where is the document debunking everything in that book and showing the cause of all these deaths, especially the increase in child deaths happening right after the vaccines rolled out for kids.
What about the 770 safety signals in VAERS. Why didn’t the CDC tell anyone about any of those signals? They notified the public about the VSD signal for stroke and didn’t even mention that it also triggered in VAERS.
Geriatric practice: I finally found a large geriatric practice of 1,000 patients, 75% are over 65. Their normal death rate is 11 per year (the mean). In 2022, they had 39 deaths for the entire year. They attribute the 28 excess deaths to the vaccine. If it wasn’t the vaccine, someone needs to explain to us what is killing these people because whatever it is, it needs to be IMMEDIATELY stopped. They can’t go public for fear of retribution.
Savo Island Cooperative (Berkeley, CA): Roughly 150 people. No deaths for 5 years before COVID; 0 in 2020; 1 in 2021; 3 in 2022 and they were all vaccinated and boosted (plus 3 strokes and 4 heart attacks). Reported to me by Jane Stillwater last night at an event I spoke at. Nobody at the event could recall any success anecdotes.
Ed Dowd mentioned the vaccines have killed 800K Americans and disabled 4X as many as killed, 3.2M since the vaccine program began.
The peer-reviewed scientific literature published a paper by Mark Skidmore showing over 217,000 deaths in 2021 alone due to the COVID vaccine. But they are looking at retracting the paper because Mark didn’t include a full bio on one of the funders of the study. Also, he asked a question about deaths from the COVID vaccine and that’s unethical (COVID virus questions are OK and ethical).
Josh Stirling looked at how cities in the US did in 2022 vs. 2021. So it’s a longitudinal study where you compare the city with itself one year ago. This is the best way to see what is going on… did your mortality increase or decrease. Check this out: cities with higher vaccination had larger all-cause mortality increases than cities with lower vaccination rates. In other words, the line goes the “wrong way.” This is devastating for the narrative, but of course consistent with what the death reports are saying. The R2 doesn’t need to be .9 for this to be convincing. They are correlated and it’s the slope of the line that is significant. The slope is the wrong way. That’s the point.
US cities; all ages; compare 2022 vs. 2021 in the same city The line slopes up. In other words, the experts were completely wrong: the vaccines are deadly. This is very compelling proof of harm that is impossible for anyone to explain away with a straight face. When combined with this analysis, it’s not credible to keep claiming the vaccines are safe and effective.
NOTE: The summary and challenge to prove Steve Kirsh’s analysis wrong is at least as long as what is above. As of the 1/31 update, an error had been discovered and he is re-working against the same data. But he is still challenging Big Pharma and their deep state partners to prove his conclusions wrong and show how they got THEIR numbers.
Although the pandemic is behind us, Big Tech is still censoring health information from the public.
The video hosting company Vimeo recently deleted the channel of The Wellness Company.
The Wellness Company is a startup with a “Freedom From Pharma” program that provides access to doctors and pharmacies that aren’t afraid to provide treatments like ivermectin and hydroxychloroquine (plus, Gateway Pundit benefits when you subscribe through this link or the links below).
In fact, it was a video on ivermectin that caused the deletion, according to The Wellness Company.
Chris Alexander of The Wellness Company said:
“Vimeo banned our account on the basis of an interview with Jen VanDeWater, a licensed pharmacist who runs our Freedom from Pharma program, about the safety and utility of Ivermectin.
“Vimeo has allowed pro-vaccination voices to post video after video that have been riddled with misinformation, disinformation and outright lies. Vimeo isn’t holding any of these people accountable and none of these accounts are being suspended or permanently banned.
“The actions of Vimeo are a reminder of why it is so important for conservatives and freedom loving Americans to build parallel systems. We can no longer rely on the compromised systems of the establishment – and that is exactly why we founded The Wellness Company.
“Nothing is more critical than healthcare and no system has been more exposed over the last three years than our healthcare system. Every American who cares about the truth and who cares about their health should join us!”
A major new autopsy report has found that three people who died unexpectedly at home with no pre-existing disease shortly after COVID vaccination were likely killed by the vaccine.
A further two deaths were found to be possibly due to the vaccine.
The report, published in Clinical Research in Cardiology, the official journal of the German Cardiac Society, detailed autopsies carried out at Heidelberg University Hospital in 2021. Led by Thomas Longerich and Peter Schirmacher, it found that in five deaths that occurred within a week of the first or second dose of vaccination with Pfizer or Moderna, inflammation of the heart tissue due to an autoimmune response triggered by the vaccine had likely or possibly caused the death.
Case characteristic of five deaths likely or possibly caused by the COVID vaccines.Lymphocyte immune cells (white blood cells) are shown in blue and brown among the heart tissue, causing localised inflammation that proved fatal.
In total the report looked at 35 autopsies carried out at the University of Heidelberg in people who died within 20 days of COVID vaccination, of which 10 were deemed on examination to be due to a pre-existing illness and not the vaccine. For the remaining 20, the report did not rule out the vaccine as a cause of death, which Dr. Schirmacher has confirmed to me is intentional as the autopsy results were inconclusive. Almost all of the remaining cases were of a cardiovascular cause, as indicated in the table below from the supplementary materials, where 21 of the 30 deaths are attributed to a cardiovascular cause. One of these is attributed to blood clots (VITT) from AstraZeneca vaccination (the report was looking specifically at post-vaccine myocarditis deaths), leaving 20 from other cardiovascular causes.
For the five deaths in the main report attributed as likely or possibly due to the vaccines, the authors state:
“All cases lacked significant coronary heart disease, acute or chronic manifestations of ischaemic heart disease, manifestations of cardiomyopathy or other signs of a pre-existing, clinically relevant heart disease.”
This indicates that the authors limited themselves to deaths where there was no “pre-existing, clinically relevant heart disease,” making the report very conservative in which deaths it was willing to pin on the vaccines.
Dr. Schirmacher told me:
“We included only cases, in which the constellation was unequivocally clear and no other cause of death was demonstrable despite all efforts. We cannot rule out vaccine effects in the other cases, but here we had an alternative potential cause of death (e.g., myocardial infarction, pulmonary embolism). If there is severe ischemic cardiomyopathy it is almost impossible to rule out myocarditis effects or definitively rule in inflammatory alterations as due to vaccination. These cases were not included.
“We did not aim to include or find every case but the characteristics of definitive, unequivocal cases beyond any doubt. Only by this way you can establish the typical characteristics; otherwise less strict criteria may lead to ‘contamination’ of the collective; it is absolutely plausible that by these criteria we may have missed further cases but the intention of our study was never quantitative or extrapolation and there are numerous positive and negative bias. But we wanted to establish the fact not the size.”
It is of course very possible that the vaccines also cause death where there is an underlying cardiovascular condition, and indeed, that it is more likely to do so. Thus these five deaths are the minimum from these autopsy cases in which the vaccines are involved—those in which there is no other plausible explanation.
It is worth noting here that initially in 2021, when the autopsies were first carried out, Dr. Schirmacher stated that his team had concluded 30–40 percent of the deaths were due to the vaccines. These earlier estimates may give us a better indication of how many of the deaths the authors really think are attributable to the vaccines, when they are unconstrained by highly conservative assumptions (and looking at causes besides myocarditis). Note that these percentages are based on a selection of deaths that occurred shortly after vaccination, not a random sample of all deaths, so the authors rightly warn that no estimation of individual risk can be made from them.
Did the autopsies find spike protein from the vaccines present in the heart tissue? The samples from the five vaccine-attributed deaths were tested for infectious agents including SARS-CoV-2 (in one instance revealing “low viral copy numbers” of a herpes virus, which the authors deemed insufficient to explain the inflammation). However, no tests were done specifically for the virus spike protein or nucleocapsid protein, such as have been used successfully in otherautopsies to aid attribution to the vaccine, so unfortunately this evidence was unavailable for these autopsies.
The autopsies in the report also only cover doses 1 and 2, not any booster doses, and only deaths within 20 days of vaccination, so the report doesn’t address directly the question of what’s been causing the elevated heart deaths since the booster rollouts from autumn 2021 or whether the vaccines can trigger cardiovascular death weeks or months later. (Otherautopsieshave confirmed that the spike protein can persist in the body for weeks or months after vaccination and trigger a fatal autoimmune attack on the heart.)
What the report does do, however, is establish that people who die suddenly in the days immediately following vaccination may well have died from a vaccine-related autoimmune attack on the heart. It also confirms how deadly even mild vaccine-induced myocarditis can be—and thus why studies like the one from Thailand, found cardiovascular adverse effects in around a third of teenagers (29.2 percent) following Pfizer vaccination and subclinical heart inflammation in one in 43 (2.3 percent), and the study from Switzerland finding at least 2.8 percent with subclinical myocarditis and elevated troponin levels (indicating heart injury) across all vaccinated people, are so worrying.
The authors of the new study diplomatically write that the “reported incidence” of myocarditis after vaccination is “low” and the risks of hospitalization and death associated with COVID-19 are “stated to be greater than the recorded risk associated with COVID-19 vaccination”—notably declining to commit themselves to the official propositions that they dutifully repeat.
The fact that those who die suddenly after vaccination may have died from the hidden effects of the COVID vaccine on their heart is thus now firmly established in the medical literature. The big remaining question is how often it occurs.
Stop Press: Dr. John Campbell has produced a helpful overview of the report’s findings in his latest video.
U.S. government scientists at a California laboratory have reportedly made a monumental breakthrough in harnessing the power of fusion energy.
The scientists, working at Lawrence Livermore National Laboratory, recently achieved a net energy gain in a fusion reaction, the Financial Times reported, citing three people with knowledge of the experiment.
An X2.0-class solar flare bursting off the lower right side of the Sun. (NASA / Fox News)
Scientists have been struggling since the 1950s to harness the fusion reaction that powers the sun. But no group has been able to produce more energy from the reaction than it consumes.
Though developing fusion power stations at scale is still decades away, the breakthrough has significant implications as the world seeks to ween itself off of fossil fuels. Fusion reactions emit zero carbon and do not produce any long-lasting radioactive waste. Per The Times, a small cup of hydrogen fuel could potentially power a house for hundreds of years.
“If this is confirmed, we are witnessing a moment of history,” said Dr Arthur Turrell, a plasma physicist, told the paper. “Scientists have struggled to show that fusion can release more energy than is put in since the 1950s, and the researchers at Lawrence Livermore seem to have finally and absolutely smashed this decades-old goal.”
U.S. Energy Secretary Jennifer Granholm and under-secretary for nuclear security Jill Hruby are expected to formally announce “a major scientific breakthrough” at the Lawrence Livermore National Laboratory on Tuesday.
Researcher Toy Reid sits before a timeline chronicling the lead-up to the pandemic at the Hart Senate Office Building in Washington. Credit: Mark Peterson/Redux for Vanity Fair
Visits: 21
ProPublica and Vanity Fair are left wing, but this ProPublica article states something that most folks didn’t even know existed. the origins of COVID-19 released by the Republican oversight staff of a Senate committee. Here’s another shocker. Our examination affirms that the story, and the totality of reporting it marshals, is sound. So please read the complete article.I’ll give my assessment in the comments section.
On Oct. 28, ProPublica and Vanity Fair published a story about an interim report on the origins of COVID-19 released by the Republican oversight staff of a Senate committee. The interim report was the product of a far-reaching investigation into the question of how the pandemic began, and we wanted to give readers an inside view of the team’s work and share independent experts’ views of its findings.
The debate over COVID-19’s origins has been contentious from the start, and the report’s conclusion that the pandemic was “more likely than not, the result of a research-related incident” triggered criticism. Scientists, China observers and others questioned the Senate team’s findings and our reporting about them.
Over the past several weeks, reporters and editors at both publications have taken a hard look at those criticisms.
Our examination affirms that the story, and the totality of reporting it marshals, is sound.
We re-interviewed some of our original sources and reached out to other specialists to address questions that were raised about the work we did to put in context the evidence cited by the interim report. In particular, we took a close look at how Toy Reid, a State Department political officer on loan to the committee, translated a Chinese Communist Party branch dispatch that was cited in both the interim report and in our story as evidence that staff at the Wuhan Institute of Virology (WIV) may have been responding to a biosafety hazard or breach.
We commissioned three Chinese language experts with impeccable credentials who were not involved in the original story to review Reid’s translation. They all agreed that his version was a plausible way to represent the passage, though two also said they would have translated the words to refer to the dangers of day-to-day lab operations. The third produced a translation that was in line with Reid’s. All agreed the passage was ambiguous. We have updated the story to underscore the complexity of interpreting that dispatch.
We have added additional context to the story. We have also identified two factual errors inconsequential to the premise of the story. They have been corrected.
It remains clear that in 2019, the WIV was addressing serious safety issues while scientists there faced pressure to perform. Risky coronavirus research took place in laboratories that lacked the maximum biocontainment safeguards, according to the interim report.
A series of WIV patents and procurement notices “suggest that the WIV experienced persistent biosafety problems relevant to the containment of an aerosolized respiratory virus like SARS-CoV-2,” the interim report says. On Nov. 19, 2019, the same day a senior government safety official arrived at the WIV to discuss what a meeting summary described as a “complex and grave situation currently facing [bio]security work,” the WIV sought to procure a costly air incinerator. One expert told us such equipment could be used as a “quick fix” if the HEPA air filtration system had failed in some way. A few weeks after that procurement notice, the WIV filed a patent application for an improved device to contain hazardous gases inside a biological chamber, like ones used to transport infected animals.
The interim report described the WIV’s struggles to find disinfectants that were effective enough to kill dangerous pathogens without corroding metal. In November 2020, with the pandemic well under way, the WIV filed a patent application for a new disinfectant. The patent said existing disinfectants corrode metals in ways that could allow pathogens to escape, “resulting in loss of life and property and serious social problems.”
The director of the WIV’s highest-level biosecurity lab acknowledged in September 2019 that some Chinese facilities researching dangerous viruses had “insufficient operational funds for routine yet vital processes.” Dr. Gerald Parker, a biosecurity health expert and adviser to the interim report, said he found such revelations “a recipe for disaster.” He added: “You further couple that with an authoritarian regime where you could be penalized for reporting safety issues. You are in a doom loop of pressure to produce, and if something goes wrong you may not be incentivized to report.”
We continue to see our story as a measured exploration of the array of questions raised about the WIV’s laboratories. The possibility that a biosecurity breach at the WIV occurred, and sparked the pandemic, remains plausible.
We plan to keep reporting on this issue and expect new evidence to emerge. It is our view that both the natural-spillover and laboratory-accident hypotheses for the origin of the COVID-19 pandemic merit continued investigation. Given the human toll, which continues to mount, it is imperative that we continue this work.
For those who want to know more details about our exploration of issues raised, our reporting methodologies and conclusions, we are providing more information below on:
After the Vanity Fair-ProPublica story appeared online, questions began to emerge on social media about Reid’s translation of a key passage of a Chinese Communist Party branch dispatch dated Nov. 12, 2019, on the WIV website. According to Reid’s translation, it begins by pointing out that the lab works with dangerous pathogens and that once the test tubes are opened, “it is just as if having opened Pandora’s Box.” While the lab had “various preventive and protective measures,” it was nonetheless important to “avoid operational errors that give rise to dangers.”
The next phrase was the focus of the criticism. It appeared in bold letters in the interim report:
“Every time this has happened, the members of the Zhengdian Lab [BSL4] Party Branch have always run to the frontline, and they have taken real action to mobilize and motivate other research personnel.”
Our story shared Reid’s thought process. We wrote:
“Reid studied the words intently. Was this a reference to past accidents? An admission of an ongoing crisis? A general recognition of hazardous practices? Or all of the above?”
Reid recognized that there was an ambiguity in the phrase he translated as “Every time this has happened.” Did the word “this” refer to the daily dangers of doing experiments in a lab that handles deadly pathogens? Or did it point to the “operational errors that give rise to dangers”?
Before we published our story, Reid told us he found the passage to have a defensive tone. In the story, we quote Reid as concluding, “They are almost saying they know Beijing is about to come down and scream at them.”
Seven days later, on Nov. 19, a senior Chinese official arrived from Beijing to the WIV for a small, high-level safety training. A meeting summary said that the official had come bearing important oral remarks and written instructions from China’s senior leaders, including General Secretary Xi Jinping, related to “the complex and grave situation currently facing [bio]security work.”
To Reid, the mention of instructions from party leaders and reference to a “complex and grave situation” reinforced that the Nov. 12 dispatch was an attempt by the party branch to deflect criticism for something that had gone awry, as he explained.
We interviewed three experts on Chinese Communist Party communications before publication and shared with them the dispatches as they appeared in Chinese on the WIV website. We conducted the interviews on background to get their candid input. They expressed concerns regarding personal safety, given the sensitivity of the subject matter. All agreed with Reid’s interpretation that the safety training on Nov. 19, 2019, as described in the meeting summary, appeared to be urgent, nonroutine and related to some sort of biosafety emergency.
To assess the criticisms of Reid’s work that were raised after the story was published, we commissioned three Chinese translators, each with more than a decade of experience. One has translated for officials at the highest levels of the American and Chinese governments. We wanted their objective view of what the passage said, so we asked them to translate it and did not mention the interim report. After they had done that, we went back and asked them to review Reid’s translation from the report.
All three of their translations were different from one another’s and different from Reid’s. Yet, each agreed that Reid’s translation was one plausible way to translate the passage into English. Our translators looked at the Chinese characters that Reid had translated to read “Every time this has happened” and instead said they read them to mean “on such occasions” or “at every such an occasion.”
Before one of the translators was told what Reid had written, she said she thought the word “occasions” referred to when lab workers make mistakes that lead to hazards — an interpretation that mirrored Reid’s. The two others said they thought “occasions” referred to something more routine: opening test tubes for experiments. The language in Chinese, all three agreed, was ambiguous and could be read either way.
Some readers noted that the Nov. 12, 2019, passage actually appeared in August 2019 in a party publication. The existence of the earlier reference, they argued, proved that its repetition in November meant that it could not refer to a biosecurity emergency at that time.
We took a close look at the August 2019 post and asked our translators and the experts we consulted to do so as well. While the posts were very similar, the version uploaded on the WIV website in November 2019 was slightly different. It included additional language after the sentence that compared opening test tubes of viruses in the lab to opening Pandora’s box. The translator we commissioned who had the most experience rendered the additional language as follows: “These viruses are untraceable both coming and going, and although there are various protective measures, it is still necessary for lab workers to operate very carefully in order to avoid creating dangers through mishandling.” The translator was puzzled by the August post because without the language added in November, “it sounds as if they are leading the charge to open Pandora’s box,” she said. “If I were reading it, I’d be scratching my head.” That additional sentence, she said, “means that they go to the front lines to show everybody to be careful and not to cause errors that would be dangerous.”
One of the experts we consulted before and after publication, a former senior U.S. intelligence official, said the language added in November 2019 gave the post a defensive posture and was consistent with Reid’s analysis that party members were responding to some type of incident. The Chinese idiom that Reid translated as “come without a shadow and leave without a trace,” he said, “is a nice phrase to describe something that sneaked up on you and there was no way to defend against it. They’re basically saying to whoever this is being delivered to: ‘We didn’t see it coming. We did the best that we could to deal with the problem.’”
More on the Corrections and Added Context
There are two sentences in the story that have been corrected.
We reported that a Chinese military vaccinologist who had in the past collaborated with the WIV, Zhou Yusen, was the first to apply for a patent for a vaccine against COVID-19. The interim report stated that Zhou “was the first to patent a COVID-19 vaccine on February 24, 2020.” In fact, other researchers around the world sought patents before Zhou’s Feb. 24, 2020, filing.
However, it was the timing and nature of Zhou’s patent application and subsequent research papers that raised questions for interim report researchers.
In our review of early SARS-CoV-2 vaccine patent filings, the U.S. patent applications we found that predated Zhou’s were provisional applications, a number of which forecast experiments they planned to do in the future. Many of these applications were for vaccine candidates proposing to use a technology like mRNA. Such applications could be filed with the SARS-CoV-2 genetic sequence in hand and minimal experiment data.
By contrast, Zhou filed a full patent application for a different kind of vaccine that required more upfront work before its submission. Our story says, “In his patent application and in subsequently published papers, Zhou documented a robust research and development process that included both adapting the virus to wild-type mice and infecting genetically modified ones with humanized lungs.” We have updated the story to make clear why Zhou’s work stood out to the interim report researchers.
In our article, we quoted two independent experts and one adviser to the interim report about when they thought Zhou’s research was likely to have begun. After reviewing the patent and the papers, two said that they thought Zhou would have had to have started this work no later than November 2019. Jesse Bloom, a virologist at the Fred Hutchinson Cancer Center, said he believed Zhou’s timetable was feasible since his team had substantial expertise and ongoing work developing similar SARS-related coronavirus vaccines, but only if “everything went right.”
We have also corrected the sentence stating that Gabriel Gras was the last French expert at the WIV. We have learned that at least one other French scientist came to the WIV after Gras left.
Elsewhere, we’ve clarified language. Our story said that party officials at the WIV’s top biosafety lab “repeatedly lamented” the problem of “the three ‘nos’: no equipment and technology standards, no design and construction teams, and no experience operating or maintaining [a lab of this caliber].” We found two references to this concept in party branch dispatches on the WIV website in 2019. These Chinese Communist Party dispatches, we reported, “are often couched in a narrative of heroism — a focus on problems overcome and challenges met, against daunting odds.” We have updated the story to clarify that authors of those posts referred to the “three ‘nos’” as a recounting of problems from early in the lab’s construction that they said had been overcome, rather than a reference to ongoing struggles.
However, one of the experts on party communications we consulted saw the inclusion of the “three ‘nos’” in WIV dispatches as a telling sign that these serious problems from the beginning were “part of the DNA of this lab.”
On Whether the Lab Leak Is a Question Worthy of Exploration
Our story and the interim report pointed to a pair of oft-cited scientific analyses of COVID-19’s origins, one of which concludes that the pandemic was likely the result of multiple zoonotic events in which “two distinct viral lineages” of SARS-CoV-2 that had been circulating among animals at a Wuhan market infected people there.
Michael Worobey, an author on both papers, undoubtedly speaks for many when he says that natural spillover is “the only plausible scenario for the origin of the pandemic.” We repeatedly heard the perspective that the scientific case on the origins of COVID-19 is closed and that exploring the possibility that the coronavirus could have leaked from a Chinese laboratory is something no news organization or government official should take seriously.
We believe the opposite, that it remains an essential avenue for exploration to prevent future pandemics. And as interviews with other scientists before and after publication have made clear, the question is far from resolved. In their view, there is not enough evidence to establish how the virus first reached the now-infamous Wuhan market or to assert that zoonotic spillover is the sole possible explanation for the pandemic’s origin.
Bloom, the virologist at the Fred Hutchinson Cancer Center, is among those scientists. “I’ve never seen anything as controversial as this in my field,” he said. “The amount of toxicity is out of control. Each side feels uniquely wronged. To me, it remains an open question.”
The story noted that the interim report also left this question open: “The authors of the interim report do not claim to have definitively solved the mystery of COVID-19’s origin.” And the story also said the interim report is “no likelier” than studies of a zoonotic origin to “close the book on the origins debate, nor does it attempt to.”
Bloom believes the findings of the interim report and the story reinforce a need to continue to explore all possible causes of the pandemic. At the same time, he recognizes that the reactions to these investigations underscore the difficulty of having a dispassionate conversation about these questions. “Right now, this whole topic is so politically fraught, it’s hard for people to give objective assessments,” he said. “We may need an independent commission to get to the bottom of this.”
Hector Retamal/AFP via Getty Images
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