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*Correspondence to Author: Mask mandate and use efficacy for COVID-19 containment in US States


Abstract and Figures

Background: COVID-19 pandemic mitigation requires evidence-based strategies. Because COVID-19 can spread via respired droplets, most US states mandated mask use in public settings. Randomized control trials have not clearly demonstrated mask efficacy against respiratory viruses, and observational studies conflict on whether mask use predicts lower infection rates. We hypothesized that statewide mask mandates and mask use were associated with lower COVID-19 case growth rates in the United States. Methods: We calculated total COVID-19 case growth and mask use for the continental United States with data from the Centers for Disease Control and Prevention and Institute for Health Metrics and Evaluation. We estimated post-mask mandate case growth in non-mandate states using median issuance dates of neighboring states with mandates. Results: Earlier mask mandates were not associated with lower total cases or lower maximum growth rates. Earlier mandates were weakly associated with lower minimum COVID-19 growth rates. Mask use predicted lower minimum but not lower maximum growth rates. Growth rates and total growth were comparable between US states in the first and last mask use quintiles during the Fall-Winter wave. These observations persisted for both natural logarithmic and fold growth models and when adjusting for differences in US state population density. Conclusions: We did not observe association between mask mandates or use and reduced COVID-19 spread in US states. COVID-19 mitigation requires further research and use of existing efficacious strategies, most notably vaccination. ABSTRACT IRJPH: 1 Damian D. Guerra et al., IRJPH, 2021; 5:55 IRJPH: 2
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*Correspondence to Author:
Damian D. Guerra
Department of Biology, University
of Louisville, Louisville, Kentucky,
United States of America
How to cite this article:
Damian D.Guerra, Daniel J.Guerra.
Mask mandate and use efficacy
for COVID-19 containment in US
States.International Research Jour-
nal of Public Health, 2021; 5:55.
eSciPub LLC, Houston, TX USA.
Damian D. Guerra et al., IRJPH, 2021; 5:55
International Research Journal of Public Health
Research Article IRJPH (2021) 5:55
Mask mandate and use efficacy for COVID-19 containment in US
Background: COVID-19 pandemic mitigation requires evidence-
based strategies. Because COVID-19 can spread via respired
droplets, most US states mandated mask use in public settings.
Randomized control trials have not clearly demonstrated mask
efficacy against respiratory viruses, and observational studies
conict on whether mask use predicts lower infection rates. We
hypothesized that statewide mask mandates and mask use were
associated with lower COVID-19 case growth rates in the United
Methods: We calculated total COVID-19 case growth and mask
use for the continental United States with data from the Centers
for Disease Control and Prevention and Institute for Health
Metrics and Evaluation. We estimated post-mask mandate case
growth in non-mandate states using median issuance dates of
neighboring states with mandates.
Results: Earlier mask mandates were not associated with lower
total cases or lower maximum growth rates. Earlier mandates
were weakly associated with lower minimum COVID-19
growth rates. Mask use predicted lower minimum but not lower
maximum growth rates. Growth rates and total growth were
comparable between US states in the first and last mask use
quintiles during the Fall-Winter wave. These observations
persisted for both natural logarithmic and fold growth models and
when adjusting for dierences in US state population density.
Conclusions: We did not observe association between mask
mandates or use and reduced COVID-19 spread in US states.
COVID-19 mitigation requires further research and use of
existing ecacious strategies, most notably vaccination.
Keywords: COVID-19, SARS-CoV-2, face covering, medical
mask, mask mandate, nonpharmaceutical intervention
Damian D. Guerra1,*, Daniel J. Guerra2
1Department of Biology, University of Louisville, Louisville, Kentucky, United States of America;
2Authentic Biochemistry, VerEvMed, Clarkston, Washington, United States of America
Damian D. Guerra et al., IRJPH, 2021; 5:55
The COVID-19 pandemic has increased
mortality and induced socioeconomic upheaval
worldwide [1]. Evidence-based containment
strategies are warranted, given that age,
obesity, cardiovascular disease, and diabetes
are common comorbidities associated with
severe COVID-19 symptoms [e.g., pneumonia,
blood clots, cytokine storm], hospitalization, and
death [2, 3]. Respired droplets and aerosols
containing SARS-CoV-2 are intuitive modes of
community transmission [4]. To reduce viral
spread, governments have issued mandates to
wear medical masks or cloth face coverings in
public settings. From April to December 2020, 40
States of the United States issued mask
mandates. Mask mandates have limited
precedent, making efficacy unclear. Our first
objective was to evaluate the efficacy of mask
mandates in attenuating COVID-19 growth in US
Prior studies have conflicted on whether masks
reduce COVID-19 spread. For USS Theodore
Roosevelt crew, mask use was lower among
COVID-19 cases compared with non-infected
[56% vs. 81%] [5]. There were no infections for
48% of universally masked patrons exposed to
COVID-19 positive hair stylists [6], but PCR tests
were not obtained for the other 52% of patrons
[6], and first wave COVID-19 hospitalizations
were no higher in public schools [high density
with minimal masking] than elsewhere in
Sweden [7]. A randomized controlled trial [RCT]
of Danish volunteers found no protective benefit
of medical masks against COVID-19 infection [8].
In RCTs before COVID-19, viral infections were
not lower in Vietnamese clinicians who wore
cloth or medical masks than in the control arm
[9], and N-95 respirators [but not medical masks]
protected Beijing clinicians from bacterial and
viral diseases compared to no masks [10]. Mask
compliance in RCTs is not always clear [11]. Mask
use was 10% and 33% for Beijing households
with and without intrahousehold COVID-19 case
growth, respectively [12]. This suggests greater
mask use may reduce COVID-19 spread. Our
second objective was to assess if mask use
predicts lower COVID-19 case growth.
We assessed if mask mandates and compliance
in US States predict statewide COVID-19 growth
during the second and third infection waves [1
June 2020-1 March 2021]. Controlling for
infection wave timing with logarithmic and linear
relative growth models, we found limited
association between COVID-19 case growth
and mask mandates or mask use before 1
October 2020, and no association during the
subsequent and largest third wave. These
findings do not support the hypothesis that
statewide mandates and enhanced mask use
slow COVID-19 spread. Pharmaceutical
interventions [including recently available
COVID-19 vaccines] provide alternative,
evidence-based strategies to minimize COVID-
19 related morbidity and mortality.
Materials and methods
Data Sources and Terms
We obtained total [confirmed and probable]
COVID-19 cases up to 6 March 2021 for the 49
continental US states, normalized per 100,000
residents, from the Centers for Disease Control
and Prevention [CDC] [13]. To reduce reporting
lag effects, we used 7-day simple moving means
[e.g., the 7-day simple moving mean of cases on
31 March is the mean of daily cases between 28
March and 3 April]. Hawaii was excluded
because COVID-19 growth patterns deviated
from those of continental US states. Confirmed
and probable cases are defined by the Council
of State and Territorial Epidemiologists.
Confirmed cases require PCR amplification of
SARS-CoV-2 RNA from patient specimens.
Probable cases require one of the following:
clinical and epidemiologic evidence, clinical or
epidemiologic evidence supported by SARS-
CoV-2 antigen detection in respiratory
specimens, or vital records listing COVID-19 as
contributing to death. Total PCR tests for each
state were obtained from Worldometers on 25
May 2021[14].
Mask mandates are statewide emergency
executive public health orders requiring nose
and mouth coverings in public settings in more
Damian D. Guerra et al., IRJPH, 2021; 5:55
than 50% of counties within a state [15, 16]. We
assigned US states to one of five quintiles based
on when mandates went into effect [effective
dates]: 18 April-16 May 2020 [Q1], 29 May-3 July
2020 [Q2], 8 July-27 July 2020 [Q3], 1 Aug-9
Dec 2020 [Q4], or no statewide mandate as of 6
March 2021 [Q5]. Effective dates were obtained
from US state executive and health departments
and press releases [available upon request].
We assessed mask use with the University of
Washington Institute for Health Metrics and
Evaluation [IHME] COVID-19 model site [17],
which estimates daily compliance from Premise,
the Facebook Global Symptom Survey
[University of Maryland], the Kaiser Family
Foundation, and the YouGov Behavior Tracker
Survey. Mask use is the percentage of people
who always wear masks in public settings. We
assigned US states to mask quintiles based on
the mean percent mask use from 1 Jun-1 Oct
2020 [Summer] or from 1 Oct 2020-1 Mar 2021
To assess geographic differences, we assigned
each US state to one of five regions: Northeast
[Connecticut, Delaware, Massachusetts,
Maryland, Maine, New Hampshire, New Jersey,
New York, Pennsylvania, Rhode Island, and
Vermont]; Midwest [Illinois, Indiana, Iowa,
Kentucky, Kansas, Michigan, Minnesota,
Missouri, Ohio, West Virginia, Wisconsin];
Mountains-Plains [Colorado, Idaho, Montana,
Nebraska, New Mexico, North Dakota,
Oklahoma, South Dakota, Utah, Wyoming];
South [Alabama, Arkansas, Florida, Georgia,
Louisiana, Mississippi, North Carolina, South
Carolina, Tennessee, Texas, Virginia]; and
Pacific [Alaska, Arizona, California, Nevada,
Oregon, Washington].
Growth Rate Calculation
COVID-19 growth has been modeled
logarithmically [15, 18, 19] and linearly [19, 20].
Therefore, we calculated COVID-19 case growth
for each US state by measuring percent natural
logarithmic [Ln Growth] and percent linear [Fold
Growth] relative growth rates:
Ln Growth:   
Fold Growth:   
Where Ct, Ct-1, and Ct-20 are total normalized
cases on a day, the prior day, and 20 days prior,
respectively. We determined adjusted
population density by calculating the weighted
mean of each state’s urban [U] and rural [R]
population density using the following formulas:
Urban Density [U]     
Mean Rural Density [R]
For the three most populous urban regions in
each US state, we obtained urban population
density [u; people/square mile in an urban area],
urban land area [a; size of urban area in square
miles], and population proportion [p; fraction of
combined urban population] via 2010 US
Census Bureau estimates [21]. For some states,
two rather than three urban regions were used.
The proportion of urban [F] and rural [1-F]
population of each state was similarly obtained
[21]. We thus calculated adjusted population
density of each state as:
Adjusted Population Density [APD]     
To assess association between population
density and growth rates, we multiplied Fold
Growth by the inverse of normalized APD:
Adj. Fold Growth = Fold Growth 
We defined minima and maxima [extrema] as
the lowest and highest growth rates between the
end of the Summer wave and the height of the
Fall-Winter wave. Ln Growth extrema comprised
20-day windows when Ln Growth rates were
lowest or highest:
Ln Growth extremum = 
 
Fold Growth extrema similarly comprise the
lowest and highest Fold Growth:
Fold Growth extremum= 
[t-20, t]
For each state, surges are differences between
maxima and minima [relative growth rate
increase], masks at extrema are the 20-day
mean mask use at minima and maxima, and Δ
Masks is the percent change in mask use
between maxima and minima.
Damian D. Guerra et al., IRJPH, 2021; 5:55
To assess association between mandates and
growth rates in the 48 contiguous states
[excluding Alaska and Hawaii], we determined
Ln, Fold, and Adj. Fold Growth between 1 March
2021 [C301] and the mandate effective date [CM]
for US states in mandate quintiles 1-3:
Post-Mandate Ln Growth =    
Post-Mandate Fold Growth =     
For states in quintiles 4-5, modeled effective
dates are medians of actual dates among
bordering states of mandate quintiles 1-3. For
example, the modeled effective date of
Tennessee [10 July] is the median of effective
dates of Arkansas [20 July], Alabama [16 July],
Kentucky [10 July], North Carolina [26 June],
and Virginia [29 May].
For each state, Summer 2020 [1 June-1 Oct] and
Fall-Winter 2020-21 [1 Oct-1 Mar] mask use is
mean mask use between these dates. Cases on
1 June or 1 Oct were the 20-day mean total
normalized cases on these two dates. We
likewise defined Summer and Fall-Winter case
growth using Ln and Fold Growth formulas:
  
 
Summer Fold Growth =     
 
Fall-Winter Ln Growth =  
Fall-Winter Fold Growth =    
Where c601, c1001, and c301 are total normalized
cases on 1 June 2020, 1 October 2020, and 1
March 2021, respectively.
We used Prism 9.2 [GraphPad; San Diego, CA]
to construct figures and perform null hypothesis
significance tests, for which the significance
threshold was p < α = 0.05 [Worksheet D in S1
Table]. Error bars denote standard deviations,
95% confidence intervals, or interquartile ranges
as indicated in figure legends. We performed
D’Agostino-Pearson tests to assess normality of
To evaluate mask mandate and use efficacy
among categories [mandate effective date or
mask use quintiles], we performed ordinary one-
way ANOVA with Tukey posttests. For non-
normal data, we performed Kruskal-Wallis with
Dunn posttests. For two sample comparisons
[e.g., Fig. 3G, J], we conducted two-tailed t tests
or Mann-Whitney tests for normal and non-
normal data, respectively.
This decision tree conforms with recommended
practices for datasets of N > 5 [22].
For interval variable associations, we performed
ordinary least squares [OLS]-simple linear
regression with null hypotheses of zero slope.
Infectious disease research has employed OLS
previously [23, 24], with linear and ln-linear models
reported in recent COVID-19 studies [25, 26].
For the Summer wave, Northeast states were
excluded because they deviated from other
states with respect to total cases and growth
covariation. We used weighted least squares
[WLS] for heteroscedastic data, as determined
by the GraphPad Prism Test for
Homoscedasticity. Regardless of statistical
significance, R2 values denote coefficients of
determination for lines of best fit with
unconstrained slopes.
COVID-19 growth rates vary with time
With the aim of reducing COVID-19 case growth,
40 US states enacted mask mandates in 2020.
We wondered if mask mandate timing affected
COVID-19 growth patterns. To identify patterns
of COVID-19 growth, we graphed natural
logarithmic [Ln] Growth of COVID-19 in US
states as a function of time [Worksheet A in S1
Table]. We observed six phases of COVID-19
growth up to 6 March 2021: first wave [before
May 2020], Spring minimum [May-June 2020],
Summer wave maximum [June-August 2020],
post-Summer minimum [August-October 2020],
Fall-Winter wave maximum [October-January
2020], and third minimum [March 2021] [Fig. 1A
and S1 Fig]. Hawaii growth patterns deviated
from those of continental US states and was thus
excluded from further analysis. Regardless of
mask mandate effective date quintile, Ln growth
patterns were comparable for all continental US
states, and there was no association between
normalized total cases and PCR tests [S1 Fig].
Damian D. Guerra et al., IRJPH, 2021;5:55
Fig 1. Earlier mask mandates are not consistently associated with lower COVID-19 growth rates in continental
US States. A-B. Natural logarithmic [A] and Fold [B] COVID-19 growth in continental US states. Red horizontal lines
denote growth rate minima [Min] and maxima [Max] between Summer and Fall-Winter waves. Surge: growth rate
increase between Min and Max. C. Ln minima were not associated with the time quintile of a state’s mandate effective
date [MQ]. D. Fold minima trended lower in MQ1 than MQs 3 and 5. E. Adjusted Fold minima were lower in MQ1 than
MQ4 and indistinguishable for all other pairwise comparisons. F-G. Ln [F] and Fold [G] maxima were not associated
with mandate effective date time quintiles. H. Adjusted Fold maxima were lower in MQ2 than MQ4 and indistinguishable
for all other pairwise comparisons. I. States with earlier mask mandates exhibited greater mask use between Oct. 2020
and March 2021. J. Cases per 100,000 by 1 March 2021 were not associated with mandate effective date time quintiles.
Different letters denote p<0.05 by Tukey tests after one-way ANOVA [C, F, G] or all pairwise comparison Dunn tests
after Kruskal-Wallis [D, E, H-J]. *: p<0.05 by Kruskal-Wallis. n.s.: not significant. Error bars: 95% confidence intervals
[A-B], standard deviations [C, F, G], and interquartile ranges [D, E, H-J].
Damian D. Guerra et al., IRJPH, 2021; 5:55
Fig 2. Earlier mask mandates are not associated with lower post-mandate COVID-19 growth rates in contiguous
US states. A. Effective and modeled effective [bold, italicized] dates in 2020 for mask mandates in contiguous US
states. State colors denote effective date time quintiles. Modeled dates of MQ4-5 states [late or no actual mandates]
are medians of effective dates among bordering states of MQ1-3 [earlier mandates]. Dashed lines denote MQ1-3 states
that border a given MQ4-5 state. B-D. Between actual or modeled mandate effective dates and 1 March 2021, Ln Growth
[B], Fold Growth [C], and population density-adjusted Fold Growth [D] were not associated with mandate effective date
time quintiles. n.s.: not significant by one-way ANOVA [B] or Kruskal-Wallis [C-D]. Error bars: standard deviations [B]
and interquartile ranges [C-D].
Earlier mask mandates are not consistently
associated with COVID-19 growth rates in US
A recent study reported time-enhanced negative
association between mask mandates and Ln
Growth of COVID-19 [15], but simple Fold-Growth
[an alternative COVID-19 metric [19, 20] may be
preferred for post-exponential, linear pandemic
spread. PCR testing for COVID-19 was limited
before Summer 2020 [27]. Thus, to determine if
US states with earlier mask mandates exhibited
less COVID-19 spread, we examined both Ln
Growth and Fold Growth at the post-Summer
wave minimum and the Fall-Winter wave
maximum [Fig 1A-B]periods of low and high
transmission, respectively. We assigned US
states to one of five quintiles [MQ1-5], with MQ1
including states with the earliest mandates, MQ4
Damian D. Guerra et al., IRJPH, 2021; 5:55
the latest mandates, and MQ5 states without
mandates. Ln minima [p=0.07; Fig. 1C] and Fold
minima [p=0.047; Fig. 1D] trended lower for
earlier mandates. Fold minima was 3-fold higher
in MQ4 than MQ1 after adjusting for population
density [p=0.04], but all other pairwise
comparisons were not significant [Fig. 1E]. This
suggested that mask mandate duration was a
weak predictor of lower minimum growth. Ln
maxima [p=0.23; Fig. 1F] and Fold maxima
[p=0.19; Fig. 1G] did not differ among quintiles.
Adjusting for population density, Fold maxima
were 2.8-fold higher in MQ4 than MQ2 [p=0.01],
but MQ1, 3, and 5 were indistinguishable [Fig.
1H], suggesting mask mandate duration was not
associated with lower maximum growth.
Likewise, surges [growth rate increases from
minima to maxima] were MQ-independent for Ln
[p=0.08] and Fold [p=0.13] models, and only
MQ2 and MQ4 exhibited significantly different
Fold surges with population density adjustment
[p=0.03; S2 Fig]. Most MQ4 states exhibited
lower initial and Summer 2020 infection waves
than Q1-2 [S1 Fig], suggesting high MQ4 growth
rates could be an artifact of lower total cases.
While there was strong positive association
between earlier mandates and Fall-Winter mask
use [p<0.001; Fig. 1I], total cases on 1 March
2021 were MQ-independent [p<0.07; Fig. 1J].
Direct MQ1 vs. MQ5 comparison by t test
uncovered a small [1.2-fold] and non-significant
[p=0.078] difference in total cases. Taken
together, these findings suggest that US state
mask mandates were not associated with slower
spread of COVID-19.
Early mask mandates do not predict lower post-
mandate COVID-19 growth in contiguous US
Most US states enacted mandates during
infection waves, which confounds assessment
of effectiveness. To assess association between
mandate effective date and post-mandate case
growth, we compared growth after actual MQ1-
3 mandates with growth after modeled MQ4-5
mandates up to 1 March 2021. For a MQ4-5
state, the modeled date was the effective date
median of contiguous, common-border MQ1-3
states [Fig. 2A]. Post-mandate growth in total
cases was MQ-independent by Ln [p=0.43], Fold
[p=41], and population density-adjusted Fold
[p=0.15] models [Fig. 2B]. Direct MQ1 vs. MQ5
comparison by Mann-Whitney test uncovered a
small [1.3-fold] and non-significant [p=0.86]
difference in adjusted fold-growth. Overall, we
did not obtain an association between mandates
and lower COVID-19 growth.
Mask use is not associated with most state
COVID-19 case growth
We speculated that statewide mask use, rather
than mask mandates per se, may predict lower
COVID-19 growth rates. The Institute of Health
Metrics and Evaluation [IHME] provides robust
estimates for mask use [defined as the
percentage of people who always wear masks in
public settings] [17]. By simple linear regression,
mask use was associated with lower Ln, Fold,
and adjusted Fold minima [p<0.0001; Fig 3A-B
and S3A Fig]. To better understand this trend,
we assigned US states to one of five mask use
quintiles [UQ1-5], with UQ1 including states with
the highest mask use and UQ5 states with the
lowest mask use. UQ5 exhibited a 3.4-fold
greater adjusted Fold minimum than UQ1
[p=0.002; Fig 3C], suggesting potential
association between mask use and COVID-19
spread at minima. By contrast, mask use was
not associated with Ln [p=0.071], Fold
[p=0.058], or adjusted Fold [p=0.076] maxima
[Fig 3D-E and S3D Fig]. Adjusted Fold maxima
were also UQ-independent [p=0.56; Fig 3F],
with direct UQ1 vs. UQ5 comparison by Mann-
Whitney test uncovering a modest [1.5-fold] and
non-significant [p=0.16] difference in maxima.
This suggests that mask use is not associated
with COVID-19 spread at maxima.
We wondered why mask use was associated
with lower minimum but not lower maximum
growth rates. Mask use was not associated with
total cases at Ln minima [p=0.54] or maxima
[p=0.086; S3C-D Fig], indicating potential
confounders in the mask-minimum growth
relationship. Excluding Northeast states, which
exhibited the largest first waves and July 2020
seroprevalence [13, 28], total cases predicted
Damian D. Guerra et al., IRJPH, 2021; 5:55
Fig 3. Mask use does not consistently predict COVID-19 case growth in continental US states. A-C. At minima,
mask use was associated with lower ln [A], fold [B], and population density-adjusted fold [C] growth rates. D-F. At
maxima, mask use was not associated with ln [D], fold [E], or population density-adjusted fold [F] growth rates. G. States
in June-Oct. 2020 mask use quintiles [UQ] 1 and 5 grew from 400 to 1350 normalized cases at indistinguishable rates
before minima. H-I. Ln cases [H] and cases [I] vs. time for UQ1 and UQ5. J. States in Oct. 2020-March 2021 mask use
UQ1 and UQ5 exhibited indistinguishable Fold Growth 80 days after maxima. K-L. Ln cases [K] and cases [L] vs. time
for UQ1 and UQ5. Simple linear regression used weighted [A-B] or ordinary [D-E] least squares. R2 values refer to
unconstrained lines of best fit. Different letters denote p<0.05 by all pairwise comparison Dunn tests after Kruskal-Wallis
[C, F]. n.s.: not significant. Error bars: Interquartile ranges [C, F, J, K] and 95% confidence intervals [G-H].
Damian D. Guerra et al., IRJPH, 2021; 5:55
Fig 4. Mask use does not predict lower COVID-19 growth during the Summer or Fall-Winter waves. A-B. Ln
Growth rate [A] and total COVID-19 cases [B] for continental US states from 20 April 2020 to 6 March 2021. Red vertical
lines denote Summer [Jun-Oct 2020] and Fall-Winter [Oct 2020-Mar 2021] waves. C. Mask use does not predict Summer
Ln Growth in non-Northeast states. D. In the Summer wave, population-adjusted Fold Growth was lower in Summer
mask use UQ1 than UQ4 and indistinguishable among UQ2-5. E. Mask use does not predict Fall-Winter Ln Growth in
continental US states. F. In the Fall-Winter wave, population-adjusted Fold Growth was indistinguishable among Fall-
Winter mask use quintiles. Simple linear regression used ordinary least squares [C, E]. R2 values refer to unconstrained
lines of best fit. Different letters denote p<0.05 by all pairwise comparison Dunn tests after Kruskal-Wallis [D, F]. n.s.:
not significant. Error bars: 95% confidence intervals [A-B] and interquartile ranges [D, F]. Solid circles []: All continental
US states. Hollow circles []: Excluded Northeast states.
Damian D. Guerra et al., IRJPH, 2021; 5:55
lower Ln minima [p=0.001; S3E Fig]. This
suggested that the link between mask use and
lower minima may be an artifact of the tendency
for faster case growth to occur at lower case
prevalence. In support of this, for 1 June 1 Oct.
2020 mask use quintiles, normalized cases grew
from 400 to 1350 at similar rates for UQ1 [which
includes eight Northeast states] and UQ5
[p=0.22; Fig. 3G]. UQ5 exhibited exponential
growth and reached these case totals ~50 days
after UQ1 [Fig 3H-I], further implying that higher
growth rates may reflect lower total cases in low
mask use states before minima. 0-80 days after
Ln maxima, when total case differences were
smaller among states, UQ1 and UQ5 exhibited
indistinguishable growth rates [p=0.78; Fig 3J; 1
Oct. 2020 1 March 2021 mask use quintiles].
Growth was post-exponential for both UQ1 and
UQ5 during this period [Fig 3K-L], and total
cases predicted lower Ln maxima in all
continental US states [p<0.0001; S3F Fig].
Together, these data suggest that mask use is
an unreliable predictor of COVID-19 growth in
US states.
Mask use does not predict Summer or Fall-
Winter COVID-19 cumulative growth in US
As expected, total cases were negatively
associated with Ln growth in non-Northeast
states for 1 June-1 Oct. 2020 and all continental
US states for 1 Oct. 2020 1 March 2021
[p<0.0001; S4A-B Fig]. We reasoned that even
if mask use could not predict growth rate, mask
use may be negatively associated with
cumulative case growth. 1 June-1 Oct. 2020
[Summer] 1 Oct. 2020 1 March 2021 [Fall-
Winter] represent two distinct COVID-19 growth
waves [Fig 4A-B]. Excluding Northeast states,
masks were not associated with lower Summer
growth using Ln [p=0.11; Fig 4C] or Fold
[p=0.18; S4C Fig] models. Mask use trended
with lower adjusted Fold Summer growth
[p=0.05; S4D Fig]. While adjusted Fold Summer
growth was 3-fold higher in UQ4 than UQ1
[p=0.009], all other pairwise comparisons were
not significant [Fig. 4D]. Likewise, mask use was
not associated with lower Fall-Winter growth
using Ln [p=0.94; Fig 4E], Fold [p=0.91; S4E
Fig], or adjusted Fold [p=0.71; S4F Fig] models,
and adjusted Fold Fall-Winter growth was not
significantly different among mask use quintiles
[p=0.38; Fig. 4F]. These data suggest that mask
use is not consistently associated with Summer
wave growth and not associated with Fall-Winter
wave growth in US states. Furthermore, low
Summer growth did not protect Northeast states
from subsequent Fall-Winter growth. In
summary, statewide SARS-CoV-2 transmission
waves appear independent of reported mask
use [17].
Our main finding is that mask mandates and use
likely did not affect COVID-19 case growth.
Mask mandates were associated with greater
mask use but ultimately did not influence total
normalized cases or post-mandate case growth.
Higher mask use [rather than mandates per se]
has been argued to decrease COVID-19 growth
rates [11]. While compliance varies by location
and time, IHME estimates are derived from
multiple sources and densely sampled. Even
when accounting for population density, higher
mask use was not associated with lower Ln or
fold maximum growth rates or lower Fall-Winter
case growth among continental US states. By
contrast, mask use-growth rate association was
highly significant at minima. This antinomy
warrants consideration. Mask use did not predict
normalized cases at growth minima or maxima,
whereas there were more cases in the highest
than the lowest mask use quintile before minima.
Northeast states exhibited the highest
seroprevalence by July 2020 [28] and comprised
80% of the highest mask use quintile, suggesting
that mask use may be a lagging indicator of case
growth. At maxima, when case prevalence was
similar among states, COVID-19 growth rates
were also similar for the highest and lowest
mask use quintiles. Thus, initial association
between masks and lower COVID-19 growth
rates that dissipated during the Fall-Winter
2020-21 wave is likely an artifact of fewer
normalized cases begetting faster growth in
states with coincidental low mask use.
Damian D. Guerra et al., IRJPH, 2021; 5:55
There is inferential but not demonstrable
evidence that masks reduce SARS-CoV-2
transmission. Animal models [29], small case
studies [6], and growth curves for mandate-only
states [16] suggest that mask efficacy increases
with mask use [11]. However, we did not observe
lower growth rates over a range of compliance
at maximum Fall-Winter growth [45-83%
between South Dakota and Massachusetts
during maxima] [17] when growth rates were high.
This complements a Danish RCT from 3 April to
2 June 2020, when growth rates were low, which
found no association between mask use and
lower COVID-19 rates either for all participants
in the masked arm [47% strong compliance] or
for strongly compliant participants only [8]. While
N-95 respirators offer some protection against
respiratory viruses [10], there is limited evidence
for cloth and medical masks. Higher self-
reported mask use protected against SARS-
CoV-1 in Beijing residents [30], but RCTs found
no differences in PCR confirmed influenza
among Hong Kong households assigned to
hand hygiene with or without masks [mask use
31% and 49%, respectively] [31]. Medical and
cloth masks did not reduce viral respiratory
infections among clinicians in Vietnam [9] or
China [10], and rhinovirus transmission increased
among universally masked Hong Kong students
and teachers in 2020 compared with prior years
[32]. These findings are consistent with a 2020
CDC meta-analysis [33] and a 2020 Cochrane
review update [34].
Our study has implications for respiratory virus
mitigation. Public health measures should
ethically promote behaviors that prevent
communicable diseases. The sudden onset of
COVID-19 compelled adoption of mask
mandates before efficacy could be evaluated.
Our findings do not support the hypothesis that
greater public mask use decreases COVID-19
spread. As masks have been required in many
settings, it is prudent to weigh potential benefits
with harms. Masks may promote social cohesion
during a pandemic [35], but risk compensation
can also occur [36]. By obscuring nonverbal
communication, masks interfere with social
learning in children [37]. Likewise, masks can
distort verbal speech and remove visual cues to
the detriment of individuals with hearing loss;
clear face-shields improve visual integration, but
there is a corresponding loss of sound quality [38,
39]. Prolonged mask use [>4 hours per day]
promotes facial alkalinization and inadvertently
encourages dehydration, which in turn can
enhance barrier breakdown and bacterial
infection risk [40]. British clinicians have reported
masks to increase headaches and sweating and
decrease cognitive precision [41]. Survey bias
notwithstanding, these sequelae are associated
with medical errors [42]. Future research is
necessary to assess risks of long-term daily
mask use [34]. As COVID-19 remains a public
health threat, it is also appropriate to emphasize
interventions with demonstrated efficacy against
COVID-19, most notably vaccination [43] and
vitamin D repletion [44].
In conclusion, we found mask mandates and use
to be poor predictors of COVID-19 spread in US
states. Strengths of our study include assessing
COVID-19 association with both mandates and
reported use; evaluating both Ln and Fold
growth models; accounting for population
density differences; and measuring case growth
after modeled mandate effective dates in states
with late or no mandates. Our study also has key
limitations. We did not assess counties or
localities, which may trend independently of
state averages. While dense sampling promotes
convergence, IHME masking estimates are
subject to survey bias. We only assessed one
biological quantity [confirmed and probable
COVID-19], but the ongoing pandemic warrants
assessment of other factors such as
hospitalizations and mortality. Importantly, our
study does not disprove the efficacy of all masks
in limited and controlled circumstances, such as
properly worn N95 respirators. A recent study
found that at typical respiratory fluence rates,
medical masks decrease airway deposition of
10-20µm SARS-CoV-2 particles but not 1-5µm
SARS-CoV-2 aerosols [45]. Aerosol expulsion
increases with COVID-19 disease severity in
non-human primates, as well as with age and
Damian D. Guerra et al., IRJPH, 2021; 5:55
BMI in humans without COVID-19 [46]. Together
with enhanced vaccination rates, aerosol
treatment with improved ventilation and air
purification could help reduce the size of COVID-
19 outbreaks.
The authors thank Brandy Jesernik, Ashley
Tracey, Jay Bhattacharya, Scott Atlas, and Erik
Fostvedt for manuscript input.
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Title: International Research Journal of Public Health
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... It is widely believed that the use of masks -including in the general population -could be an important measure to combat SARS-CoV2 [2]. Yet moderate or strong empirical scientific evidence for the effectiveness of masks when used by the general population is lacking, and there is solid data questioning the definite antiviral effectiveness of masks [3][4][5][6], even from the Cochrane database analysing systematic reviews [7]. And even mask-supportive reviews include statements such as: "wearing a mask could reduce the risk of COVID-19infection ", but " more evidence is still needed to better define the protective effect of the mask on the wider population" [1]. ...
... On the one hand there is no clear high-quality empirical data providing moderate or strong evidence that mask use in the general population could have a relevant impact on SARS-CoV2 virus transmission rates [3][4][5][6][7][8]10]. An overview of systematic reviews on mask use against airborne virus diseases [8] did find only one high quality study [7]. ...
Literature was systematically reviewed regarding CO exposure and facemask use. Observational and experimental data are helpful for a risk-benefit assessment for masks as a popular non-pharmaceutical intervention against SARS-CoV2 in the populace. Masks impede breathing by increasing the resistance and dead space volume leading to a re-breathing of CO with every breath taken. Fresh air has around 0.04% CO, while wearing masks more than 5 minutes bears a possible chronic exposure to carbon dioxide of 1.41% to 3.2% of the inhaled air. Although the buildup is usually within the short-term exposure limits, long-term consequences must be considered due to experimental data. US Navy toxicity experts set the exposure limits for submarines carrying female crews to 0.8% CO based on animal studies indicating an increased risk for stillbirths. Additionally, in mammals chronically exposed to 0.3% CO experimental data demonstrates teratogenicity with irreversible damage of neurons and reduced spatial learning caused by brainstem neuron apoptosis and a reduced blood level of the insulin-like growth factor 1. With significant impact on three readout parameters (morphological, functional, marker) this chronic 0.3% CO exposure has to be defined as being toxic. Additional data exists on the exposure of chronic 0.3% CO in adolescent mammals causing neuron destruction, which includes less activity, increased anxiety and impaired learning and memory. There is a possible negative impact risk by imposing extended mask mandates especially for vulnerable subgroups. Circumstantial evidence exists that extended mask use may be related to current observations of stillbirths and to reduced verbal motor and overall cognitive performance in children born during the pandemic. Extended masking in pregnant women, children and adolescents has not been thoroughly tested and studied. As a result of the animal experimental data available, a risk-benefit analysis is urgent and a need exists to rethink mask mandates, which provide appropriate warnings.
... 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]. ...
Full-text available
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.
... In May 2020 a meta-study of 14 publications carried out by the CDC [34] showed that wearing facemasks evinced no benefi ts during infl uenza outbreaks (infl uenza virions are the same size as coronaviruses). Later that year, no correlation was found between wearing facemasks and infection rates across the American states [35], and there was no evidence of benefi t reported from a German study [36]. Evidence presented in favor of wearing masks was often absurd. ...
Full-text available
The many legacies bequeathed to us by the COVID-19 pandemic have embraced novel approaches to vaccine development, a greater awareness of the principles of hygiene among the general public, the need for governments to take a proactive stance when faced with unfamiliar pathologies, and the role of specialist medical and scientific advisers. However, many of the claimed protocols are medieval in nature and have little scientific evidence to support their introduction. Similarly, we have failed to grasp the importance of the anti-vaccination campaigns, which are always founded on ignorance or willfulness, but on previous examples of duplicitous behavior by those in authority, coupled with our poor understanding of the way virus variants function, have engendered a sense that many of the precautions taken may have been unnecessary (Ford, 2020a). In consequence, I fear that future threats from hemolytic viruses of high transmissibility may be met with complacency. Improved public education, and greater academic transparency concerning the nature of risk, will be necessary if a future pandemic is to be effectively controlled.
... Thread diameter has been reported to be of significance in particle penetration (Chattopadhyay et al., 2015;Zangmeister et al., 2020) ▪ 25 o "We did not observe association between mask mandates or use and reduced COVID-19 spread in US states." (Guerra and Guerra, 2021) o Although a CDC study (Guy et al., 2021) concluded that "Mask mandates were associated with statistically significant decreases in county-level daily COVID-19 case and death growth rates within 20 days of implementation. Allowing on-premises restaurant dining was associated with increases in county-level case and death growth rates within 41-80 days after reopening.", ...
Full-text available
As a behavioral neuroscientist focused on psychopathology, the happenings pertaining to the measures taken to address COVID-19 and their consequences are as much of interest as anything other work that I do. This document is a collation of quotes and some comments pertaining to every aspect of COVID-19 and its impact on the brain and behavior, on the individual and on society as a whole. The same document is updated weekly (generally) and also available on the web at:
Full-text available
The novel coronavirus (COVID-19) that was first reported at the end of 2019 has impacted almost every aspect of life as we know it. This paper focuses on the incidence of the disease in Italy and Spain—two of the first and most affected European countries. Using two simple mathematical epidemiological models—the Susceptible-Infectious-Recovered model and the log-linear regression model, we model the daily and cumulative incidence of COVID-19 in the two countries during the early stage of the outbreak, and compute estimates for basic measures of the infectiousness of the disease including the basic reproduction number, growth rate, and doubling time. Estimates of the basic reproduction number were found to be larger than 1 in both countries, with values being between 2 and 3 for Italy, and 2.5 and 4 for Spain. Estimates were also computed for the more dynamic effective reproduction number, which showed that since the first cases were confirmed in the respective countries the severity has generally been decreasing. The predictive ability of the log-linear regression model was found to give a better fit and simple estimates of the daily incidence for both countries were computed.
Full-text available
CDC recommends a combination of evidence-based strategies to reduce transmission of SARS-CoV-2, the virus that causes COVID-19 (1). Because the virus is transmitted predominantly by inhaling respiratory droplets from infected persons, universal mask use can help reduce transmission (1). Starting in April, 39 states and the District of Columbia (DC) issued mask mandates in 2020. Reducing person-to-person interactions by avoiding nonessential shared spaces, such as restaurants, where interactions are typically unmasked and physical distancing (≥6 ft) is difficult to maintain, can also decrease transmission (2). In March and April 2020, 49 states and DC prohibited any on-premises dining at restaurants, but by mid-June, all states and DC had lifted these restrictions. To examine the association of state-issued mask mandates and allowing on-premises restaurant dining with COVID-19 cases and deaths during March 1-December 31, 2020, county-level data on mask mandates and restaurant reopenings were compared with county-level changes in COVID-19 case and death growth rates relative to the mandate implementation and reopening dates. Mask mandates were associated with decreases in daily COVID-19 case and death growth rates 1-20, 21-40, 41-60, 61-80, and 81-100 days after implementation. Allowing any on-premises dining at restaurants was associated with increases in daily COVID-19 case growth rates 41-60, 61-80, and 81-100 days after reopening, and increases in daily COVID-19 death growth rates 61-80 and 81-100 days after reopening. Implementing mask mandates was associated with reduced SARS-CoV-2 transmission, whereas reopening restaurants for on-premises dining was associated with increased transmission. Policies that require universal mask use and restrict any on-premises restaurant dining are important components of a comprehensive strategy to reduce exposure to and transmission of SARS-CoV-2 (1). Such efforts are increasingly important given the emergence of highly transmissible SARS-CoV-2 variants in the United States (3,4).
Full-text available
Significance Superspreading events have distinguished the COVID-19 pandemic from the early outbreak of the disease. Our studies of exhaled aerosol suggest that a critical factor in these and other transmission events is the propensity of certain individuals to exhale large numbers of small respiratory droplets. Our findings indicate that the capacity of airway lining mucus to resist breakup on breathing varies significantly between individuals, with a trend to increasing with the advance of COVID-19 infection and body mass index multiplied by age (i.e., BMI-years). Understanding the source and variance of respiratory droplet generation, and controlling it via the stabilization of airway lining mucus surfaces, may lead to effective approaches to reducing COVID-19 infection and transmission.
Full-text available
Face masks are an important component in controlling COVID-19, and policy orders to wear masks are common. However, behavioral responses are seldom additive, and exchanging one protective behavior for another could undermine the COVID-19 policy response. We use SafeGraph smart device location data and variation in the date that US states and counties issued face mask mandates as a set of natural experiments to investigate risk compensation behavior. We compare time at home and the number of visits to public locations before and after face mask orders conditional on multiple statistical controls. We find that face mask orders lead to risk compensation behavior. Americans subject to the mask orders spend 11–24 fewer minutes at home on average and increase visits to some commercial locations—most notably restaurants, which are a high-risk location. It is unclear if this would lead to a net increase or decrease in transmission. However, it is clear that mask orders would be an important part of an economic recovery if people otherwise overestimate the risk of visiting public places.
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Background: Observational evidence suggests that mask wearing mitigates transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It is uncertain if this observed association arises through protection of uninfected wearers (protective effect), via reduced transmission from infected mask wearers (source control), or both. Objective: To assess whether recommending surgical mask use outside the home reduces wearers' risk for SARS-CoV-2 infection in a setting where masks were uncommon and not among recommended public health measures. Design: Randomized controlled trial (DANMASK-19 [Danish Study to Assess Face Masks for the Protection Against COVID-19 Infection]). ( NCT04337541). Setting: Denmark, April and May 2020. Participants: Adults spending more than 3 hours per day outside the home without occupational mask use. Intervention: Encouragement to follow social distancing measures for coronavirus disease 2019, plus either no mask recommendation or a recommendation to wear a mask when outside the home among other persons together with a supply of 50 surgical masks and instructions for proper use. Measurements: The primary outcome was SARS-CoV-2 infection in the mask wearer at 1 month by antibody testing, polymerase chain reaction (PCR), or hospital diagnosis. The secondary outcome was PCR positivity for other respiratory viruses. Results: A total of 3030 participants were randomly assigned to the recommendation to wear masks, and 2994 were assigned to control; 4862 completed the study. Infection with SARS-CoV-2 occurred in 42 participants recommended masks (1.8%) and 53 control participants (2.1%). The between-group difference was -0.3 percentage point (95% CI, -1.2 to 0.4 percentage point; P = 0.38) (odds ratio, 0.82 [CI, 0.54 to 1.23]; P = 0.33). Multiple imputation accounting for loss to follow-up yielded similar results. Although the difference observed was not statistically significant, the 95% CIs are compatible with a 46% reduction to a 23% increase in infection. Limitation: Inconclusive results, missing data, variable adherence, patient-reported findings on home tests, no blinding, and no assessment of whether masks could decrease disease transmission from mask wearers to others. Conclusion: The recommendation to wear surgical masks to supplement other public health measures did not reduce the SARS-CoV-2 infection rate among wearers by more than 50% in a community with modest infection rates, some degree of social distancing, and uncommon general mask use. The data were compatible with lesser degrees of self-protection. Primary funding source: The Salling Foundations.
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Background In recent years new forms of syndromic surveillance that use data from the Internet have been proposed. These have been developed to assist the early prediction of epidemics in various cases and diseases. It has been found that these systems are accurate in monitoring and predicting outbreaks before these are observed in population and, therefore, they can be used as a complement to other methods. In this research, our aim is to examine a highly infectious disease, measles, as there is no extensive literature on forecasting measles using Internet data, Methods This research has been conducted with official data on measles for 5 years (2013–2018) from the competent authority of the European Union (European Center of Disease and Prevention - ECDC) and data obtained from Google Trends by using scripts coded in Python. We compared regression models forecasting the development of measles in the five countries. Results Results show that measles can be estimated and predicted through Google Trends in terms of time, volume and the overall spread. The combined results reveal a strong relationship of measles cases with the predicted cases (correlation coefficient R = 0.779 in two-tailed significance p < 0.01). The mean standard error was relatively low 45.2 (12.19%) for the combined results. However, major differences and deviations were observed for countries with a relatively low impact of measles, such as the United Kingdom and Spain. For these countries, alternative models were tested in an attempt to improve the results. Conclusions The estimation of measles cases from Google Trends produces acceptable results and can help predict outbreaks in a robust and sound manner, at least 2 months in advance. Python scripts can be used individually or within the framework of an integrated Internet surveillance system for tracking epidemics as the one addressed here.
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The science around the use of masks by the public to impede COVID-19 transmission is advancing rapidly. In this narrative review, we develop an analytical framework to examine mask usage, synthesizing the relevant literature to inform multiple areas: population impact, transmission characteristics, source control, wearer protection, sociological considerations, and implementation considerations. A primary route of transmission of COVID-19 is via respiratory particles, and it is known to be transmissible from presymptomatic, paucisymptomatic, and asymptomatic individuals. Reducing disease spread requires two things: limiting contacts of infected individuals via physical distancing and other measures and reducing the transmission probability per contact. The preponderance of evidence indicates that mask wearing reduces transmissibility per contact by reducing transmission of infected respiratory particles in both laboratory and clinical contexts. Public mask wearing is most effective at reducing spread of the virus when compliance is high. Given the current shortages of medical masks, we recommend the adoption of public cloth mask wearing, as an effective form of source control, in conjunction with existing hygiene, distancing, and contact tracing strategies. Because many respiratory particles become smaller due to evaporation, we recommend increasing focus on a previously overlooked aspect of mask usage: mask wearing by infectious people (“source control”) with benefits at the population level, rather than only mask wearing by susceptible people, such as health care workers, with focus on individual outcomes. We recommend that public officials and governments strongly encourage the use of widespread face masks in public, including the use of appropriate regulation.
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Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and the resulting coronavirus disease 2019 (Covid-19) have afflicted tens of millions of people in a worldwide pandemic. Safe and effective vaccines are needed urgently. Methods Download a PDF of the Research Summary. In an ongoing multinational, placebo-controlled, observer-blinded, pivotal efficacy trial, we randomly assigned persons 16 years of age or older in a 1:1 ratio to receive two doses, 21 days apart, of either placebo or the BNT162b2 vaccine candidate (30 μg per dose). BNT162b2 is a lipid nanoparticle–formulated, nucleoside-modified RNA vaccine that encodes a prefusion stabilized, membrane-anchored SARS-CoV-2 full-length spike protein. The primary end points were efficacy of the vaccine against laboratory-confirmed Covid-19 and safety. Results A total of 43,548 participants underwent randomization, of whom 43,448 received injections: 21,720 with BNT162b2 and 21,728 with placebo. There were 8 cases of Covid-19 with onset at least 7 days after the second dose among participants assigned to receive BNT162b2 and 162 cases among those assigned to placebo; BNT162b2 was 95% effective in preventing Covid-19 (95% credible interval, 90.3 to 97.6). Similar vaccine efficacy (generally 90 to 100%) was observed across subgroups defined by age, sex, race, ethnicity, baseline body-mass index, and the presence of coexisting conditions. Among 10 cases of severe Covid-19 with onset after the first dose, 9 occurred in placebo recipients and 1 in a BNT162b2 recipient. The safety profile of BNT162b2 was characterized by short-term, mild-to-moderate pain at the injection site, fatigue, and headache. The incidence of serious adverse events was low and was similar in the vaccine and placebo groups. Conclusions A two-dose regimen of BNT162b2 conferred 95% protection against Covid-19 in persons 16 years of age or older. Safety over a median of 2 months was similar to that of other viral vaccines. (Funded by BioNTech and Pfizer; number, NCT04368728.)