Content uploaded by Daniel J. Guerra
Author content
All content in this area was uploaded by Daniel J. Guerra on Feb 01, 2022
Content may be subject to copyright.
*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.
Website: https://escipub.com/
Damian D. Guerra et al., IRJPH, 2021; 5:55
International Research Journal of Public Health
(ISSN:2573-380X)
Research Article IRJPH (2021) 5:55
Mask mandate and use efficacy for COVID-19 containment in US
States
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
conict 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 dierences 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 ecacious 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
ABSTRACT
IRJPH: https://escipub.com/international-research-journal-of-public-health/ 1
Damian D. Guerra et al., IRJPH, 2021; 5:55
IRJPH: https://escipub.com/international-research-journal-of-public-health/ 2
Introduction
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
states.
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
IRJPH: https://escipub.com/international-research-journal-of-public-health/ 3
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
[Fall-Winter].
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.
IRJPH: https://escipub.com/international-research-journal-of-public-health/ 4
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.
Statistics
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
residuals.
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.
Results
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
IRJPH: https://escipub.com/international-research-journal-of-public-health/ 5
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
IRJPH: https://escipub.com/international-research-journal-of-public-health/ 6
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
states
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
IRJPH: https://escipub.com/international-research-journal-of-public-health/ 7
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
states
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
IRJPH: https://escipub.com/international-research-journal-of-public-health/ 8
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
IRJPH: https://escipub.com/international-research-journal-of-public-health/ 9
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.
IRJPH: https://escipub.com/international-research-journal-of-public-health/ 10
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
states.
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].
Discussion
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.
IRJPH: https://escipub.com/international-research-journal-of-public-health/ 11
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
IRJPH: https://escipub.com/international-research-journal-of-public-health/ 12
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.
Acknowledgments
The authors thank Brandy Jesernik, Ashley
Tracey, Jay Bhattacharya, Scott Atlas, and Erik
Fostvedt for manuscript input.
References
[1]. Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et
al. A Novel Coronavirus from Patients with
Pneumonia in China, 2019. N Engl J Med.
2020;382[8]:727-33.
[2]. Hojyo S, Uchida M, Tanaka K, Hasebe R, Tanaka
Y, Murakami M, et al. How COVID-19 induces
cytokine storm with high mortality. Inflamm Regen.
2020;40:37.
[3]. Chu Y, Yang J, Shi J, Zhang P, Wang X. Obesity is
associated with increased severity of disease in
COVID-19 pneumonia: a systematic review and
meta-analysis. Eur J Med Res. 2020;25[1]:64.
[4]. Jayaweera M, Perera H, Gunawardana B,
Manatunge J. Transmission of COVID-19 virus by
droplets and aerosols: A critical review on the
unresolved dichotomy. Environ Res.
2020;188:109819.
[5]. Payne DC, Smith-Jeffcoat SE, Nowak G,
Chukwuma U, Geibe JR, Hawkins RJ, et al. SARS-
CoV-2 Infections and Serologic Responses from a
Sample of U.S. Navy Service Members - USS
Theodore Roosevelt, April 2020. MMWR Morb
Mortal Wkly Rep. 2020;69[23]:714-21.
[6]. Hendrix MJ, Walde C, Findley K, Trotman R.
Absence of Apparent Transmission of SARS-CoV-
2 from Two Stylists After Exposure at a Hair Salon
with a Universal Face Covering Policy - Springfield,
Missouri, May 2020. MMWR Morb Mortal Wkly Rep.
2020;69[28]:930-2.
[7]. Ludvigsson JF, Engerström L, Nordenhäll C,
Larsson E. Open Schools, Covid-19, and Child and
Teacher Morbidity in Sweden. N Engl J Med.
2021;384[7]:669-71.
[8]. Bundgaard H, Bundgaard JS, Raaschou-Pedersen
DET, von Buchwald C, Todsen T, Norsk JB, 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]:335-43.
[9]. MacIntyre CR, Seale H, Dung TC, Hien NT, Nga
PT, Chughtai AA, et al. A cluster randomised trial of
cloth masks compared with medical masks in
healthcare workers. BMJ Open.
2015;5[4]:e006577.
[10]. MacIntyre CR, Wang Q, Rahman B, Seale H,
Ridda I, Gao Z, et al. Efficacy of face masks and
respirators in preventing upper respiratory tract
bacterial colonization and co-infection in hospital
healthcare workers. Prev Med. 2014;62:1-7.
[11]. Howard J, Huang A, Li Z, Tufekci Z, Zdimal V, van
der Westhuizen H-M, et al. An evidence review of
face masks against COVID-19. Proceedings of the
National Academy of Sciences.
2021;118[4]:e2014564118.
[12]. Wang Y, Tian H, Zhang L, Zhang M, Guo D, Wu
W, et al. Reduction of secondary transmission of
SARS-CoV-2 in households by face mask use,
disinfection and social distancing: a cohort study in
Beijing, China. BMJ Glob Health. 2020;5[5].
[13]. United States COVID-19 Cases and Deaths by
State over Time. Centers for Disease Control and
Prevention [CDC]; 2021.
[14]. Worldometers.info. Worldometer Dover, DE
USA2021 [updated 25 May 2021; cited 2021.
Available from:
https://www.worldometers.info/coronavirus/country
/us/.
[15]. Guy GP, Jr., Lee FC, Sunshine G, McCord R,
Howard-Williams M, Kompaniyets L, et al.
Association of State-Issued Mask Mandates and
Allowing On-Premises Restaurant Dining with
County-Level COVID-19 Case and Death Growth
Rates - United States, March 1-December 31,
2020. MMWR Morb Mortal Wkly Rep.
2021;70[10]:350-4.
[16]. Lyu W, Wehby GL. Community Use Of Face
Masks And COVID-19: Evidence From A Natural
Experiment Of State Mandates In The US. Health
Aff [Millwood]. 2020;39[8]:1419-25.
[17]. Institute for Health Metrics and Evaluation
COVID-19 model: University of Washington; 2021
[Available from: https://covid19.healthdata.org/.
[18]. Liu Z, Fang CT. A modeling study of human
infections with avian influenza A H7N9 virus in
mainland China. Int J Infect Dis. 2015;41:73-8.
[19]. Perneger T, Kevorkian A, Grenet T, Gallée H,
Gayet-Ageron A. Alternative graphical displays for
the monitoring of epidemic outbreaks, with
application to COVID-19 mortality. BMC Medical
Research Methodology. 2020;20[1]:248.
[20]. De Flora S, La Maestra S. Growth and decline of
the COVID-19 epidemic wave in Italy from March to
June 2020. J Med Virol. 2021;93[3]:1613-9.
IRJPH: https://escipub.com/international-research-journal-of-public-health/ 13
Damian D. Guerra et al., IRJPH, 2021; 5:55
[21]. Bureau USC. 2010 Census Urban and Rural
Classification and Urban Area Criteria. In:
Commerce UDo, editor. 2010.
[22]. Curtis MJ, Alexander S, Cirino G, Docherty JR,
George CH, Giembycz MA, et al. Experimental
design and analysis and their reporting II: updated
and simplified guidance for authors and peer
reviewers. British Journal of Pharmacology.
2018;175[7]:987-93.
[23]. Khan A, Waleed M, Imran M. Mathematical
analysis of an influenza epidemic model,
formulation of different controlling strategies using
optimal control and estimation of basic reproduction
number. Mathematical and Computer Modelling of
Dynamical Systems. 2015;21[5]:432-59.
[24]. Samaras L, Sicilia M-A, García-Barriocanal E.
Predicting epidemics using search engine data: a
comparative study on measles in the largest
countries of Europe. BMC Public Health.
2021;21[1]:100.
[25]. Ghosal S, Sengupta S, Majumder M, Sinha B.
Linear Regression Analysis to predict the number of
deaths in India due to SARS-CoV-2 at 6 weeks from
day 0 [100 cases - March 14th 2020]. Diabetes
Metab Syndr. 2020;14[4]:311-5.
[26]. Chu J. A statistical analysis of the novel
coronavirus [COVID-19] in Italy and Spain. PloS
one. 2021;16[3]:e0249037.
[27]. Jha AK, Tsai T, Jacobson B. Why we need at least
500,000 tests per day to open the economy — and
stay open: Brown School of Public Health; 2020
[Available from:
https://globalepidemics.org/2020/04/18/why-we-
need-500000-tests-per-day-to-open-the-economy-
and-stay-open/.
[28]. Anand S, Montez-Rath M, Han J, Bozeman J,
Kerschmann R, Beyer P, et al. Prevalence of
SARS-CoV-2 antibodies in a large nationwide
sample of patients on dialysis in the USA: a cross-
sectional study. Lancet [London, England].
2020;396[10259]:1335-44.
[29]. Chan JF, Yuan S, Zhang AJ, Poon VK, Chan CC,
Lee AC, et al. Surgical Mask Partition Reduces the
Risk of Noncontact Transmission in a Golden
Syrian Hamster Model for Coronavirus Disease
2019 [COVID-19]. Clin Infect Dis.
2020;71[16]:2139-49.
[30]. Wu J, Xu F, Zhou W, Feikin DR, Lin CY, He X, et
al. Risk factors for SARS among persons without
known contact with SARS patients, Beijing, China.
Emerg Infect Dis. 2004;10[2]:210-6.
[31]. Cowling BJ, Chan KH, Fang VJ, Cheng CK, Fung
RO, Wai W, et al. Facemasks and hand hygiene to
prevent influenza transmission in households: a
cluster randomized trial. Ann Intern Med.
2009;151[7]:437-46.
[32]. Fong MW, Leung NHL, Cowling BJ, Wu P. Upper
Respiratory Infections in Schools and Childcare
Centers Reopening after COVID-19 Dismissals,
Hong Kong. Emerg Infect Dis. 2021;27[5].
[33]. Fong MW, Gao H, Wong JY, Xiao J, Shiu EYC,
Ryu S, et al. Nonpharmaceutical Measures for
Pandemic Influenza in Nonhealthcare Settings-
Social Distancing Measures. Emerg Infect Dis.
2020;26[5]:976-84.
[34]. Jefferson T, Del Mar CB, Dooley L, Ferroni E, Al-
Ansary LA, Bawazeer GA, et al. Physical
interventions to interrupt or reduce the spread of
respiratory viruses. Cochrane Database of
Systematic Reviews. 2020[11].
[35]. Goh Y, Tan BYQ, Bhartendu C, Ong JJY, Sharma
VK. The face mask: How a real protection becomes
a psychological symbol during Covid-19? Brain
Behav Immun. 2020;88:1-5.
[36]. Yan Y, Bayham J, Richter A, Fenichel EP. Risk
compensation and face mask mandates during the
COVID-19 pandemic. Scientific Reports.
2021;11[1]:3174.
[37]. Spitzer M. Masked education? The benefits and
burdens of wearing face masks in schools during
the current Corona pandemic. Trends Neurosci
Educ. 2020;20:100138.
[38]. Corey RM, Jones U, Singer AC. Acoustic effects
of medical, cloth, and transparent face masks on
speech signals. J Acoust Soc Am.
2020;148[4]:2371.
[39]. Atcherson SR, Finley ET, Renee McDowell BR,
Watson C. More Speech Degradations and
Considerations in the Search for Transparent Face
Coverings During the COVID-19 Pandemic.
Audiology Today. 2020;Nov/Dec.
[40]. Hua W, Zuo Y, Wan R, Xiong L, Tang J, Zou L, et
al. Short-term skin reactions following use of N95
respirators and medical masks. Contact Dermatitis.
2020;83[2]:115-21.
[41]. Davey SL, Lee BJ, Robbins T, Randeva H, Thake
CD. Heat stress and PPE during COVID-19: impact
on healthcare workers' performance, safety and
well-being in NHS settings. J Hosp Infect.
2021;108:185-8.
[42]. Rosenberg K. The joint commission addresses
health care worker fatigue. Am J Nurs.
2014;114[7]:17.
[43]. Polack FP, Thomas SJ, Kitchin N, Absalon J,
Gurtman A, Lockhart S, et al. Safety and Efficacy of
the BNT162b2 mRNA Covid-19 Vaccine. N Engl J
Med. 2020;383[27]:2603-15.
IRJPH: https://escipub.com/international-research-journal-of-public-health/ 14
Damian D. Guerra et al., IRJPH, 2021; 5:55
[44]. Radujkovic A, Hippchen T, Tiwari-Heckler S,
Dreher S, Boxberger M, Merle U. Vitamin D
Deficiency and Outcome of COVID-19 Patients.
Nutrients. 2020;12[9].
[45]. Xi J, Si XA, Nagarajan R. Effects of mask-wearing
on the inhalability and deposition of airborne SARS-
CoV-2 aerosols in human upper airway. Phys Fluids
[1994]. 2020;32[12]:123312.
[46]. Edwards DA, Ausiello D, Salzman J, Devlin T,
Langer R, Beddingfield BJ, et al. Exhaled aerosol
increases with COVID-19 infection, age, and
obesity. Proceedings of the National Academy of
Sciences of the United States of America.
2021;118[8].
• Title: International Research Journal of Public Health
• ISSN: 2573-380X
• DOI: 10.28933/IRJPH
• IF: 1.36 (citefactor)
• Email: IRJPH@escipub.com
• TEL: +1-281-656-1158
About the journal
The journal is hosted by eSciPub LLC. Our aim is to provide a platform that encourages publication of the
most recent research and reviews for authors from all countries.
About the publisher
eSciPub LLC is a publisher to support Open Access initiative located in Houston, Texas, USA. It is a member
of the largest community of professional publishers in the United States: the Independent Book Publishers
Association. It hosts more than 100 Open Access journals in Medicine, Business & Economics, Agriculture,
Biological Sciences, Chemistry, Education, Physical Sciences, Sociology, and Engineering and Technology.
Rapid Response Team
Please feel free to contact our rapid response team if you have any questions. Our customer representative
will answer your questions shortly.
Notes from Editorial Office
International Research Journal of Public Health (ISSN:2573-380X) is committed to freedom of expression.
Sharing different research information in public health improves global health and creates new insights. Our
aim is to provide a platform that encourages publication of the most recent research and reviews for authors
from all countries. The article entitled “Mask mandate and use efficacy for COVID-19 containment in US
States” by Drs Damian D. Guerra and Daniel J. Guerra was initially reviewed and then accepted for
publication without peer-review (Per our policies, we may accept and publish manuscripts of authors
without peer-review. click here for details). Should you have any questions or comments regarding the article,
you may contact the authors directly. Or please contact us.
Terms of Use/Privacy Policy/ Disclaimer/ Other Policies:
You agree that by using our site, you have read, understood, and agreed to be bound by all of our terms of
use/privacy policy/ disclaimer/ other policies (click here for details). This site cannot and does not contain
professional advice. The information on this site is provided for general informational and educational
purposes only and is not a substitute for professional advice. Accordingly, before taking any actions based
upon such information, we encourage you to consult with the appropriate professionals. We do not provide
any kind of professional advice. The use or reliance of any information contained on this site or our mobile
application is solely at your own risk. Under no circumstance shall we have any liability to you for any loss or
damage of any kind incurred as a result of the use of the site or our mobile application or reliance on any
information provided on the site and our mobile application. We may publish articles without peer-review.
Published articles of authors are open access. Authors hold the copyright and retain publishing rights without
restrictions. Authors are solely responsible for their articles published in our journals. Publication of any
information in authors’ articles does not constitute an endorsement by us. We make no representation or
warranty of any kind, express or implied, regarding the accuracy, adequacy, validity, reliability, availability or
completeness of any information that authors provided. more.....
This work and its PDF file(s) are licensed under under a Creative Commons Attribution 4.0 International
License.
CC BY 4.0