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We all want this crisis to end. Read this. Then find a mask and put it on when you go out in public

Published

5 years ago

14 minute read

This is article is abridged for your convenience.

Public use of masks to control the coronavirus pandemic

(Originally published March 29 by Longrich Paleo Lab)

Nicholas R. Longrich, PhD

Department of Biology and Biochemistry, University of Bath, Bath, United Kingdom

The Longrich Paleontology Lab is part of the Milner Centre for Evolution at the University of Bath. We use fossils to understand large-scale evolutionary change in organisms and ecosystems. 

The US and UK governments, as well as the World Health Organization, currently advise against the use of masks by the public to fight the ongoing Coronavirus Disease 19 (COVID-19) pandemic (1). But could they be wrong?

The governments of China, South Korea, Hong Kong, Viet Nam, Czechia, Slovakia, Bosnia and Taiwan all recommend that the public wear masks to slow the spread of the coronavirus. In some countries, like Japan, masks aren’t officially recommended, but are still widely used by the public. Many countries treat masks as a strategic resource. China has ramped up production of facemasks, converting Foxconn factories that once made iPhones to make face masks. Taiwan has also ramped up the production of facemasks, prohibited their export, and implemented price controls and rationing. It’s hard to see how both approaches could be right. Increasingly, advice against the use of face masks has been questioned (1) (2) (3), including by the head of China’s CDC (4). Austria has recently moved to make mask wearing in public obligatory, and in the United States, the CDC is now debating their use.

Common sense, scientific studies, but perhaps most of all the success of countries using masks to fight the coronavirus suggest that masks may make a difference. There are fewer scientific studies available to guide decision making than we might like, and the evidence is not always clear-cut. However, decision-making in a crisis requires that decisions be made in the absence of perfect clarity. What is clear is that the exponential mathematics of pandemics mean that even if masks are of limited benefit in reducing infection rates, masks could make a large difference over time, potentially slowing the pace of the pandemic, limiting its spread, saving lives, and finally, letting countries to restart the economies that their people depend on for their livelihoods.

Figure produced by Johns Hopkins University using data from Worldometers on March 29.

Masks protect you from others, others from you

It seems sensible to assume that any barrier between two people’s airways reduces the chance of an air-borne virus being transmitted between them. Masks worn by infected people catch some fraction of virus-laden respiratory droplets that are released by breathing and coughing. Perhaps just as important, breathing through a mask slows and deflects air as it is exhaled, potentially reducing the distance that viral droplets travel as aerosols.

Meanwhile, masks worn by uninfected people catch a fraction of the virus they’d otherwise inhale. If both infected and uninfected people wear masks, then these effects multiply. For example, hypothetically, if an infected person’s mask reduces the amount of virus spread by 75%, and the uninfected person’s mask reduces it by another 75%, then the total reduction of the virus spread is 94%.

It’s still possible that this reduction isn’t enough to prevent infection. However, masks could still protect people— because dosage matters. Lower dosing of virus means infection takes longer to build up, giving the immune system time to mount a response.

The immune system fights viruses, like a farmer trying to remove weeds from his field. How difficult those weeds will be to control depends on how many seeds there are. 1000 seeds in a field might not be a challenge, but 1,000,000 or 100 million make weeding far more difficult. In the same way, even when masks fail to prevent infection, by lowering the initial dose of virus they could  conceivably make the difference between mild symptoms and a severe illness requiring hospitalization, or even leading to death.

Models suggest masks could work to control pandemics

Of course, it’s possible that masks might have only limited benefit in stopping the spread of COVID-19— for any number of reasons.  Masks might provide limited protection, because they are less effective than suggested by some studies, because people misuse them, because of shortages of effective masks like surgical masks and N-95s— or all of these.

But to understand how they could still make a difference, we have to consider masks in the context of small reductions in viral transmission rates. Consider how epidemics grow— exponentially. Allowed to spread unchecked, one case of Covid-19 becomes 2.5 (assuming for this model an R0 of 2.5), each case causing 2.5 more, and so on. Over the course of 15 reproductive cycles, each taking 7 days, or about 3 months in total, one case becomes 2.5 x 2.5 x 25… or 2.5^15 =   931,323 cases (Fig. 1).

1 Figure 1 Small Reductions in R.png

Figure 1. A simple model showing exponential growth in an uncontained outbreak over time (generation time = 7 days, R0 = 2.5) and with small reductions in the reproductive rate R.

Now, let’s suppose widespread use of masks cuts the growth rate by just 10%. Each person now infects 2.25 others, who infect 2.25 others, and so on. Over 15 cycles, 2.25^15 = 191,751 cases. An 80% reduction. Understanding this exponential growth explains how the virus caught the world by surprise even as the pandemic was monitored in real time. Exponential growth just doesn’t make sense, until you do the numbers, and even, they’re still hard to believe. But another counterintuitive aspect of exponential growth is that small decreases in the exponent greatly slow growth. A 10% increase in the exponent can have a massive effect, but even a limited intervention, with a 10% decrease over time, pays large dividends (Fig. 1).

These are very, very simple models. But sophisticated modeling also shows large scale use of masks could slow, even stop pandemics. A 2010 study found that above a certain threshold, widespread use of effective masks can reduce the reproductive number (R) of an influenza virus below 1, and the pandemic stops (25). If face masks were highly effective (well-designed, used properly and consistently), then public use of masks could stop a flu pandemic if used by just 50% of people. If masks were less effective, more than half the population would have to wear them to stop the pandemic. If masks were highly ineffective, they could flatten the curve of the epidemic, but wouldn’t stop it (25). We don’t know which model is most accurate. But does it even matter? In the context of the current pandemic, any of these scenarios would be a huge win.

Real world experience suggests masks work in pandemics

The most compelling evidence of the potential effectiveness of masks in the fight against COVID-19 comes from their use in the real world. Places that have controlled their coronavirus epidemics most effectively – China, South Korea, Hong Kong, Taiwan, Vietnam, Singapore, Kuwait, Czechia, Slovakia, Japan- use masks (Fig. 2). Aside from China, which was the epicenter of the pandemic and so played catchup in developing and implementing its strategy, virtually all of the worst outbreaks are in Western countries that officially advise against mask use, and where there is little culture or practice of mask wearing.

2 Figure 2 31st.png

Figure 2. Western countries (US, Canada, Australia, UK, Western Europe) versus countries and territories using masks as part of official government or in practice policy (China, South Korea, Japan, Hong Kong, Taiwan, Vietnam, Thailand, Kuwait, Slovakia, Czech Republic, in blues and greens). Countries with official or unofficial policies of mask usage have controlled the outbreak far better than those without. Note that Austria currently uses masks but has only revised its official policy recently.

Places like China, South Korea, Taiwan, Vietnam, Kuwait, Czechia and Singapore differ greatly in political organization, ranging from communism to democracies, and also in their level of economic development and population density. And strikingly, these countries also differ in their suppression strategies. China implemented a lockdown of Wuhan, shut down industry nationwide, implemented temperature checks and social distancing, tested extensively— and employed masks. Korea responded with an aggressive testing and contact tracing—and masks. Japan has done far less extensive testing than Korea, but shut down schools and large gatherings— and used masks. The pandemic management strategies used by these countries far more diverse than has been appreciated. Arguably one of the few things all these successes share is widespread wearing of masks. And on the other hand, one common factor shared by the pandemic suppression strategies of the US, Canada, the UK and Europe is the decision to discourage the use of masks by the public. This evidence doesn’t prove, but it does very strongly hint that masks are a critical part of these country’s suppression strategies. And by watching countries like Austria that have recently revised their policies, we can test this idea.

What kind of mask? Surgical masks as good as N95s; are improvised masks better than nothing?

Would cloth masks work? Research into the effectiveness of cloth masks is limited  (34). Existing research shows homemade masks are- unsurprisingly- inferior to surgical masks. However, they appear to be better than nothing. One laboratory study found homemade masks were half as effective as surgical masks in filtering particles (35). Another study found homemade masks made from various materials stopped virus aerosols, but less well than surgical masks (36). A surgical mask stopped 90% of viral aerosol particles, a dish towel, 72%, linen, 62%, and a cotton T-shirt, 51% (36).

Conclusions

Strong scientific evidence and rational arguments exist for the widespread, public use of facemasks. The principle behind facemasks- they reduce the amount of virus exhaled by infected people, and inhaled by uninfected- suggest they should be a primary tool in combating any respiratory virus. Scientific research, including experimental studies, retrospective studies of the SARS epidemic, hospital studies of COVID-19, and modeling studies, all suggests masks are likely to be effective in controlling the pandemic. Most importantly, the experience of countries using masks against SARS and the current coronavirus pandemic imply that they are effective when used by the public. However, modeling studies and the real-world experience of countries like China and South Korea suggests that neither masks, nor anything else, provides a magic bullet against a pandemic. So strategies should not rely on any single intervention, but rather a wide range of interventions, potentially including masks. Further research and open debate on the effectiveness of masks and other strategies are urgently needed.

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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

Published on June 12, 2025

By

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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Business

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

Published on June 11, 2025

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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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