The causative agent for COVID-19 was identified by next-generation sequencing from samples received from several pneumonia patients. The detection of the coronavirus was done through real-time polymerase chain reaction analyses performed from plasma sample. Ribonucleic acid extracted from a patient’s bronchoalve-olar lavage fluid was used as a template to clone and sequence a genome from lineage B of the betacorona-virus. A polymerase chain reaction test was considered positive using cycle threshold above 34 for detected samples. Tests with thresholds so high will detect genetic leftover fragments, which pose no particular risk. No correlation between viral load and disease severity has ever been connected to a virus. On the basis of the little science centered on misleading mortality calculations and most importantly on mathematical disease modeling, generalized lockdowns were imposed on a global scale, with a considerable number of fatalities and resulting in global economic breakdown. Most studies found little to no evidence for the effectiveness of face masks in the general population, neither as personal protective equipment nor as a source control. In this review, we attempted to expose a different perspective of some aspects of the pandemic related to disease diagnosis and protective nonpharmacologic interven-tions. Our viewpoint raises serious questions on the validity of such restrictive measures in saving lives and warn against the application of such damaging strategies in a potential future outbreak.

Key words : Face masks, Lockdown, Polymerase chain reaction testing, SARS-CoV-2 immunity

Introduction

On December 31, 2019, an outbreak started in Wuhan, China, caused by a novel coronavirus 2019-nCoV (named SARS-CoV-2). The pathogen responsible was identified as a novel betacoronavirus in the same family as SARS-CoV and MERS-CoV, via next-generation sequencing from samples received from several affected patients.^1-4 Shortly after, the World Health Organization (WHO) announced a worldwide pandemic.^5-7 The rapid sequencing of SARS-CoV-2 identified nearly 30 000 nucleotide bases that hold the genetic sequence of the virus.^1,4 When the many deposited SARS-CoV-2 strains were compared, a bat coronavirus sequence, RaTG3, was subsequently identified with 96% sequence similarity with the novel virus.⁸ In the 2 initial clinical reports from Wuhan, China,^9,10 a reverse transcriptase–real-time polymerase chain reaction (RT-PCR) test was considered positive using high values of cycle threshold greater than >34 cycles¹⁰ or greater than >50 cycles⁹ for detected samples. Tests with thresholds >30 cycles most likely detect genetic leftover fragments, which pose no particular risk. However, several later reports revealed that part of the genetic sequence of COVID-19 pathogen does not exist in nature for SARS-like viruses, suggesting that the virus might have been engineered in a laboratory.^4,11-13

Shortly after the declaration of the pandemic, generalized lockdowns were enforced on a global scale, with a considerable number of fatalities and resulting in global economic breakdown. Home confinement, face masks, and distancing were imposed worldwide as effective measures to slow down the progression of the pandemic. These strategies were based on scarce scientific evidence centered on misleading mortality calculations and most impor-tantly on mathematical disease modeling.^14-18

Concomitantly, several vaccines designed by different pharmaceutical companies and in different countries entered phase 3 clinical trials in humans.^19-21 The most innovative ones were the Pfizer-BioNTech’s BNT162b2 (United States-Germany) and the Moderna mRNA-1273 (United States), which used exosome technologies that included genetic material messenger RNA (mRNA) encapsulated in lipid nanoparticles. On December 31, 2020, Pfizer-BioNTech²² and Moderna²³ published their trial results of 2-month follow-up, from nearly 44 000 and 30 000 volunteers, respectively. The studies were only designed to test vaccine safety in the short-term and triggering of an immune response that may offer adequate protection against COVID-19. Findings from subsequent scientific reports questioned the effectiveness of these vaccines in providing pro-tective immunity, despite boosting measures, and raised concerns on their safety.^24-30

Herd immunity was not attained as promised, and the virus continues to spread despite global wide-scale vaccination. Informed consent was bypassed despite the experimental nature of the vaccines, their inability to stop infection, and their alleged serious adverse events.^21,29 In the absence of any legal, ethical, or scientific validity, vaccine mandates were imposed in many countries in public and private domains and on international and domestic travels, leading globally to the disruption of human lives at the social, medical, and economical levels.^14,15,30,31

In the first part of our review,³² we proposed an exosomal biogenesis of SARS-CoV-2. Under the influence of coexisting multiple different stressors with additive or multiplicative harmful biological effects, the outbreak was induced by a massive production by exposed mammals of chimeric nano-particles or exosomes containing molecular material of different genetic origin.^33-38 This shedding of tiny particles in an overcrowded ambient environment led to a considerable exosomal-mediated antigenic and nucleic acid exchange within and between different mammal species (labeled mammalosis) and the resulting host-dependent alloimmune response.

In the context of these uncertainties and con-troversies, this second part of our review represents a critical scientific appraisal of (1) the validity of PCR testing in the diagnosis of COVID-19 cases and (2) the usefulness of the nonpharmaceutical interventions (NPIs) in preventing SARS-CoV-2 infection and in slowing down the progression of the pandemic. In the third and last part, we will examine the efficacy and safety of anti-COVID-19 mRNA vaccines in immunocompetent and immunocompromised indi-viduals and the scientific, ethical, and legal validity of the vaccine’s mandates.

COVID-19 Diagnosis: Validity of Polymerase Chain Reaction Tests

The RT-PCR selects one sequence of the viral genome and transforms RNA into DNA to amplify it into a large number of copies. Polymerase chain reaction greatly amplifies signals but also greatly amplifies the noises. The amplification is exponential, so the slightest contamination can result in errors of great magnitude. Several technical deficiencies known to affect PCR assay performance include the following: (1) inadequate sample storage, nucleic acid preparation, and quality, yielding highly variable results; (2) poor choice of reverse-transcription primers and probes for the PCR, leading to inefficient and less than robust assay performance; and (3) inappropriate data and statistical analyses, generating results that can be highly misleading. Consequently, there is the real danger of the scientific literature being corrupted with a multitude of publications reporting inadequate and conflicting results.³⁹

Real-time PCR provides quantitative information, recorded as the cycle threshold (Ct) value, about the number of nucleic acid copies detected in a diagnostic sample. The Ct value correlates with the number of copies in an inversely proportional and exponential relationship.⁴⁰ The fewer cycles required, the greater the amount of viral RNA load in the sample; the higher the cycle number, the greater the build-up of the fluorescence background and the more likely the test will produce a false positive (Figure 1). Currently, there is a lack of consensus on how best to perform and interpret quantitative real-time PCR experiments.³⁹ Ct values are interpreted as positive, indeterminate, or negative based on assay-specific cutoffs and evolving clinical guidelines.⁴¹ Cutoff values for Ct vary between different test kits.

As of July 14, 2020, 37 tests were approved by the US Food and Drug Administration (FDA) under emergency use authorization conditions. Recom-mended cutoffs of PCR cycle number varied widely from 30 to 45 cycles. A Ct value of 40 cycles was most popular, with also a recommendation between 43 and 45.⁴² The test detects 1 or more than 1 RNA sequence, and different kits use different sequences. On September 23, 2021, the FDA revised emergency use authorizations of certain molecular, antigen, and serology tests to establish additional conditions of authorization in response to the continued emer-gence of new variants of SARS-CoV-2, with the most recent update released on January 12, 2023. The revision required test developers to update their authorized labeling and evaluate the impact of SARS-CoV-2 viral mutations on their test’s performance.⁴³ Because of differences in nucleic acid extraction method, viral target, and other parameters, Ct values are also not directly comparable across assays or technology platforms.⁴⁴ The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines recommend that data with 40 or more cycles should be discarded and some feel that 35 is a better cutoff.³⁹ Tests with cycle thresholds higher than 30 will likely detect genetic leftovers fragments, which pose no particular risk (Figure 1).

According to a report released by WHO on July 9, 2020, “the detection of RNA using RT-PCR-based assays is not necessarily indicative of a replication- and infection-competent (viable) virus that could be transmissible and capable of causing infection.”⁴⁵ Although Ct values may better correlate with infectivity of SARS-CoV-2, they cannot be directly interpreted as viral loads without calibration to a quantitative standard.^46,47 An RT-PCR has never been tested against a gold standard.⁴⁸ As recently reported in a systematic review on the effectiveness of PCR tests to detect the presence of SARS-CoV-2 virus, estimates of diagnostic accuracy should be interpreted with the consideration of an absence of a definitive reference standard to diagnose or rule out COVID-19 infection.⁴⁹ In addition, RT-PCR may remain positive even for a month, and merely the detection of the virus does not correlate with infectivity or viability of the virus.^41,50,51 Astonishingly, in the 2 initial clinical reports from Wuhan,^9,10 RT-PCR tests were considered positive in some but not all reported patients using high values of cycle threshold greater than 34 cycles¹⁰ or 50 cycles⁹ for detected samples. As mentioned, tests with thresholds so high (Ct >30) may detect not just live virus but also genetic fragments, which are leftovers from infection that pose no particular risk⁵² (Figure 1). These findings strongly questioned the accuracy of the test and hence the validity of the initial identification of the novel virus SARS-CoV-2 genome.

SARS-CoV-2 has never been purified and was apparently visualized from human cells from 1 Wuhan patient.⁹ The RNA was extracted from bronchoalveolar lavage fluid obtained from the affected patient and centrifuged. Supernatant was inoculated on human airway epithelial cells, obtained from a patient undergoing surgery for lung cancer, and then purified and observed under transmission electron microscopy for 6 days. The so-called “virus particles” identified by electron micrographs had a diameter that varied from about 60 to 140 nm. These nanoparticles were named SARS-CoV-2. Of note, there is no evidence that the RNA being used in the COVID-19 PCR tests were found in those particles because one cannot identify the nature of the contents. In addition, no autopsy was performed on patient 2, and no lung biopsies were performed on any of the patients to isolate viral pathogen. Therefore, there is no connection between the PCR test and the particles and no proof that the particles are viral. These findings do not fulfill Koch’s postulates.⁵³

The so-called cytopathic effect is also nonspecific and may be caused instead by the in vitro culture conditions that can induce oxidative stress leading to cell damage.⁵⁴ Therefore, any cellular components such as proteins or fragments of nucleic acids identified in a cell culture could represent waste products and may not be considered as virus antigen or genetic material belonging to a virus. In the second initial clinical report,¹⁰ no test was performed to detect infectious virus in blood. The authors stated, “we did not perform tests for detecting infectious virus in blood, we avoided the term viremia and used RNAemia instead.” The main limitations of the study were (1) diagnosis was confirmed with lower respiratory tract specimen that was not paired with nasopharyngeal swab, (2) serological detection was not done to look for SARS-CoV-2 antibody increases in 18 patients with undetectable viral RNA, and (3) kinetics of viral load and antibody titers were not available.

The PCR tests involve a multistep process, and any error at any stage could result in an incorrect result.⁵⁵ The accuracy of the test depends on its specificity (% of true positive) and sensitivity (% of false negative).^48,49 These 2 parameters are affected by several factors such as (1) infection prevalence,⁵⁵ (2) clinical manifestations,^48,56 (3) stage of disease,⁵⁷ (4) degree of viral multiplication or clearance,⁵⁸ (5) swab sampling quality and site,^59-61 (6) Ct values used,^{9,10,39,43,44} (7) laboratory experience,^39,55 and (8) kind of the PCR test kit used in relation to which gene target is used and whether multiple gene targets are used in combination.^62,63

According to a recent systematic review, the accuracy of COVID-19 tests reported false negative rates of between 2% and 29% (equating to sensitivity of 71%-98%), based on negative RT-PCR tests, which were positive on repeat testing.64 Lee performed a laboratory analysis of the PCR test from the Centers for Disease Control and Prevention (CDC), which was widely used in the first months of the pandemic, and found it had a 70% specificity (30% false positives) and 80% sensitivity (20% false negatives).⁶⁵ This is because of faulty designs built into the test from the beginning, as various news accounts from several sources have since revealed. This level of inaccuracy matches the CDC’s own internal report that found 33% false results when its PCR test was released in late February 2020.⁶⁶ These findings suggest that PCR tests are certainly not close to a 99% specificity level in practice as believed, for various reasons as recently reported by Braunstein and colleagues.⁶⁷ Even a test with a very high 99% specificity (1% chance of false positives) will yield high levels of false positives when used to screen asymptomatic populations in a background of low disease prevalence,⁵⁶ as observed in the Moderna trial that included ~30 000 participants²³ with 0.6% positive PCR at initial testing of the participants, who were selected based on being at higher risk of exposure to the virus, and the Johnson & Johnson vaccine clinical trial that included almost 40 000 participants in more than 6 countries, with a 0.5% PCR positive baseline.⁶⁸ Therefore, even a test with a very high 99% specificity (1% chance of false positives), when used to screen asymptomatic populations with a low background rate of actual infection, will yield high levels of false positives.⁵⁵

During the SARS pandemic in 2004, the CDC guidance was as follows: “To decrease the possibility of a false-positive result, testing should be limited to patients with a high index of suspicion for having SARS-CoV disease.”⁶⁹ In contrast, early on during the COVID-19 pandemic, both WHO and CDC recommended intensive testing of even asymptomatic people. However, the CDC flip-flopped its decision by revising this guidance in August 2020 to recommend not testing asymptomatic people even after potential exposure, only to reverse course again after public and expert pushback in the United States.⁷⁰ In a recent study, it was shown that viral load in asymptomatic patients was similar to that in symptomatic patients.⁶⁰ Of note, the presence of viral RNA in specimens does not always correlate with viral transmissibility.⁷¹ Most importantly, a correlation between viral load and disease severity and RNA tests has never been established.^9,10,60 Moreover, the accuracy of viral RNA swabs in clinical practice varies depending on the site and quality of sampling.⁴⁸ In one study, the sensitivity of RT-PCR in 205 patients varied, at 93% for bronchoalveolar lavage, 72% for sputum, 63% for nasal swabs, and only 32% for throat swabs.⁵⁹ Interestingly, in the product information of the SARS-CoV-2 Coronavirus Multiplex RT-qPCR Kit, the manufacturer cautioned that the kit is for research use only and is not intended for diagnostic use, in addition to nonspecific interference of several commonly known respiratory viral and bacterial pathogens.⁷²

In the clinical setting, PCR testing is unreliable or at least far less reliable than previously thought. This doubtful accuracy of the PCR test is related to the many valid concerns regarding one or more of the following: (1) dubious validity of the initial RNA sequence of the novel virus isolated from the Wuhan patient, (2) absence of RT-PCR testing against a gold standard, (3) logistic, technical drawbacks and uncertainties regarding the testing process, (4) considerable increase in the risk of false positivity in the context of the low prevalence of COVID-19 and the high proportion of COVID-19 asymptomatic patients, (5) lack of correlation between viral load and disease severity, (6) presence of viral RNA in specimens that does not always correlate with viral transmissibility, and (7) the inability to differentiate between infective and noninfective viruses. The RNA test has never been connected to a virus, and it won’t unless the virus is purified.

The questionable accuracy and the many above mentioned limitations of the PCR test lead one to question the validity of the extent and magnitude of the whole corona pandemic that was and still mainly based on the utilization of the PCR testing for case identification and clinical diagnosis.^30,73 In fact, several reports from Europe highlight that, at the end of the first epidemic curve, most people had no evidence of past infection. In Geneva, about 1 in 10 people developed detectable antibodies against SARS-CoV-2, despite the fact that it was one of the more heavily affected areas in Europe.⁷⁴ Similarly, in Spain, one of Europe’s worst hit countries, only 5% of people tested positive for antibodies against SARS-CoV-2 in places with high infection rates like Madrid.⁷⁵ Similar results were reported in the United States, varying between 1% in San Francisco to 6.9% in New York.⁷⁶ These findings may be explained by either an inaccurate PCR testing responsible for an overestimation of false positive cases and/or inac-curate serology testing by enzyme-linked immuno-sorbent assay technique missing many seroconverted cases. Other unlikely possibilities could include rapid loss of natural immunity or viral escape of the immune system preventing the formation of antibodies.

COVID-19 Protective Measures: Nonpharmaceutical Interventions

Lockdowns
Shortly after the declaration of the pandemic by the WHO, on January 30, 2020, the first confirmed cases were 2 Chinese tourists already hospitalized 1 day before in Rome. On January 31, 2020, 1 day later, a “state of emergency” was declared. On February 21, 2020, the first indigenous case, a 38-year-old Italian man, was confirmed positive in Codogno (named as “patient 1”). The Codogno patient had no detectable connection to Wuhan, suggesting that community transmission had been ongoing for some time. How patient 1 was infected remains a mystery. In the next 2 days, 36 new cases appeared, none of which had contact with the first patient or anyone with the COVID-19 infection. This was the beginning of one of the largest and most uncontrolled groups of patients with COVID-19 in the world, which rapidly triggered intracommunity transmission in Italy.¹⁷ On March 8, 2020, the preexisting local quarantine expanded to most of Northern Italy (Lombardy and other provinces) and 1 day after to all of Italy (>60 million people in quarantine). The rest of the world followed suit like dominoes in the following weeks and months. By June 22, 2020, the case fatality rate (CFR) in Italy was estimated to be around 14%, amounting to nearly 561 deaths/million population and representing one of the worst-hit regions of the world in terms of per capita deaths. Italy was the world’s first country with a nationwide lockdown and curfew. The government imposed on March 11 an emergency law “Io resto a casa” (I stay at home).¹⁵ The Italian scenario was terrifying but also became a model to follow for other nations.

These unprecedented drastic interventions into the lives of populations occurred in the name of health on a global scale and in such a short period of time. The generalized lockdowns, globally and mainly in poor countries, resulted in considerable negative effects at the economic, social, psychological, and medical levels. Imposing such extreme measures have affected billions of people and have pushed societies to the edge of collapse by creating poverty, hunger, misery, debt, and unemployment. Poor, marginalized, and vulnerable groups were the most affected by the drastic measures, exacerbating already existing inequalities.¹⁴ Most strategies were politically orchestrated and mandated and based on a low level of scientific evidence centered on inappropriately and therefore misleading mortality calculations and most importantly on mathematical disease modeling.^16,18 In many countries, governments officials emphasized on the importance of individual responsibility to divert attention away from all the already preexisting structural and material deficiencies and planning errors behind the tragic death rates, which varied between countries and among world regions, with highest CFRs in the Americas, Europe, and Asia, excluding China.^7,16,32

These protective strategies were scientifically unfounded and originally based on a nonpeer-reviewed report¹⁸ published on March 16, 2020, by Dr. Neil M. Ferguson, a British epidemiologist and professor of mathematical biology, and colleagues at Imperial College London on the effects of NPIs to reduce COVID-19 mortality and healthcare demand. The group proposed a nonpeer-reviewed and a zero evidence-based microsimulation epidemiological model to policymakers in the United Kingdom. The modeling predicted that the epidemic would likely result in hundreds of thousands of deaths with an already deficient healthcare systems, mainly intensive care units, being overwhelmed many times over. To reduce peak healthcare demand by two-thirds and deaths by half, the model recommended 2 fundamental strategies: (1) mitigation, which focuses on slowing but not necessarily stopping epidemic spread, thus reducing peak healthcare demand while protecting those most at risk of severe disease from infection, and (2) suppression, which aims to reverse epidemic growth, reducing case numbers to low levels and maintaining that situation indefinitely.

Optimal attenuation policies consisted of combining home isolation of suspect cases, home quarantine of those living in the same household as suspected cases, and social distancing of the elderly and others at most risk of severe disease. Suppression would, at minimum, require a combination of social distancing of the entire population, home isolation of cases, and household quarantine of their family members. These efforts may need to be supplemented by school and university closures. The report emphasized the importance of maintaining these types of intensive interventions strategies until a vaccine became available since transmission will quickly rebound if protective measures are relaxed. The authors of this report supported their predictions by the local and partial lockdowns imposed in China and South Korea, showing that suppression is possible in the short term. The report recognized that such severe measures may have negative effects on social, economic, and health systems due to increased absenteeism. It also questioned whether NPIs adopted thus far could be maintained in the long-term and whether their costs could be reduced.

Ferguson had become one of the most trusted voices on the outbreak and the country’s foremost epidemiologist and government adviser on the pandemic trajectory. He took a prime role in discussing the reasons for the lockdown and social distancing measures.⁷⁷ On May 5, 2020, Ferguson resigned after breaking the lockdown rules to meet his married lover while lecturing the public on the need for strict social distancing.^78,79 The English government-advising scientist left his position after it emerged that his lover had visited his home on 2 occasions, shortly after the release of the report, in a breach of the government’s official guidance on social distancing that had been ironically based on his own recommendation.^78,79 While he had only just finished a 2-week self-isolation after testing positive for coronavirus, he allowed his lover who lived in another house with her husband and children, to visit him at home during the lockdown. Shockingly, Prof. Ferguson had frequently appeared in the media to support the lockdown and praised the “very intensive social distancing” measures. The scandal was detailed on the front pages of British media instead of the reporting on the same day of the highest official death rate in Europe despite lockdown, which overtook that of Italy.

Recently, the Telegraph obtained more than 100 000 WhatsApp messages sent between Matt Hancock, health minister during the pandemic, and other ministers and officials at the height of the COVID-19 pandemic. The “Lockdown Files”⁸⁰ revealed conversations among government officials that shed new light on issues including care home deaths, lockdowns, testing, school closures, and face masks. The British cabinet’s most senior civil servant, Simon Case, warned Prime Minister Boris Johnson of the “terrible” consequences of lockdown. He insisted that “we have to be brutally honest with people over the consequences of lockdown—including the effects on non-COVID health, social cohesion, mental health, education, and jobs.” The WhatsApp messages reveal serious concerns raised by senior ministers of the collateral damage caused by lockdowns to prevent the spread of COVID-19. The ministers and officials behind lockdowns were well aware of the possibility, and then the reality, of collateral damages affecting millions of lives. They pushed ahead with the controversial policy, despite warnings that the cure would be worse than the disease.⁸¹ Ministers were worried that non-COVID deaths would be increased by the public not being checked for “minor ailments” that could later turn into acute problems later. The files also disclosed an alarming rise in the “sad deaths of children” in mental health inpatient units across England, as well as fears over an “upcoming epidemic” in children’s respiratory viruses caused by lockdowns suppressing infections.

By the end of April 2020, the Telegraph reported a new scientific analysis⁸² from the England National Health System warning that a second and then a third wave of non-corona deaths were about to hit Britain unless radical solutions could be found to resume normal services and slash waiting lists. At the time of the Telegraph report,⁸² the second wave was already breaking. It was made up of non-COVID-19 patients who were not able or willing to access healthcare because of the crisis. Reasons for healthcare included a wide range of typical emergency admissions, including stroke and heart attack patients, as well as those with long-term chronic conditions who were not able to access the care services that they needed. Many died in their homes, while others were just getting to hospital too late, received early palliative care, or were subjected to aggressive medical treatments. Moreover, by early May, scientific reports revealed that the staggering number of extra deaths in the community at that time could not be explained by COVID-19. Only one-third of the excess deaths seen in England and Wales could be explained by the coronavirus.⁸³ Care homes and other community settings had to deal with an overwhelming load of 30 000 more deaths than would normally be expected, as patients were moved out of hospitals that were anticipating high demand for beds according to the modeling prediction.¹⁸ In fact, many of these deaths were among people “who may well have lived longer if they had managed to get to hospital.”

By the end of October, the British Prime Minister announced the second lockdown in England to save lives and protect, while the government was well aware of the knock-on effect on the future death toll and the strain on hospitals, as the Telegraph’s “Lockdown Files” suggested. Interestingly, the Minister of Health rejected advice from England’s Chief Medical Officer to replace the 14-day COVID quarantine with 5 days of testing because “it would imply we’ve been getting it wrong” despite being informed that the 14-day quarantine period was likely to have been “too long all along.” Moreover, the same Minister continued to resist loosening up strategies recommended by top government advisers saying it “sounds like a massive loosening” and that removing the quarantine requirement could make it appear that ministers had made a mistake. His reticence about reopening the country was maintained regardless of the costs or even if there was little scientific justification for doing so. One of the most alarming messages that were leaked revealed that, in December 2020, the Prime Minister suggested that the government “frighten the pants off everyone” to ensure adherence strict COVID rules.⁸⁴

This psychological warfare was imposed through “Project Fear,” in which the government used tactics to force compliance with COVID rules.⁸⁵ It was evident that the intention was not just to educate people but to terrify them into compliance with the rules. The Lockdown Files also revealed how the Health Minister struggled to speed up the roll out of the vaccine, saying that the approval of “jabs” was a “Hancock triumph.” He feared being overshadowed by other government officials. He fought to take credit for the success of Britain’s vaccine campaign success, telling colleagues: “Everyone knows I’m Mr. Vaccine and this is the route out.”⁸⁶ He was reported to be being furious if he thought others were getting the credit and discussed with his media adviser how taking credit for the vaccine would allow the public and media to “forgive” him for backing lockdowns that removed their liberty. He also had battled with his cabinet colleagues over who should have overall control of the vaccine procurement strategy.⁸⁷

Saturation of an already collapsed healthcare system might at least be in part responsible for the observed increase in fatality rates in many of the European countries and the Americas with consi-derable environmental pollution and an aging population with other comorbidities.^32,88-94 There is no clear and robust evidence of increased deaths worldwide from all-cause mortality during the pandemic, suggesting COVID-19-related deaths may be part of a noncommunicable disease mortality pool.³² Countries with collapsed health systems and low numbers of beds per 1000 habitants had the highest number of direct and indirect COVID-19-associated deaths per million population.^7,17,95 In addition to these deaths, other fatalities were caused by the consequences of isolation and home confinement, with the loss of social interaction, poor diet and nutrition, germaphobia, alcoholism, drug abuse, domestic violence, anxiety, and depressive disorders, with noticeable global increase in suicide rates.^42,82-85,96 Daily SARS-CoV-2 infection rates and reductions in human mobility were associated with increased prevalence of major depressive disorders and anxiety disorders by 27% and 25%, respectively.⁹⁶

Unfortunately, the scientifically and economically unfounded Italian and British COVID-19 model was followed and applied by most countries around the world despite the early release in the pandemic of several scientific reports showing little or no benefit of these drastic measures. Ioannidis and colleagues assessed case growth in countries like Sweden and South Korea, 2 countries that did not implement mandatory stay-at-home and business closures, and compared the countries with 8 other countries where restrictive NPIs were applied. The study found no clear significant beneficial effect of more restrictive NPIs on case growth in any country and concluded that similar reductions in case growth may be achievable with less restrictive interventions.⁹⁷ Recently, a systematic review of observational studies from 150 countries was published on the effectiveness of school closures and school reopenings on COVID-19 transmission in the community. The authors concluded an uncertain beneficial effect because of the smaller role that children play in transmission of the disease. Moreover, because of data heterogeneity, the authors were unable to conduct a meta-analysis. With such varied evidence on effectiveness, and the harmful effects, they recommend a measured approach before implementing school closures.³¹

Confinements significantly reduced the physical and intellectual abilities of children. A French study found that confined young children gained 5 to 10 kg with considerable reduction in their physical capacities and 40% drop in their cognitive abilities.⁹⁸ Other reports revealed considerable exaggeration by the pandemic of a preexisting multifactorial accelerated aging in children and adolescents.⁹⁹

These stringent protective measures were politically orchestrated and imposed by governments to reduce overestimated COVID-19-related fatalities that were theoretically predicted by mathematical modeling, a feature that is challenging and problematic to determine. Unfortunately, different tactics for counting deaths and changes in testing policy occurred in many countries and across regions, resulting in variations in mortality rates among countries (Figure 2)¹⁰⁰ and world regions (Figure 3).⁷ Different incomparable methods were adopted to calculate the number of fatalities such as (1) cumulative number of deaths, which is misleading, (2) number of confirmed death cases per million population, (3) CFR (number of confirmed death cases per number of positively tested cases), and (4) infection fatality rate (IFR), representing the number of confirmed death cases per number of population at risk.⁷ In the latter 2 indicators (CFR and IFR), death rate is a function of the denominator, which is extremely difficult to determine101 since it can vary from country to country and from region to region with regard to testing policy and prevalence of the infection.^74-76

Moreover, the mortality rate is affected by many determinants such as death case definition, quality of the healthcare system, socioeconomical level, statistical analysis methods, environmental pollution, and the patient’s characteristics in term of aging and comorbidities.^{7,16,32,42,88-94} Astonishingly, on March 24, 2020, the National Vital Statistics System (NVSS) under the direction of the CDC and NIH, instructed physicians, medical examiners, and coroners that COVID-19 would be recorded as (1) the underlying cause of death “more often than not,” (2) cause of death listed in part I of the death certificate even in assumed cases, and (3) the primary cause of death even if the decedent had other chronic comorbidities. Most importantly, all comorbidities for COVID-19 would be listed in part II, rather than in part I, as they had been since 2003 for all other causes of death. If the CDC had simply employed their 2003 industry standard for data collection and reporting, which has been successfully used nationwide for 17 years, the total fatalities attributed to COVID-19 would be reduced by an estimated 90.2%.¹⁰²

These findings have been recently confirmed by a CNN medical personality who admitted that the US government had overestimated COVID-19 death and stressed the need for transparent coverage of the actual numbers.¹⁰³ In Belgium, deaths were counted independent of any testing. Of 52% of all deaths, only 4.5% turned out to be positive, yet all were counted in the national statistics. Authorities decided to be radically transparent and included not only deaths that were confirmed to be virus-related but even those suspected of being linked, whether the patients was tested or not.^14,104 South Korea had much lower CFR compared with Italy (1.0% vs 7.2%). South Korea adopted a strategy of widely testing for SARS-CoV-2, whereas Italy applied more stringent testing policies prioritizing testing for patients with more severe clinical symptoms suspected of having COVID-19 and hospitalization.¹⁴

Given the variability in testing policies and taking into account the (1) role of selection bias, (2) large number of asymptomatic cases, (3) confusions in case definitions, (4) patient characteristics, and (5) difficulty of knowing who is dying with versus dying from the disease, the denominator for calculating actual death rates cannot be reliably determined. The estimated world CFR (Figure 4) dropped from 2.4% at the beginning of the pandemic to 0.9% as of April 13, 2023, much lower than the originally predicted one by the WHO (3.4%), and varied according to countries and ages ranging from 0.000% in children to 0.02% in people <50 years old, which increases with the number of associated comorbidities and age reaching 13% to 20% in people age ≥80 years.^7,16,32 In contrast, the IFR is extremely difficult to calculate.101 According to a recent CDC study, the actual COVID-19 case count could be 6 to 24 times higher than official estimates, implying lower IFR with higher estimated actual counts.⁷⁶

These uncertainties about the case definition of death from or with COVID-19, the variability in CFR calculation, the difficulty in determining IFR, the highly suspected considerable number of non-COVID-19 deaths (death-related collateral damage) as a consequence of the lockdown and its negative effects at the social, medical and economical levels, and the lack of robust statistics on all-cause mortality and excess fatalities raised serious questions on the validity of such restrictive measures in saving lives and should warn against the application of such damaging strategies in a potential future outbreak.

Face masks
During the SARS-CoV-2 outbreak, face masks were considered as the cornerstone of NPIs. They were deployed as a mandatory public health measure for the general population in most countries around the world. Their mandatory widespread usage was justified by being an important tool in suppressing the transmission of the virus. Before the COVID-19 pandemic, masks constituted a mandatory self-protective and third-party protective measure for medical personnel in the health-care setting.¹⁰⁵ Their efficacy is based on the assumption of their ability in reducing transmission of pathogens, mainly bacteria.¹⁰⁶ However, this effectiveness of masks against viral particles has been the subject of debate among scientists, way before the COVID-19 pandemic.^107,108 In 2020, many scientists and physicians started to believe that the use of masks could also provide protection against viral transmission despite low scientific evidence to support such measures.

Recently, Chu and colleagues performed a systematic review and meta-analysis to investigate the optimum distance for avoiding person-to-person virus transmission and to assess the use of face masks and eye protection to prevent transmission of SARS-CoV-2. Transmission of viruses was lower with physical distancing of 1 meter or more, compared with a distance of less than 1 meter, and protection was increased as distance was lengthened with moderate certainty. In contrast, wearing face masks and eye protection was associated with low level of evidence in reducing the risk of infection. They also found that N95 respirators might be associated a decrease in risk compared with other masks.¹⁰⁹

The study by Chu and colleagues had several major limitations: (1) a large body of the analyzed studies were from China, (2) the level of certainty for findings was low for masks and moderate for distancing, (3) few studies assessed the effects of interventions in non-healthcare settings and they primarily evaluated mask use in households or contacts of cases, (4) the analysis included only observational studies and no randomized control trial, (5) studies were not always fully adjusted and suffered from recall and measurement bias, (6) of the 29 studies analyzed, 7 studies were unpublished, observational, and not peer reviewed, thus should not be used to guide clinical practice, and (7) although the meta-analysis funded by WHO was used to guide global facemask policy for the general population, only 3 of the 29 studies were classified as relating to a non-healthcare (ie, community) setting. Of these 3 studies, 1 was misclassified as related to masks in a hospital environment, 1 showed no benefit of face masks, and 1 was a poorly designed retrospective study about SARS-1.The authors acknowledged the low level of evidence. Although direct evidence was limited, the authors concluded that the optimum use of any type of face mask, in a healthcare setting or in the community, could depend on contextual factors and that robust randomized trials are needed to better inform the evidence for these interventions. This meta-analysis, despite being sponsored by WHO and published in The Lancet, seemed flawed and therefore should not have been used to set anti-COVID-19 guidelines by policy makers.

Many recent studies have assessed the antiviral effectiveness of masks and the effects of masks on the population, transmission characteristics, source control, wearer protection, sociological considerations, and implementation considerations, with contra-dictory results and limited conclusive results.^110-113 A recent meta-analysis of 10 randomized controlled studies on pandemic influenza published by the CDC found that face mask use, either by an infected person or by uninfected person, does not have a substantial effect on influenza transmission.¹¹³ Similarly, another review by WHO that assessed the body of evidence on the effectiveness of each of the 18 NPIs in community settings revealed an overall quality of evidence that was low for most interventions. The WHO review included a number of high- quality randomized controlled trials that demonstrated that personal protective measures, such as hand hygiene and face masks, have, at best, a small effect on influenza transmission.¹¹⁴ In fact, most studies found little to no evidence for the effectiveness of face masks in the general population^115-117 and in schools.¹¹⁸

To date, the only large randomized controlled trial, which involved more than 6000 Danish participants, found no significant effect of high-quality medical face masks against SARS-CoV-2 infection in a community setting. The study found uncertainty about the protective effects of face masks. Of note, the investigators involved in the study reported that their research results were rejected by at least 3 of the world’s leading medical journals, delaying its publication by several months.¹¹⁷ A systematic review and meta?analysis on effectiveness of N95 respirators versus surgical masks against influenza concluded no difference between the 2 protective measures.¹¹⁹ The most recent Cochrane Review found that face masks did not reduce influenza-like illness in the general population or in healthcare workers. No clear differences were shown 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 was found to modestly reduce the burden of respiratory illnesses.¹²⁰

In the United States and many European countries, coronavirus infections strongly increased after the introduction of mask mandates, and no difference was observed between countries with or without mask mandates like Norway, Sweden, Finland, and Denmark (Figure 5).¹²¹ Sweden was widely regarded, or indeed criticized, as the least repressive Western country during the coronavirus pandemic, having imposed no lockdowns, no elementary school closures, and no mask mandates. Interestingly, in March 2021, the European average COVID-19 mortality per million population exceeded that of Sweden (Figure 6).⁷ Furthermore, a direct comparison between US states with and without mask mandates indicated that mask mandates made no difference.¹²² Despite having an N95/FFP2 mask mandate from January 2021 that was further extended into September, Austria reported the highest infection rate in the world by November of the same year.¹²²

The novel coronavirus is essentially a spherical nanoparticle measuring 60 to 140 nm. Evidence supported its airborne transmission by small aerosol.^32,123-129 However, because of the large pore size and poor fit of surgical masks, most surgical face masks cannot filter out aerosols. Surgical masks are designed to protect against droplets or particles with a diameter of >100 μm, whereas SARS-CoV-2 virus is 100 times smaller than the pore diameter.¹³⁰ A randomized controlled trial demonstrated that penetration of cloth masks by particles was almost 97% and penetration of medical masks was 44%. These aerosols penetrate or bypass the mask and fill a medium-sized room within minutes.¹³¹ Interestingly, the CDC found that 85% of people infected wit SARS-CoV-2 reported wearing a mask “always” (70.6%) or “often” (14.4%). Compared with the control group of uninfected people, always wearing a mask did not reduce the risk of infection.¹³²

In contrast, some recent studies^129,131-133 and systematic review and metanalysis^109,134 argued that face masks are indeed effective against SARS-CoV-2 and could at least prevent infection of other people. Many of these studies ignored the effects of other measures, the natural development of infection rates, or changes in test activity or they compared places with different epidemiological conditions.¹²¹ The study from Zhang and colleagues¹²⁹ was so flawed that >40 scientists recommended that the study be retracted.¹³⁵ A reanalysis by several statisticians of the statistical sampling biases of a Bangladesh randomized controlled trial,¹³³ published in the prestigious journal Science, found that the study was poorly designed with no benefit.¹³⁶ In a meta-analysis on public measures to reduce COVID-19 incidence, SARS-CoV-2 transmission, and COVID-19 mortality published in BMJ,¹³⁴ the study claimed a reduction in infection by 53% by mask wearing; an editorial in the same journal acknowledged the “lack of good research” and the implausibility of the result.¹³⁷

Wearing masks for long period of time, as was the case during the pandemic, may predispose wearers to multiple adverse effects. Several studies reported on negative psychosocial,¹³⁸ professional,¹³⁹ physio-metabolic,¹¹⁰ and medical effects of face masks, including sudden death in children wearing masks while undergoing physical activities in China.¹³⁹ Moreover, a Japanese study found a significant number of infectious pathogens such as fungi and bacteria on face masks, including some pathogenic microbes.¹⁴¹ Face masks and N95/FFP2 masks were shown to increase CO₂ levels in inhaled air to levels above the acceptable exposure threshold.¹⁴² Scientists warned about risk of chlorine compound inhalation in polyester masks,¹⁴³ and the European Safety Federation recalled a large number of mask models because of noncompliance to safety issues that could lead to “serious risks.”¹⁴⁴

A recent systematic review of 2168 studies investigating the adverse effects of wearing face masks during the COVID-19 pandemic found that the practice led to several negative health consequences.¹¹⁰ N95 masks had a greater negative impact compared with medical and surgical masks. These effects included headache (most common symptom) with a prevalence of 62% for general mask use and up to 70% with the use of N95 masks. Shortness of breath was observed in 33% and 37% of cases for general mask use and for N95 mask use, respectively. Although 17% of surgical mask wearers experienced itching, that figure rose to 51% among N95 users. Prevalence of acne among mask users was 38% and that of skin irritation was 36%. Dizziness was observed in 5% of subject. Wearing face masks was associated with a decrease in SpO₂ and simultaneous increase in blood CO₂, heart rate, and systolic blood pressure. Wearing masks interfered with O₂ uptake and CO₂ release and compromised respiratory compensation. O₂ and CO₂ homeostasis influence various downstream metabolic processes. Independent of wearing duration, the results validate mask-induced exhaustion syndrome and consequent downstream physio-metabolic dysfunctions.

Mask-induced burnout syndrome may have long-term clinical consequences, especially for vulnerable groups. In fact, some mask-related symptoms could have been misinterpreted as symptoms of long-term COVID-19 disease. The authors concluded “face mask side-effects must be assessed (risk-benefit) against the available evidence of their effectiveness against viral transmissions. In the absence of strong empirical evidence of effectiveness, mask wearing should not be compulsory, and even less imposed by law.”¹¹⁰ Most importantly, mask-induced exhaustion syndrome is likely to be associated with a severe state of stress known to decrease an already impaired immunity related to COVID-19 and its different types of immunosuppressive therapies.

Given the findings in relation to their ques-tionable efficacy and potential safety risks and the little to no evidence supporting their utility in the general population, face masks should not have been mandated as protective tools against the transmission of the coronavirus or any other virus.

In conclusion, since the beginning of the pandemic, technocrates have proposed to the world a certain amount of so-called doubtful protective measures. This was compounded by the public manifestations of ill-informed limelight-hungry scientists and doctors and was heightened by the media’s tendency to choose the same experts who would parrot repeatedly the same messages. Most importantly, many mainstream media outlets repeatedly exaggerated the negative over the positive, obscured nuance, and failed for too long to uncover the truth about the harm caused by these pandemic policies.¹⁴⁵ Assisted and abetted by the media and by many academics, and in the name of protecting lives at any cost, politicians put forward strategies to apply these measures through mandates that infringed on the individual freedom of choice. These scientifically unfounded severe measures were imposed and enforced in most countries through tactics of psychological warfare to force compliance with COVID-19 rules, as was revealed in the British Lockdown Files with “Project Fear.”^84,85 These drastic policies were pushed ahead by the UK government, as by many governments around the world, to reduce overestimated COVID-19-related fatalities despite warnings that the cure would be worse than the disease. These policies were based on little science and centered on misleading mathematical disease modeling and mortality calculations that were and are still challenging and problematic to determine. These generalized interventions were imposed on a global scale and resulted in a considerable number of fatalities and global economic breakdown. The consequent collateral damages of these pseudo-protective measures and their negative effects at the social, medical, and economical levels raise questions on the validity of such restrictive measures in saving lives, leading to loss of trust in major institutions like the FDA, CDC, National Institutes of Health, the WHO, and others that depend on public trust to perform their missions.

According to our own perspective on the irrelevance of NPIs and their application during the COVID-19 pandemic, we raise doubts about the validity of the PCR for COVID-19 case definition and the effectiveness and safety of these protective interventions. We therefore caution and warn against the application of such damaging strategies in a potential future outbreak. As Nelson Mandela once said, “I never lose, either I win or I learn.”

References:

1. Gralinski LE, Menachery VD. Return of the coronavirus: 2019-nCoV. Viruses. 2020;12(2):135. doi:10.3390/v12020135
   CrossRef - PubMed
2. World Health Organization. WHO statement regarding cluster of pneumonia cases in Wuhan, China. January 19, 2020. https://www.who.int/china/news/detail/09-01-2020-who-statement-regarding-cluster-of-pneumonia-cases-in-wuhan-china
   CrossRef - PubMed
3. World Health Organization. January 17, 2020. Laboratory testing of 2019 novel coronavirus (????????????2019-nCoV)???????????? in suspected human cases: interim guidance. https://www.who.int/publications/i/item/laboratory-testing-of-2019-novel-coronavirus-(-2019-ncov)-in-suspected-human-cases-interim-guidance-17-january-2020
   CrossRef - PubMed
4. Cohen J. Mining coronavirus genomes for clues to the outbreak’s origins. Science. January 31, 2020. https://www.science.org/content/article/mining-coronavirus-genomes-clues-outbreak-s-origins
   CrossRef - PubMed
5. World Health Organization. Novel coronavirus (2019-nCoV). Situation report-1. January 21, 2020. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200121-sitrep-1-2019-ncov.pdf
   CrossRef - PubMed
6. Guo YR, Cao QD, Hong ZS, et al. The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak - an update on the status. Mil Med Res. 2020;7(1):11. doi:10.1186/s40779-020-00240-0
   CrossRef - PubMed
7. Coronavirus pandemic (COVID-19). WorldInData. https://ourworldindata.org/covid-cases.
   CrossRef - PubMed
8. Wu F, Zhao S, Yu B, et al. A new coronavirus associated with human respiratory disease in China. Nature. 2020;579(7798):265-269. doi:10.1038/s41586-020-2008-3
   CrossRef - PubMed
9. Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497-506. doi:10.1016/S0140-6736(20)30183-5
   CrossRef - PubMed
10. Zhu N, Zhang D, Wang W, et al. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med. 2020;382(8):727-733. doi:10.1056/NEJMoa2001017
    CrossRef - PubMed
11. Jones W. COVID-19 virus has a genetic feature never found in nature that lab scientists use to track engineered viruses. The Daily Sceptic. June 8, 2021. https://dailysceptic.org/2021/06/08/covid-19-virus-has-a-genetic-feature-never-found-in-nature-that-lab-scientists-use-to-track-engineered-viruses/
    CrossRef - PubMed
12. Feis A. ‘Damning’ science shows COVID-19 likely engineered in lab: experts. New York Post. June 7, 2021. https://nypost.com/2021/06/06/damning-science-shows-covid-19-likely-engineered-in-lab/
    CrossRef - PubMed
13. Quay S, Muller R. The science suggests a Wuhan lab leak. Opinion. Wall Street Journal. June 6, 2021. https://www.wsj.com/articles/the-science-suggests-a-wuhan-lab-leak-11622995184
    CrossRef - PubMed
14. Caduff C. What went wrong: corona and the world after the full stop. Med Anthropol Q. 2020;34(4):467-487. doi:10.1111/maq.12599
    CrossRef - PubMed
15. Bagnato A. Staying at home. E-flux Architecture. May 2020. https://www.e-flux.com/architecture/at-the-border/329404/staying-at-home/
    CrossRef - PubMed
16. Onder G, Rezza G, Brusaferro S. Case-fatality rate and characteristics of patients dying in relation to COVID-19 in Italy. JAMA. 2020;323(18):1775-1776. doi:10.1001/jama.2020.4683
    CrossRef - PubMed
17. Goumenou M, Sarigiannis D, Tsatsakis A, et al. COVID?19 in Northern Italy: an integrative overview of factors possibly influencing the sharp increase of the outbreak (Review). Mol Med Rep. 2020;22(1):20-32. doi:10.3892/mmr.2020.11079
    CrossRef - PubMed
18. Ferguson NM, Laydon D, Nedjati-Gilani G, et al. Report 9 - Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand. Imperial College London. https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/report-9-impact-of-npis-on-covid-19/
    CrossRef - PubMed
19. Poland GA, Ovsyannikova IG, Kennedy RB. SARS-CoV-2 immunity: review and applications to phase 3 vaccine candidates. Lancet. 2020;396(10262):1595-1606. doi:10.1016/S0140-6736(20)32137-1
    CrossRef - PubMed
20. Doshi P. Will COVID-19 vaccines save lives? Current trials aren’t designed to tell us. BMJ. 2020;371:m4037. doi:10.1136/bmj.m4037
    CrossRef - PubMed
21. Barbari A. COVID-19 vaccine concerns: fact or fiction? Exp Clin Transplant. 2021;19(7):627-634. doi:10.6002/ect.2021.0056
    CrossRef - PubMed
22. Polack FP, Thomas SJ, Kitchin N, et al. Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine. N Engl J Med. 2020;383(27):2603-2615. doi:10.1056/NEJMoa2034577
    CrossRef - PubMed
23. Baden LR, El Sahly HM, Essink B, et al. Efficacy and safety of the mRNA-1273 SARS-CoV-2 vaccine. N Engl J Med. 2021;384(5):403-416. doi:10.1056/NEJMoa2035389
    CrossRef - PubMed
24. Brosh-Nissimov T, Orenbuch-Harroch E, Chowers M, et al. BNT162b2 vaccine breakthrough: clinical characteristics of 152 fully vaccinated hospitalized COVID-19 patients in Israel. Clin Microbiol Infect. 2021;27(11):1652-1657. doi:10.1016/j.cmi.2021.06.036
    CrossRef - PubMed
25. Chemaitelly H, Tang P, Hasan MR, et al. Waning of BNT162b2 vaccine protection against SARS-CoV-2 infection in Qatar. N Engl J Med. 2021;385(24):e83. doi:10.1056/NEJMoa2114114
    CrossRef - PubMed
26. Levin EG, Lustig Y, Cohen C, et al. Waning immune humoral response to BNT162b2 Covid-19 vaccine over 6 months. N Engl J Med. 2021;385(24):e84. doi:10.1056/NEJMoa2114583
    CrossRef - PubMed
27. Goldberg Y, Mandel M, Bar-On YM, et al. Waning immunity after the BNT162b2 vaccine in Israel. N Engl J Med. 2021;385(24):e85. doi:10.1056/NEJMoa2114228
    CrossRef - PubMed
28. Tartof SY, Slezak JM, Fischer H, et al. Effectiveness of mRNA BNT162b2 COVID-19 vaccine up to 6 months in a large integrated health system in the USA: a retrospective cohort study. Lancet. 2021;398(10309):1407-1416. doi:10.1016/S0140-6736(21)02183-8
    CrossRef - PubMed
29. Mazraani M, Barbari A. Anti-coronavirus disease 2019 vaccines: need for informed consent. Exp Clin Transplant. 2021;19(8):753-762. doi:10.6002/ect.2021.0235
    CrossRef - PubMed
30. Barbari A. Mandatory sanitary pass: is it justified? Exp Clin Transplant. 2022;20(4):342-354. doi:10.6002/ect.2021.0358
    CrossRef - PubMed
31. Walsh S, Chowdhury A, Braithwaite V, et al. Do school closures and school reopenings affect community transmission of COVID-19? A systematic review of observational studies. BMJ Open. 2021;11(8):e053371. doi:10.1136/bmjopen-2021-053371
    CrossRef - PubMed
32. Barbari A. A different perspective on the COVID-19 pandemic: origin of the outbreak. Exp Clin Transplant. 2023. Accepted manuscript.
    CrossRef - PubMed
33. Valadi H, Ekstrom K, Bossios A, Sjostrand M, Lee JJ, Lotvall JO. Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat Cell Biol. 2007;9(6):654-659. doi:10.1038/ncb1596
    CrossRef - PubMed
34. Lu Y, Zhou Y, Ju R, Chen J. Human-animal chimeras for autologous organ transplantation: technological advances and future perspectives. Ann Transl Med. 2019;7(20):576. doi:10.21037/atm.2019.10.13
    CrossRef - PubMed
35. Ulrich R, Gerlich WH, Kruger DH. Chimaera and its modern virus-like descendants. Intervirology. 1996;39(1-2):126-132. doi:10.1159/000150484
    CrossRef - PubMed
36. Liu X, Xiong J, Sun Z, et al. Omicron: a chimera of two early SARS-CoV-2 lineages. Signal Transduct Target Ther. 2022;7(1):90. doi:10.1038/s41392-022-00949-5
    CrossRef - PubMed
37. Makarenkov V, Mazoure B, Rabusseau G, Legendre P. Horizontal gene transfer and recombination analysis of SARS-CoV-2 genes helps discover its close relatives and shed light on its origin. BMC Ecol Evol. 2021;21(1):5. doi:10.1186/s12862-020-01732-2
    CrossRef - PubMed
38. Kaufmann JK, Nettelbeck DM. Virus chimeras for gene therapy, vaccination, and oncolysis: adenoviruses and beyond. Trends Mol Med. 2012;18(7):365-376. doi:10.1016/j.molmed.2012.04.008
    CrossRef - PubMed
39. Bustin SA, Benes V, Garson JA, et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 2009;55(4):611-622. doi:10.1373/clinchem.2008.112797
    CrossRef - PubMed
40. Jacobson LS, McIntyre L, Mykusz J. Assessment of real-time PCR cycle threshold values in Microsporum canis culture-positive and culture-negative cats in an animal shelter: a field study. J Feline Med Surg. 2018;20(2):108-113. doi:10.1177/1098612X17706270
    CrossRef - PubMed
41. Kapoor M, Kalita D, Panda PK. Cycle threshold values versus reverse transcription-polymerase chain reaction positivity in COVID-19 de-isolation. Indian J Med Microbiol. 2021;39(1):133-135. doi:10.1016/j.ijmmb.2020.12.009
    CrossRef - PubMed
42. Crowe D. Flaws in coronavirus pandemic theory. Version 8.5. June 6, 2020. https://theinfectiousmyth.com/book/CoronavirusPanic.pdf
    CrossRef - PubMed
43. US Food and Drug Administration. Authorized molecular-based laboratory developed tests for detection of nucleic acid from SARS-CoV-2. https://www.fda.gov/medical-devices/coronavirus-disease-2019-covid-19-emergency-use-authorizations-medical-devices/in-vitro-diagnostics-euas
    CrossRef - PubMed
44. Kinloch NN, Ritchie G, Dong W, et al. SARS-CoV-2 RNA quantification using droplet digital RT-PCR. J Mol Diagn. 2021;23(8):907-919. doi:10.1016/j.jmoldx.2021.04.014
    CrossRef - PubMed
45. World Health Organization. Transmission of SARS-CoV-2: implications for infection prevention precautions. July 9, 2020. https://www.who.int/news-room/commentaries/detail/transmission-of-sars-cov-2-implications-for-infection-prevention-precautions
    CrossRef - PubMed
46. Han MS, Byun JH, Cho Y, Rim JH. RT-PCR for SARS-CoV-2: quantitative versus qualitative. Lancet Infect Dis. 2021;21(2):165. doi:10.1016/S1473-3099(20)30424-2
    CrossRef - PubMed
47. Lescure FX, Bouadma L, Nguyen D, et al. Clinical and virological data of the first cases of COVID-19 in Europe: a case series. Lancet Infect Dis. 2020;20(6):697-706. doi:10.1016/S1473-3099(20)30200-0
    CrossRef - PubMed
48. Watson J, Whiting PF, Brush JE. Interpreting a covid-19 test result. BMJ. 2020;369:m1808. doi:10.1136/bmj.m1808
    CrossRef - PubMed
49. Jarrom D, Elston L, Washington J, et al. Effectiveness of tests to detect the presence of SARS-CoV-2 virus, and antibodies to SARS-CoV-2, to inform COVID-19 diagnosis: a rapid systematic review. BMJ Evid Based Med. 2022;27(1):33-45. doi:10.1136/bmjebm-2020-111511
    CrossRef - PubMed
50. Mallapaty S. What the cruise-ship outbreaks reveal about COVID-19. Nature.2020;580(7801):18.March 26, 2020. https://www.nature.com/articles/d41586-020-00885-w
    CrossRef - PubMed
51. Mizumoto K, Kagaya K, Zarebski A, Chowell G. Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020. Euro Surveill. 2020;25(10). doi:10.2807/1560-7917.ES.2020.25.10.2000180
    CrossRef - PubMed
52. Mandavilli A. Your coronavirus test is positive. maybe it shouldn’t be. New York Times. August 29, 2020. https://www.nytimes.com/2020/08/29/health/coronavirus-testing.html
    CrossRef - PubMed
53. LibreTexts Biology. Koch’s postulates. Boundless. https://bio.libretexts.org/Bookshelves/Microbiology/Microbiology_(Boundless)/10%3A_Epidemiology/10.01%3A_Principles_of_Epidemiology/10.1D%3A__Kochs_Postulates
    CrossRef - PubMed
54. Nemeth A, Orgovan N, Sodar BW, et al. Antibiotic-induced release of small extracellular vesicles (exosomes) with surface-associated DNA. Sci Rep. 2017;7(1):8202. doi:10.1038/s41598-017-08392-1
    CrossRef - PubMed
55. Tahamtan A, Ardebili A. Real-time RT-PCR in COVID-19 detection: issues affecting the results. Expert Rev Mol Diagn. 2020;20(5):453-454. doi:10.1080/14737159.2020.1757437
    CrossRef - PubMed
56. Hant T. Editorial: The “false positive paradox” and risks of testing asymptomatics. BMJ 2021;373:n1411. doi:10.1136/bmj.n1411
    CrossRef - PubMed
57. Sethuraman N, Jeremiah SS, Ryo A. Interpreting diagnostic tests for SARS-CoV-2. JAMA. 2020;323(22):2249-2251. doi:10.1001/jama.2020.8259
    CrossRef - PubMed
58. Wolfel R, Corman VM, Guggemos W, et al. Virological assessment of hospitalized patients with COVID-2019. Nature. 2020;581(7809):465-469. doi:10.1038/s41586-020-2196-x
    CrossRef - PubMed
59. Wang W, Xu Y, Gao R, et al. Detection of SARS-CoV-2 in different types of clinical specimens. JAMA. 2020;323(18):1843-1844. doi:10.1001/jama.2020.3786
    CrossRef - PubMed
60. Zou L, Ruan F, Huang M, et al. SARS-CoV-2 viral load in upper respiratory specimens of infected patients. N Engl J Med. 2020;382(12):1177-1179. doi:10.1056/NEJMc2001737
    CrossRef - PubMed
61. Wyllie AL, Fournier J, Casanovas-Massana A, et al. Saliva or nasopharyngeal swab specimens for detection of SARS-CoV-2. N Engl J Med. 2020;383(13):1283-1286. doi:10.1056/NEJMc2016359
    CrossRef - PubMed
62. Vogels CBF, Brito AF, Wyllie AL, et al. Analytical sensitivity and efficiency comparisons of SARS-CoV-2 RT-qPCR primer-probe sets. Nat Microbiol. 2020;5(10):1299-1305. doi:10.1038/s41564-020-0761-6
    CrossRef - PubMed
63. Chan JF, Yip CC, To KK, et al. Improved molecular diagnosis of COVID-19 by the novel, highly sensitive and specific COVID-19-RdRp/Hel real-time reverse transcription-PCR assay validated in vitro and with clinical specimens. J Clin Microbiol. 2020;58(5). doi:10.1128/JCM.00310-20
    CrossRef - PubMed
64. Arevalo-Rodriguez I, Buitrago-Garcia D, Simancas-Racines D, et al. False-negative results of initial RT-PCR assays for COVID-19: a systematic review. PLoS One. 2020;15(12):e0242958. doi:10.1371/journal.pone.0242958
    CrossRef - PubMed
65. Lee SH. Testing for SARS-CoV-2 in cellular components by routine nested RT-PCR followed by DNA sequencing. Int J Geriatr Rehabil. 2020;2:69-96.
    CrossRef - PubMed
66. Temple-Raston D. CDC Report: officials knew coronavirus test was flawed but released it anyway. NPR. November 6, 2020. https://www.npr.org/2020/11/06/929078678/cdc-report-officials-knew-coronavirus-test-was-flawed-but-released-it-anyway
    CrossRef - PubMed
67. Braunstein GD, Schwartz L, Hymel P, Fielding J. False positive results with SARS-CoV-2 RT-PCR tests and how to evaluate a RT-PCR-positive test for the possibility of a false positive result. J Occup Environ Med. 2021;63(3):e159-e162. doi:10.1097/JOM.0000000000002138
    CrossRef - PubMed
68. Sadoff J, Gray G, Vandebosch A, et al. Safety and efficacy of single-dose Ad26.COV2.S vaccine against Covid-19. N Engl J Med. 2021;384(23):2187-2201. doi:10.1056/NEJMoa2101544
    CrossRef - PubMed
69. Centers for Disease Control and Prevention. Supplement F: laboratory guidance - CDC notice regarding 2003 SARS. 2004. https://www.cdc.gov/sars/guidance/f-lab/downloads/f-lab-full.pdf
    CrossRef - PubMed
70. Fang M. CDC quietly changes testing guidelines to exclude people with no symptoms 2021. HuffPost. August 26, 2020. https://www.huffpost.com/entry/cdc-testing-guidelines-coronavirus-no-symptoms_n_5f4667fac5b64f17e136efd2
    CrossRef - PubMed
71. Joynt GM, Wu WK. Understanding COVID-19: what does viral RNA load really mean? Lancet Infect Dis. 2020;20(6):635-636. doi:10.1016/S1473-3099(20)30237-1
    CrossRef - PubMed
72. Scribd. SARS-CoV-2 coronavirus multiplex RT-QPCR Kit (Cd019Rt). Product information. https://www.scribd.com/document/544786941/PCR#
    CrossRef - PubMed
73. Crowe D. Flaws in coronavirus pandemic theory. Version 7.2. 2020. https://www.academia.edu/42632288/Flaws_in_Coronavirus_Pandemic_Theory_version_7_2
    CrossRef - PubMed
74. Stringhini S, Wisniak A, Piumatti G, et al. Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Geneva, Switzerland (SEROCoV-POP): a population-based study. Lancet. 2020;396(10247):313-319. doi:10.1016/S0140-6736(20)31304-0
    CrossRef - PubMed
75. Pollan M, Perez-Gomez B, Pastor-Barriuso R, et al. Prevalence of SARS-CoV-2 in Spain (ENE-COVID): a nationwide, population-based seroepidemiological study. Lancet. 2020;396(10250):535-544. doi:10.1016/S0140-6736(20)31483-5
    CrossRef - PubMed
76. Havers FP, Reed C, Lim T, et al. Seroprevalence of antibodies to SARS-CoV-2 in 10 sites in the United States, March 23-May 12, 2020. JAMA Intern Med. 2020;10.1001/jamainternmed.2020.4130. doi:10.1001/jamainternmed.2020.4130
    CrossRef - PubMed
77. Nuki P. The terrifying data behind the government's sudden coronavirus lockdown. The Telegraph. March 17, 2020. https://www.telegraph.co.uk/global-health/science-and-disease/terrifying-data-behind-government-coronavirus-lockdown/
    CrossRef - PubMed
78. Sample I, Proctor K, Mason R. UK scientists being drawn into ‘very unpleasant’ political situation. The Guardian. May 6, 2020. https://www.theguardian.com/world/2020/may/06/uk-scientists-being-drawn-into-very-unpleasant-political-situation.
    CrossRef - PubMed
79. Mikhailova A, Hope C, Gillard M, Wells L. Exclusive: government scientist Neil Ferguson resigns after breaking lockdown rules to meet his married lover. The Telegraph. May 5, 2020. https://www.telegraph.co.uk/news/2020/05/05/exclusive-government-scientist-neil-ferguson-resigns-breaking/
    CrossRef - PubMed
80. Lockdown Files Team. The Lockdown Files. The Telegraph. https://www.telegraph.co.uk/news/lockdown-files/
    CrossRef - PubMed
81. Warner J. The catastrophic consequences of lockdown are revealing themselves. The Telegraph. November 2, 2022. https://www.telegraph.co.uk/business/2022/11/02/catastrophic-consequences-lockdown-revealing/
    CrossRef - PubMed
82. Nuki P. Two new waves of deaths are about to break over the NHS, new analysis warns. The Telegraph. April 25, 2020. https://www.telegraph.co.uk/global-health/science-and-disease/two-new-waves-deaths-break-nhs-new-analysis-warns/
    CrossRef - PubMed
83. Griffin S. COVID-19: “Staggering number” of extra deaths in community is not explained by COVID-19. BMJ. 2020;369:m1931. doi:10.1136/bmj.m1931
    CrossRef - PubMed
84. Penna D. Project Fear’s ‘psychological warfare’ must never be repeated, say lockdown rebel. The Telegraph. March 5, 2023. https://www.telegraph.co.uk/news/2023/03/05/project-fears-psychological-warfare-must-never-repeated-say/
    CrossRef - PubMed
85. Telegraph view. The chilling costs of Project Fear. The Telegraph. March 4, 2023. https://www.telegraph.co.uk/opinion/2023/03/04/chilling-costs-project-fear/?li_source=LI&li_medium=liftigniter-onward-journey
    CrossRef - PubMed
86. Lockdowns files team. Matt Hancock rejected advice to cut Covid isolation as it would ‘imply we’ve been wrong’. The Telegraph. March 5, 2023. https://www.telegraph.co.uk/news/2023/03/05/matt-hancock-rejected-covid-self-isolation-advice/
    CrossRef - PubMed
87. Lockdowns files team. How Matt Hancock sought to hog the Covid vaccine limelight. The Telegraph. March 5, 2023. https://www.telegraph.co.uk/news/2023/03/05/matt-hancock-triumph-covid-vaccine-lockdown-files-whatsapp/
    CrossRef - PubMed
88. KFF Global Health Policy. The U.S. government and global non-communicable disease efforts. January 29, 2019. https://www.kff.org/global-health-policy/fact-sheet/the-u-s-government-and-global-non-communicable-diseases/
    CrossRef - PubMed
89. Schraufnagel DE, Balmes JR, Cowl CT, et al. Air pollution and noncommunicable diseases: a review by the forum of international respiratory societies' environmental committee, part 2: air pollution and organ systems. Chest. 2019;155(2):417-426. doi:10.1016/j.chest.2018.10.041
    CrossRef - PubMed
90. Landrigan PJ, Fuller R, Acosta NJR, et al. The Lancet Commission on pollution and health. Lancet. 2018;391(10119):462-512. doi:10.1016/S0140-6736(17)32345-0
    CrossRef - PubMed
91. Goswami GG, Mahapatro M, Ali A, Rahman R. Do old age and comorbidity via non-communicable diseases matter for COVID-19 mortality? A path analysis. Front Public Health. 2021;9:736347. doi:10.3389/fpubh.2021.736347
    CrossRef - PubMed
92. Kompaniyets L, Pennington AF, Goodman AB, et al. Underlying medical conditions and severe illness among 540,667 adults hospitalized With COVID-19, March 2020-March 2021. Prev Chronic Dis. 2021;18:E66. doi:10.5888/pcd18.210123
    CrossRef - PubMed
93. NCD Countdown 2030 collaborators. NCD Countdown 2030: worldwide trends in non-communicable disease mortality and progress towards Sustainable Development Goal target 3.4. Lancet. 2018;392(10152):1072-1088. doi:10.1016/S0140-6736(18)31992-5
    CrossRef - PubMed
94. Ayres JS. A metabolic handbook for the COVID-19 pandemic. Nat Metab. 2020;2(7):572-585. doi:10.1038/s42255-020-0237-2
    CrossRef - PubMed
95. The GlobalEconomy.com. Hospital beds per 1,000 people - country ranking. shttps://www.theglobaleconomy.com/rankings/hospital_beds_per_1000_people/
    CrossRef - PubMed
96. COVID-19 Mental Disorders Collaborators. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. Lancet. 2021;398(10312):1700-1712. doi:10.1016/S0140-6736(21)02143-7
    CrossRef - PubMed
97. Bendavid E, Oh C, Bhattacharya J, Ioannidis JPA. Assessing mandatory stay-at-home and business closure effects on the spread of COVID-19. Eur J Clin Invest. 2021;51(4):e13484. doi:10.1111/eci.13484
    CrossRef - PubMed
98. Santi P, Cabut S. Les confinements auraient nettement réduit les capacités physiques et intellectuelles des enfants. https://www.lemonde.fr/sciences/article/2021/06/28/les-confinements-ont-nettement-reduit-les-capacites-physiques-et-intellectuelles-des-enfants_6086079_1650684.html
    CrossRef - PubMed
99. Merle A. La sédentarité des jeunes, une bombe sanitaire à retardement aggravée par la pandémie. https://www.francetvinfo.fr/sports/la-sedentarite-des-jeunes-une-bombe-sanitaire-a-retardement-aggravee-par-la-pandemie_4445485.html.
    CrossRef - PubMed
100. Worldometer. Coronavirus cases. May 14, 2020. https://www.worldometers.info/coronavirus/#countries
     CrossRef - PubMed
101. Ioannidis JPA. Infection fatality rate of COVID-19 inferred from seroprevalence data. Bull World Health Organ. 2021;99(1):19-33F. doi:10.2471/BLT.20.265892
     CrossRef - PubMed
102. Ealy H, McEvoy, M, Sava, M, et al. If COVID fatalities were 90.2% lower, how would you feel about schools reopening? The Natural Health Nut. March 22, 2023. https://naturalhealthnut.net/if-covid-fatalities-were-90-2-lower-how-would-you-feel-about-schools-reopening/
     CrossRef - PubMed
103. Phillips J. CNN medical personality admits US ‘overestimated’ Covid death toll. The Epoch Times. January 19, 2023. https://www.epochtimes.fr/une-personnalite-medicale-de-cnn-admet-que-les-etats-unis-ont-surestime-le-nombre-de-deces-dus-au-covid-19-2209991.html
     CrossRef - PubMed
104. Schultz T. Why Belgium’s death rate is so high: it counts lots of suspected COVID-19 Cases. NPR. April 22, 2020. https://www.npr.org/sections/coronavirus-live-updates/2020/04/22/841005901/why-belgiums-death-rate-is-so-high-it-counts-lots-of-suspected-covid-19-cases
     CrossRef - PubMed
105. Matuschek C, Moll F, Fangerau H, et al. The history and value of face masks. Eur J Med Res. 2020;25(1):23. doi:10.1186/s40001-020-00423-4
     CrossRef - PubMed
106. Vincent M, Edwards P. Disposable surgical face masks for preventing surgical wound infection in clean surgery. Cochrane Database Syst Rev. 2016;4(4):CD002929. doi:10.1002/14651858.CD002929.pub3
     CrossRef - PubMed
107. Lee SA, Grinshpun SA, Reponen T. Respiratory performance offered by N95 respirators and surgical masks: human subject evaluation with NaCl aerosol representing bacterial and viral particle size range. Ann Occup Hyg. 2008;52(3):177-185. doi:10.1093/annhyg/men005
     CrossRef - PubMed
108. Jefferson T, Del Mar CB, Dooley L, et al. Physical interventions to interrupt or reduce the spread of respiratory viruses. Cochrane Database Syst Rev. 2020;11(11):CD006207. doi:10.1002/14651858.CD006207.pub5
     CrossRef - PubMed
109. Chu DK, Akl EA, Duda S, et al. Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis. Lancet. 2020;395(10242):1973-1987. doi:10.1016/S0140-6736(20)31142-9
     CrossRef - PubMed
110. Kisielinski K, Hirsch O, Wagner S, et al. Physio-metabolic and clinical consequences of wearing face masks--systematic review with meta-analysis and comprehensive evaluation. Front Public Health. 2023;11:1125150. doi:10.3389/fpubh.2023.1125150
     CrossRef - PubMed
111. Coclite D, Napoletano A, Gianola S, et al. Face mask use in the community for reducing the spread of COVID-19: a systematic review. Front Med (Lausanne). 2020;7:594269. doi:10.3389/fmed.2020.594269
     CrossRef - PubMed
112. Howard J, Huang A, Li Z, et al. An evidence review of face masks against COVID-19. Proc Natl Acad Sci U S A. 2021;118(4). doi:10.1073/pnas.2014564118
     CrossRef - PubMed
113. Xiao J, Shiu EYC, Gao H, et al. Nonpharmaceutical measures for pandemic influenza in nonhealthcare settings-personal protective and environmental measures. Emerg Infect Dis. 2020;26(5):967-975. doi:10.3201/eid2605.190994
     CrossRef - PubMed
114. World Health Organization. Non-pharmaceutical public health measures for mitigating the risk and impact of epidemic and pandemic influenza. https://www.who.int/publications/i/item/non-pharmaceutical-public-health-measuresfor-mitigating-the-risk-and-impact-of-epidemic-and-pandemic-influenza
     CrossRef - PubMed
115. Alfelali M, Haworth EA, Barasheed O, et al. Facemask against viral respiratory infections among Hajj pilgrims: a challenging cluster-randomized trial. PLoS One. 2020;15(10):e0240287. doi:10.1371/journal.pone.0240287
     CrossRef - PubMed
116. European Centre for Disease Prevention and Control. Using face masks in the community: effectiveness in reducing transmission of COVID-19- first update.2021. February 15, 2021. https://www.ecdc.europa.eu/sites/default/files/documents/covid-19-face-masks-community-first-update.pdf
     CrossRef - PubMed
117. Bundgaard H, Bundgaard JS, Raaschou-Pedersen DET, et al. Effectiveness of adding a mask recommendation to other public health measures to prevent SARS-CoV-2 infection in Danish mask wearers: a randomized controlled trial. Ann Intern Med. 2021;174(3):335-343. doi:10.7326/M20-6817
     CrossRef - PubMed
118. Coma E, Catala M, Mendez-Boo L, et al. Unravelling the role of the mandatory use of face covering masks for the control of SARS-CoV-2 in schools: a quasi-experimental study nested in a population-based cohort in Catalonia (Spain). Arch Dis Child. 2023;108(2):131-136. doi:10.1136/archdischild-2022-324172
     CrossRef - PubMed
119. Long Y, Hu T, Liu L, et al. Effectiveness of N95 respirators versus surgical masks against influenza: a systematic review and meta-analysis. J Evid Based Med. 2020;13(2):93-101. doi:10.1111/jebm.12381
     CrossRef - PubMed
120. Jefferson T, Dooley L, Ferroni E, et al. Physical interventions to interrupt or reduce the spread of respiratory viruses. Cochrane Database Syst Rev. 2023;1(1):CD006207. doi:10.1002/14651858.CD006207.pub6
     CrossRef - PubMed
121. Weiss Y. https://twitter.com/yinonw/status/1321177359601393664
     CrossRef - PubMed
122. Swiss Policy Research. Are face masks effective? The evidence. https://swprs.org/face-masks-evidence/
     CrossRef - PubMed
123. Morawska L, Cao J. Airborne transmission of SARS-CoV-2: the world should face the reality. Environ Int. 2020;139:105730. doi:10.1016/j.envint.2020.105730
     CrossRef - PubMed
124. McAuley J, Rauhala E. Scientists urge WHO to address airborne spread of coronavirus. The Washington Post. July 5, 2020. https://www.washingtonpost.com/world/europe/coronavirus-airborne-spread-world-health-organization/2020/07/05/9de19c38-bed8-11ea-b4f6-cb39cd8940fb_story.html
     CrossRef - PubMed
125. Morawska L, Milton DK. It is time to address airborne transmission of coronavirus disease 2019 (COVID-19). Clin Infect Dis. 2020;71(9):2311-2313. doi:10.1093/cid/ciaa939
     CrossRef - PubMed
126. Lewis D. Mounting evidence suggests coronavirus is airborne - but health advice has not caught up. Nature. 2020;583(7817):510-513. doi:10.1038/d41586-020-02058-1
     CrossRef - PubMed
127. Lednicky JA, Lauzardo M, Fan ZH, et al. Viable SARS-CoV-2 in the air of a hospital room with COVID-19 patients. medRxiv. 2020;10.1101/2020.08.03.20167395. doi:10.1101/2020.08.03.20167395
     CrossRef - PubMed
128. Feuer W. CDC revises coronavirus guidance to acknowledge that it spreads through airborne transmission. https://www.cnbc.com/2020/10/05/cdc-revises-coronavirus-guidance-to-acknowledge-that-it-spreads-through-airborne-transmission.html
     CrossRef - PubMed
129. Zhang R, Li Y, Zhang AL, Wang Y, Molina MJ. Identifying airborne transmission as the dominant route for the spread of COVID-19. Proc Natl Acad Sci U S A. 2020;117(26):14857-14863. doi:10.1073/pnas.2009637117
     CrossRef - PubMed
130. Smereka J, Ruetzler K, Szarpak L, Filipiak KJ, Jaguszewski M. Role of mask/respirator protection against SARS-CoV-2. Anesth Analg. 2020;131(1):e33-e34. doi:10.1213/ANE.0000000000004873
     CrossRef - PubMed
131. MacIntyre CR, Seale H, Dung TC, et al. A cluster randomised trial of cloth masks compared with medical masks in healthcare workers. BMJ Open. 2015;5(4):e006577. doi:10.1136/bmjopen-2014-006577
     CrossRef - PubMed
132. Fisher KA TM, MD, Feldstein LR et al. Community and close contact exposures associated with COVID-19 among symptomatic adults ?18 years in 11 outpatient health care facilities — United States, July 2020. MMWR Morb Mortal Wkly Rep. 2020;69(36):1258-1264. doi:10.15585/mmwr.mm6936a5
     CrossRef - PubMed
133. Abaluck J, Kwong LH, Styczynski A, et al. Impact of community masking on COVID-19: a cluster-randomized trial in Bangladesh. Science. 2022;375(6577):eabi9069. doi:10.1126/science.abi9069
     CrossRef - PubMed
134. Talic S, Shah S, Wild H, et al. Effectiveness of public health measures in reducing the incidence of covid-19, SARS-CoV-2 transmission, and covid-19 mortality: systematic review and meta-analysis. BMJ. 2021;375:e068302. doi:10.1136/bmj-2021-068302
     CrossRef - PubMed
135. Bailey R. Prominent researchers say a widely cited study on wearing masks is badly flawed. Reason. June 22, 2020. https://reason.com/2020/06/22/prominent-researchers-say-a-widely-cited-study-on-wearing-masks-is-badly-flawed/
     CrossRef - PubMed
136. Chikina M, Pegden W, Recht B. Re-analysis on the statistical sampling biases of a mask promotion trial in Bangladesh: a statistical replication. Trials. 2022;23(1):786. doi:10.1186/s13063-022-06704-z
     CrossRef - PubMed
137. Glasziou PP, Michie S, Fretheim A. Public health measures for covid-19. BMJ. 2021;375:n2729. doi:10.1136/bmj.n2729
     CrossRef - PubMed
138. Stajduhar A, Ganel T, Avidan G, Rosenbaum RS, Freud E. Face masks disrupt holistic processing and face perception in school-age children. Cogn Res Princ Implic. 2022;7(1):9. doi:10.1186/s41235-022-00360-2
     CrossRef - PubMed
139. Fikenzer S, Uhe T, Lavall D, et al. Effects of surgical and FFP2/N95 face masks on cardiopulmonary exercise capacity. Clin Res Cardiol. 2020;109(12):1522-1530. doi:10.1007/s00392-020-01704-y
     CrossRef - PubMed
140. That’s GBA. Student deaths lead Chinese schools to change mask rules. May 8, 2020. https://www.thatsmags.com/china/post/31100/student-deaths-lead-schools-to-adjust-rules-on-masks-while-exercising
     CrossRef - PubMed
141. Park AM, Khadka S, Sato F, et al. Bacterial and fungal isolation from face masks under the COVID-19 pandemic. Sci Rep. 2022;12(1):11361. doi:10.1038/s41598-022-15409-x
     CrossRef - PubMed
142. Acuti Martellucci C, Flacco ME, Martellucci M, Violante FS, Manzoli L. Inhaled CO(2) concentration while wearing face masks: a pilot study using capnography. Environ Health Insights. 2022;16:11786302221123573. doi:10.1177/11786302221123573
     CrossRef - PubMed
143. Mowbray J. Exclusive: chemical cocktail found in face masks. Ecotextile News. April 1, 2021. https://www.ecotextile.com/2021040127603/dyes-chemicals-news/exclusive-chemical-cocktail-found-in-face-masks.html
     CrossRef - PubMed
144. European Safety Federation. Non-compliant respiratory protection. https://www.eu-esf.org/personal-protective-equipment/non-compliant/respiratory-protection?start=10.
     CrossRef - PubMed
145. Russell JH, Patterson D. The mask debacle. Tablet. February 16, 2022. https://www.tabletmag.com/sections/science/articles/the-mask-debacle.
     CrossRef - PubMed