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“I want to apologize for advocating for the use of masks” – Spread of respiratory illnesses not slowed down by mask use

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The Cochrane Database of Systematic Reviews has released a comprehensive study of Physical interventions to interrupt or reduce the spread of respiratory viruses.  

The study examines the effectiveness of masks and of following a hand hygiene program in reducing the likelihood of acquiring respiratory virus infections such as COVID-19.

British Health Researcher Dr. John Campbell shares the disappointing results in this presentation:

From Dr. John Campbell

 

From the Cochrane Library

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 We identified five ongoing, unpublished studies; two of them evaluate masks in COVID‐19.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.

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.

 


Dr. John Campbells presentation notes with links:

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.

Do physical measures such as hand-washing or wearing masks stop or slow down the spread of respiratory viruses?

https://www.cochranelibrary.com/cdsr/…

Evidence published up to October 2022.

Background Influenza (H1N1) caused by the H1N1pdm09 virus in 2009

Severe acute respiratory syndrome (SARS) in 2003 Coronavirus disease 2019 (COVID-19)

Update of a Cochrane Review last published in 2020.

We include results from studies from the current COVID-19 pandemic.

Main results 11 new RCTs and cluster-RCTs n = 610,872

Bringing the total number of RCTs to 78

Medical/surgical masks compared to no masks

Wearing masks in the community probably makes little or no difference to the outcome of influenza‐like illness

wearing a mask may make little to no difference in how many people caught a flu-like illness/COVID-like illness

Risk ratio (RR) 0.95, (0.84 to 1.09) 9 trials, n = 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 RR 1.01, (CI 0.72 to 1.42)

6 trials, n = 13,919 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

We are very uncertain on the effects of N95/P2 respirators compared with medical/surgical masks on the outcome of clinical respiratory illness

Compared with wearing medical or surgical masks, wearing N95/P2 respirators probably makes little to no difference in how many people have confirmed flu and may make little to no difference in how many people catch a flu-like illness, or respiratory illness.

Confirmed influenza RR 0.70, (0.45 to 1.10) N = 7,779 Very low-certainty evidence

Influenza like illness N95/P2 respirators compared with medical/surgical masks may be effective for ILI RR 0.82 N= 8,407 Low-certainty evidence

The use of a N95/P2 respirators compared to medical/surgical masks

Probably makes little or no difference for laboratory-confirmed influenza infection RR 1.10 N = 8,407 Moderate-certainty evidence

Restricting pooling to healthcare workers made no difference to the overall findings.

Harms were poorly measured and reported

Discomfort wearing medical/surgical masks or N95/P2 respirators was mentioned in several studies

Very low-certainty evidence

One new RCT Medical/surgical masks were non-inferior to N95 respirators N = 1,009 healthcare workers in four countries, providing direct care to COVID-19 patients.

After 15 years as a TV reporter with Global and CBC and as news director of RDTV in Red Deer, Duane set out on his own 2008 as a visual storyteller. During this period, he became fascinated with a burgeoning online world and how it could better serve local communities. This fascination led to Todayville, launched in 2016.

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Pentagon agency to simulate lockdowns, mass vaccinations, public compliance messaging

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From LifeSiteNews

By Tim Hinchliffe

With lockdowns, mass vaccination campaigns, and social distancing still on the table from the last around, it appears that AI and Machine Learning will play a much bigger role in the next.

DARPA is getting into the business of simulating disease outbreaks, including modeling interventions such as mass vaccination campaigns, lockdowns, and communication strategies.

At the end of May, the U.S. Defense Advanced Research Projects Agency (DARPA) put out a Request for Information (RFI) seeking information regarding “state-of-the-art capabilities in the simulation of disease outbreaks.”

The Pentagon’s research and development funding arm wants to hear from academic, industry, commercial, and startup communities on how to develop “advanced capabilities that drive technical innovation and identify critical gaps in bio-surveillance, diagnostics, and medical countermeasures” in order to “improve preparedness for future public health emergencies.”

As if masks, social distancing, lockdowns, and vaccination mandates under the unscientific guise of slowing the spread and preventing the transmission of COVID weren’t harmful enough, the U.S. military wants to model the effects of these exact same countermeasures for future outbreaks.

The RFI also asks participants “Fatality Rate & Immune Status: How are fatality rates and varying levels of population immunity (natural or vaccine-induced) incorporated into your simulations?“

Does “natural or vaccine-induced” relate to “population immunity” or “fatality rates” or both?

Moving on, the RFI gets into modeling lockdowns, social distancing, and mass vaccination campaigns, along with communication strategies:

Intervention Strategies: Detail the range of intervention strategies that can be modeled, including (but not limited to) vaccination campaigns, social distancing measures, quarantine protocols, treatments, and public health communication strategies. Specifically, describe the ability to model early intervention and its impact on outbreak trajectory.

The fact that DARPA wants to model these so-called intervention strategies just after the entire world experienced them suggests that these exact same measures will most likely be used again in the future:

“We are committed to developing advanced modeling capabilities to optimize response strategies and inform the next generation of (bio)technology innovations to protect the population from biological threats. We are particularly focused on understanding the complex interplay of factors that drive outbreak spread and evaluating the effectiveness of potential interventions.” — DARPA, Advanced Disease Outbreak Simulation Capabilities RFI, May 2025.

“Identification of optimal timelines and capabilities to detect, identify, attribute, and respond to disease outbreaks, including but not limited to biosensor density deployment achieving optimal detection timelines, are of interest.” ­— DARPA, Advanced Disease Outbreak Simulation Capabilities RFI, May 2025.

With lockdowns, mass vaccination campaigns, and social distancing still on the table from the last around, it appears that AI and Machine Learning will play a much bigger role in the next.

For future innovation, the DARPA RFI asks applicants to: “Please describe any novel technical approaches – or applications of diverse technical fields (e.g., machine learning, artificial intelligence, complex systems theory, behavioral science) – that you believe would significantly enhance the state-of-the-art capabilities in this field or simulation of biological systems wholistically.”

Instead of putting a Dr. Fauci, a Dr. Birx, a replaceable CDC director, a TV doctor, a big pharma CEO, or a Cuomo brother out there to lie to your face about how they were all just following The ScienceTM, why not use AI and ML and combine them with behavioral sciences in order to concoct your “public health communications strategies?”

When you look at recently announced DARPA programs like Kallisti and MAGICS, which are aimed at creating an algorithmic Theory of Mind to model, predict, and influence collective human behavior, you start to get a sense of how all these programs can interweave:

“The MAGICS ARC calls for paradigm-shifting approaches for modeling complex, dynamic systems for predicting collective human behaviour.” — DARPA, MAGICS ARC, April 2025

On April 8, DARPA issued an Advanced Research Concepts (ARC) opportunity for a new program called “Methodological Advancements for Generalizable Insights into Complex Systems (MAGICS)” that seeks “new methods and paradigms for modeling collective human behavior.”

Nowhere in the MAGICS description does it mention modeling or predicting the behavior of “adversaries,” as is DARPA’s custom.

Instead, it talks at length about “modeling human systems,” along with anticipating, predicting, understanding, and forecasting “collective human behavior” and “complex social phenomena” derived from “sociotechnical data sets.”

Could DARPA’s MAGICS program be applied to simulating collective human behavior when it comes to the next public health emergency, be it real or perceived?

“The goal of an upcoming program will be to develop an algorithmic theory of mind to model adversaries’ situational awareness and predict future behaviour.” — DARPA, Theory of Mind Special Notice, December 2024.

In December 2024, DARPA launched a similar program called Theory of Mind, which was renamed Kallisti a month later.

The goal of Theory of Mind is to develop “new capabilities to enable national security decisionmakers to optimize strategies for deterring or incentivizing actions by adversaries,” according to a very brief special announcement.

DARPA never mentions who those “adversaries” are. In the case of a public health emergency, an adversary could be anyone who questions authoritative messaging.

The Theory of Mind program will also:

… seek to combine algorithms with human expertise to explore, in a modeling and simulation environment, potential courses of action in national security scenarios with far greater breadth and efficiency than is currently possible.

This would provide decisionmakers with more options for incentive frameworks while preventing unwanted escalation.

We are interested in a comprehensive overview of current and emerging technologies for disease outbreak simulation, how simulation approaches could be extended beyond standard modeling methods, and to understand how diseases spread within and between individuals including population level dynamics.

They say that all the modeling and simulating across programs is for “national security,” but that is a very broad term.

DARPA is in the business of research and development for national security purposes, so why is the Pentagon modeling disease outbreaks and intervention strategies while simultaneously looking to predict and manipulate collective human behavior?

If and when the next outbreak occurs, the same draconian and Orwellian measures that governments and corporations deployed in the name of combating COVID are still on the table.

And AI, Machine Learning, and the military will play an even bigger role than the last time around.

From analyzing wastewater to learning about disease spread; from developing pharmaceuticals to measuring the effects of lockdowns and vaccine passports, from modeling and predicting human behavior to coming up with messaging strategies to keep everyone in compliance – “improving preparedness for future public health emergencies” is becoming more militaristically algorithmic by the day.

“We are exploring innovative solutions to enhance our understanding of outbreak dynamics and to improve preparedness for future public health emergencies.” — DARPA, Advanced Disease Outbreak Simulation Capabilities RFI, May 2025.

Reprinted with permission from The Sociable.

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Audit report reveals Canada’s controversial COVID travel app violated multiple rules

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From LifeSiteNews

By Anthony Murdoch

Canada’s Auditor General found that government procurement rules were not followed in creating the ArriveCAN app.

Canada’s Auditor General revealed that the former Liberal government under Prime Minister Justin Trudeau failed multiple times by violating contract procurement rules to create ArriveCAN, its controversial COVID travel app.

In a report released Tuesday, Auditor General Karen Hogan noted that between April 2015 to March 2024, the Trudeau government gave out 106 professional service contracts to GC Strategies Inc. This is the same company that made the ArriveCAN app.

The contracts were worth $92.7 million, with $64.5 million being paid out.

According to Hogan, Canada’s Border Services Agency gave four contracts to GC Strategies valued at $49.9 million. She noted that only 54 percent of the contracts delivered any goods.

“We concluded that professional services contracts awarded and payments made by federal organizations to GC Strategies and other companies incorporated by its co-founders were not in accordance with applicable policy instruments and that value for money for these contracts was not obtained,” Hogan said.

She continued, “Despite this, federal government officials consistently authorized payments.”

The report concluded that “Federal organizations need to ensure that public funds are spent with due regard for value for money, including in decisions about the procurement of professional services contracts.”

Hogan announced an investigation of ArriveCAN in November 2022 after the House of Commons voted 173-149 for a full audit of the controversial app.

Last year, Hogan published an audit of ArriveCAN and on Tuesday published a larger audit of the 106 contracts awarded to GC Strategies by 31 federal organizations under Trudeau’s watch.

‘Massive scandal,’ says Conservative leader Pierre Poilievre

Conservative Party leader Pierre Poilievre said Hogan’s report on the audit exposed multiple improprieties.

“This is a massive scandal,” he told reporters Tuesday.

“The facts are extraordinary. There was no evidence of added value. In a case where you see no added value, why are you paying the bill?”

ArriveCAN was introduced in April 2020 by the Trudeau government and made mandatory in November 2020. The app was used by the federal government to track the COVID jab status of those entering the country and enforce quarantines when deemed necessary.

ArriveCAN was supposed to have cost $80,000, but the number quickly ballooned to $54 million, with the latest figures showing it cost $59.5 million.

As for the app itself, it was riddled with technical glitches along with privacy concerns from users.

LifeSiteNews has published a wide variety of reports related to the ArriveCAN travel app.

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