Mask mandate and use efficacy for COVID-19 containment in US States

Guerra, Damian D.; Guerra, Daniel J.

doi:10.1101/2021.05.18.21257385

Cited by 6 publications

(6 citation statements)

References 42 publications

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“…The study evaluated the growth in COVID-19 cases post-mask mandate in non-mandate states compared to neighboring states with mask mandates. The study found no statistically significant difference in higher or lower transmission level growth rate of COVID-19 cases (Guerra & Guerra. 2021).…”

Section: Literature Reviewmentioning

confidence: 66%

“…Evidence in the literature suggested changing factors that was completely ignored in the literature resulted in the spread of misinformation about the effectiveness of facial coverings in response to the COVID-19 pandemic. Guerra and Guerra (2021) research that analyzed data from the Centers for Disease Control and Prevention, along with data from Institute for Health Metrics and Evaluation concluded mandates that required the use of masks were not associated with a reduction in the spread of COVID-19 at Print ISSN 2056-3620(Print) Online ISSN 2056-3639(Online) 13 @ECRTD-UK: https://www.eajournals.org/ Publication of the European Centre for Research Training and Development -UK statewide-level during COVID-19 surges. The study evaluated the growth in COVID-19 cases post-mask mandate in non-mandate states compared to neighboring states with mask mandates.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Walters and Djokic Quantitative Analysis Factor Distortion Theory: Addressing Challenges to Validity and Reliability in Scientific Research, Examples of Junk Science Research on Climates Change, COVID-19, the United States Real Estate Market and Global Financial Crisis of 2007 and 2008

Walters¹,

Djokić²

2022

IJQQRM

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The distortion and politicization of quantitative data analysis have resulted in the common acceptance of junk science into the body of literature to promote the political agenda of many. There is a cost associated with data analysis lacking validity and credibility. The politicization of quantitative analysis has resulted in policies being adapted around the world based on COVID-19 research, climate change research, and economic research which amount to junk science. Today, low-income and moderately-income families are facing a looming housing crisis as a result of the misinformation that caused the Global Financial Crisis of 2007 and 2008 and the real estate market crash preceding the crisis. The misinformation threatened the entire financial system of the world, resulting in a debt crisis for a number of countries. Many American families are faced with economic hardship resulting from the increased cost of energy as a result of policies based on climate change research data analysis with no evidence or scientific foundation supporting the theory. COVID-19 policies adopted by the United States government destroyed many small businesses across the country which supported families for generations. Government policies in response to COVID-19 interjected bureaucrats with no medical training between doctors and the treatment provided to their patients. Recent peer-reviewed publication Walters (2020D) introduced Walters and Djokic Quantitative Analysis Factor Distortion Theory to address the problem of unreliable data analysis lacking validity and credibility, resulting in junk science being accepted into the literature as scientific research. Walters and Djokic Quantitative Analysis Factor Distortion Theory is an expansion of Eddison Walters Modern Economic Analysis Theory that was developed to address the changing factor of technology advancement which has been commonly ignored in error by researchers when analyzing economic data over extended periods. Walters and Djokic Quantitative Analysis Factor Distortion Theory raised significant concerns regarding unreliable data analysis due to changing factors assumed to be constant. Changing factors assumed to be constant have resulted in distorted data analysis, lacking credibility and reliability, which has recently become commonly accepted as scientific research in error. The assumption of, “all else being equal”, has been commonly ignored in data analysis of research being accepted into the body of literature. Ignoring changing factors in data analysis to achieve a predetermined outcome is not scientific research and should not be accepted into the body of literature. Walters and Djokic Quantitative Analysis Factor Distortion Theory is a theory that is critical to restoring reliability and credibility to research accepted into the body of literature.

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Section: Literature Reviewmentioning

confidence: 66%

Section: Literature Reviewmentioning

confidence: 99%

Walters and Djokic Quantitative Analysis Factor Distortion Theory: Addressing Challenges to Validity and Reliability in Scientific Research, Examples of Junk Science Research on Climates Change, COVID-19, the United States Real Estate Market and Global Financial Crisis of 2007 and 2008

Walters¹,

Djokić²

2022

IJQQRM

View full text Add to dashboard Cite

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“…In other research, also undertaken in Spain, Marks et al examined various factors potentially affecting transmission of SARS-CoV-2, including mask use, reporting no observations relating to risk of transmission associated with reported mask usage, although the authors acknowledge the deficiencies in the analysis due to the lack of data on mask type [25]. A retrospective study on mask mandates and their use in the general population across the United States drew similar conclusions that these measures were not observed to be associated with reduced spread of SARS-CoV-2 [26]. Further, a report on a systematic review of 14 RCTs to evaluate the effectiveness of personal protective measures, including mask-wearing, found no evidence for a substantial effect of mask use on the transmission of laboratory-confirmed influenza [27].…”

Section: Efficacy Of Face Masks In Reducing Transmissionmentioning

confidence: 97%

Revisiting the rationale of mandatory masking

Beauchamp,

Mayhew

2023

J. Breath Res.

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In this perspective, we review the evidence for the efficacy of face masks to reduce the transmission of respiratory viruses, specifically severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and consider the value of mandating universal mask wearing against the widespread negative impacts that have been associated with such measures. Before the SARS-CoV-2 pandemic, it was considered that there was little to no benefit in healthy people wearing masks as prophylaxis against becoming infected or as unwitting vectors of viral transmission. This accepted policy was hastily reversed early on in the pandemic, when districts and countries throughout the world imposed stringent masking mandates. Now, more than three years since the start of the pandemic, the amassed studies that have investigated the use of masks to reduce transmission of SARS-CoV-2 (or other pathogens) have led to conclusions that are largely inconsistent and contradictory. There is no statistically significant or unambiguous scientific evidence to justify mandatory masking for general, healthy populations with the intention of lessening the viral spread. Even if mask wearing could potentially reduce the transmission of SARS-CoV-2 in individual cases, this needs to be balanced against the physical, psychological and social harms associated with forced mask wearing, not to mention the negative impact of innumerable disposed masks entering our fragile environment. Given the lack of unequivocal scientific proof that masks have any effect on reducing transmission, together with the evident harms to people and the environment through the use of masks, it is our opinion that the mandatory use of face masks in the general population is unjustifiable and must be abandoned in future pandemic countermeasures policies.

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“…Response often included restrictive population-wide measures, summarily called here non-pharmaceutical interventions (NPIs). However, here is little proof that most aggressive measures work much better than less disruptive, focused measures (e.g., Fögen, 2022;Guerra & Guerra, 2021; for reducing COVID-19 burden. Some adopted measures may even have severe negative consequences (for reviews see e.g., Panneer et al, 2022;Schippers, 2020).…”

Section: Introductionmentioning

confidence: 99%