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We present two models for the COVID-19 pandemic predicting the impact of universal face mask wearing upon the spread of the SARS-CoV-2 virus—one employing a stochastic dynamic network based compartmental SEIR (susceptible-exposed-infectious-recovered) approach, and the other employing individual ABM (agent-based modelling) Monte Carlo simulation—indicating (1) significant impact under (near) universal masking when at least 80% of a population is wearing masks, versus minimal impact when only 50% or less of the population is wearing masks, and (2) significant impact when universal masking is adopted early, by Day 50 of a regional outbreak, versus minimal impact when universal masking is adopted late. These effects hold even at the lower filtering rates of homemade masks. To validate these theoretical models, we compare their predictions against a new empirical data set we have collected that includes whether regions have universal masking cultures or policies, their daily case growth rates, and their percentage reduction from peak daily case growth rates. Results show a near perfect correlation between early universal masking and successful suppression of daily case growth rates and/or reduction from peak daily case growth rates, as predicted by our theoretical simulations. Taken in tandem, our theoretical models and empirical results argue for urgent implementation of universal masking in regions that have not yet adopted it as policy or as a broad cultural norm. As governments plan how to exit societal lockdowns, universal masking is emerging as one of the key NPIs (non-pharmaceutical interventions) for containing or slowing the spread of the pandemic. Combined with other NPIs including social distancing and mass contact tracing, a ``mouth-and-nose lockdown'' is far more sustainable than a ``full body lockdown'', from economic, social, and mental health standpoints. To provide both policy makers and the public with a more concrete feel for how masks impact the dynamics of virus spread, we are making an interactive visualization of the ABM simulation available online at http://dek.ai/masks4all. We recommend immediate mask wearing recommendations, official guidelines for correct use, and awareness campaigns to shift masking mindsets away from pure self-protection, towards aspirational goals of responsibly protecting one's community.
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Universal Masking is Urgent in the COVID-19 Pandemic:
SEIR and Agent Based Models, Empirical Validation,
Policy Recommendations
De Kai PHD MBA
HKUST (University of Science & Technology), Hong Kong
International Computer Science Institute, Berkeley, CA, USA
dekai@cs.ust.hk dekai@icsi.berkeley.edu
@dekai123 http://dek.ai
Guy-Philippe Goldstein MBA
Ecole de Guerre Economique, Paris, France
guyphilippeg@gmail.com
@guypgoldstein
Alexey Morgunov
University of Cambridge, UK
Manifold Research, Cambridge, UK
asm63@cam.ac.uk alexey@manifoldresearch.com
@AlexeyMorgunov
Vishal Nangalia PHD MBC HB FRCA
University College London, UK
ELU AI Ltd, London, UK
Royal Free Hospital, London, UK
vishal.nangalia@gmail.com
@v alien
Anna Rotkirch PHD
Population Research Institute, The Family Federation of Finland
anna.rotkirch@vaestoliitto.fi
@AnnaRotkirch https://blogs.helsiki.fi/rotkirch
21 April 2020
Abstract
We present two models for the COVID-19 pandemic
predicting the impact of universal face mask wearing
upon the spread of the SARS-CoV-2 virusone employ-
ing a stochastic dynamic network based compartmen-
tal SEIR (susceptible-exposed-infectious-recovered) ap-
proach, and the other employing individual ABM (agent-
based modelling) Monte Carlo simulationindicating (1)
significant impact under (near) universal masking when at
least 80% of a population is wearing masks, versus min-
imal impact when only 50% or less of the population is
wearing masks, and (2) significant impact when universal
masking is adopted early, by Day 50 of a regional out-
break, versus minimal impact when universal masking is
adopted late. These effects hold even at the lower filtering
rates of homemade masks. To validate these theoretical
models, we compare their predictions against a new em-
pirical data set we have collected that includes whether
regions have universal masking cultures or policies, their
daily case growth rates, and their percentage reduction
from peak daily case growth rates. Results show a near
perfect correlation between early universal masking and
successful suppression of daily case growth rates and/or
reduction from peak daily case growth rates, as predicted
by our theoretical simulations.
Taken in tandem, our theoretical models and empiri-
cal results argue for urgent implementation of universal
masking in regions that have not yet adopted it as policy or
*This collective work grew out of a Kinnernet discussion group
about COVID-19 initiated by Guy-Philippe Goldstein. All authors con-
tributed to the overall design and writing. Additionally, Goldstein for-
mulated overall study goals and analysed policy data, Morgunov ran the
SEIR simulation and collected policy data, De Kai created the online
interactive ABM simulation, Nangalia contributed with medical exper-
tise and to the model design, and Rotkirch and De Kai first drafted the
report.
1
arXiv:2004.13553v1 [physics.soc-ph] 22 Apr 2020
as a broad cultural norm. As governments plan how to exit
societal lockdowns, universal masking is emerging as one
of the key NPIs (non-pharmaceutical interventions) for
containing or slowing the spread of the pandemic. Com-
bined with other NPIs including social distancing and
mass contact tracing, a “mouth-and-nose lockdown” is far
more sustainable than a “full body lockdown”, from eco-
nomic, social, and mental health standpoints. To provide
both policy makers and the public with a more concrete
feel for how masks impact the dynamics of virus spread,
we are making an interactive visualization of the ABM
simulation available online at http://dek.ai/masks4all. We
recommend immediate mask wearing recommendations,
official guidelines for correct use, and awareness cam-
paigns to shift masking mindsets away from pure self-
protection, towards aspirational goals of responsibly pro-
tecting one’s community.
1 Introduction
With almost all of the world’s countries having imposed
measures of social distancing and restrictions on move-
ment in March 2020 to combat the COVID-19 pan-
demic, governments now seek a sustainable pathway back
towards eased social restrictions and a functioning econ-
omy. Mass testing for infection and serological tests for
immunity, combined with mass contact tracing, quaran-
tine of infected individuals, and social distancing, are rec-
ommended by the WHO and have become widely ac-
knowledged means of controlling spread of the SARS-
CoV-2 virus until a vaccine is available.
Against this backdrop, a growing number of voices
suggest that universal face mask wearing, as practiced ef-
fectively in most East Asian regions, is an additional, es-
sential component in the mitigation toolkit for a sustain-
able exit from harsh lockdowns. The masks-for-all argu-
ment claims that “test, trace, isolate” should be expanded
to “test, trace, isolate, mask”. This paper presents cross-
disciplinary, multi-perspective arguments for the urgency
of universal masking, via both new theoretical models and
new empirical data analyses. Specifically, we aim to illus-
trate how different degrees of mass face wearing affects
infection rates, and why the timing of introduction of uni-
versal masking is crucial.
In the first of two new theoretical models, we
introduce an SEIR (susceptible-exposed-infectious-
recovered) model of the effects of mass face mask wear-
ing over time compared to effects of social distancing and
lockdown. In the second of two new theoretical models,
we introduce a new interactive individual ABM (agent-
based modelling) Monte Carlo simulation showing how
masking significantly lowers rates of transmission. Both
models predict significant reduction in the daily growth of
infections on average under universal masking (80-90%
of the population) if instituted by day 50 of an outbreak,
but not if only 50% of the population wear masks or if
institution of universal masking is delayed.
We then compare the two new simulations presented
here against a new empirical data set we have collected
that includes whether regions have universal masking cul-
tures or policies, their daily case growth rates, and their
percentage reduction from peak daily case growth rates.
Since little precise quantitative data is available on cul-
tures where masking is prevalent, we explain in some
depth the historical and sociological factors that support
our classification of masking cultures. Results show a
near perfect correlation between early universal mask-
ing and successful suppression of daily case growth rates
and/or reduction from peak daily case growth rates, as
predicted by our theoretical simulations.
To preview the key policy recommendations that our
two new SEIR and ABM predictive models and empirical
validation all lead to:
1. Masking should be mandatory or strongly recom-
mended for the general public when in public trans-
port and public spaces, for the duration of the pan-
demic.
2. Masking should be mandatory for individuals in es-
sential functions (health care workers, social and
family workers, the police and the military, the ser-
vice sector, construction workers, etc.) and medical
masks and gloves or equally safe protection should
be provided to them by employers. Cloth masks
should be used if medical masks are unavailable.
3. Countries should aim to eventually secure mass
production and availability of appropriate medical
masks (without exploratory valves) for the entire
population during the pandemic.
2
4. Until supplies are sufficient, members of the general
public should wear nonmedical fabric face masks
when going out in public and medical masks should
be reserved for essential functions.
5. The authorities should issue masking guidelines to
residents and companies regarding the correct and
optimal ways to make, wear and disinfect masks.
6. The introduction of mandatory masking will benefit
from being rolled out together with campaigns, citi-
zen initiatives, the media, NGOs, and influencers in
order to avoid a public backlash in societies not cul-
turally accustomed to masking. Public awareness is
needed that “masking protects your communitynot
just you”.
2 Background
Masks indisputably protect individuals against airborne
transmission of respiratory diseases. A recent Cochrane
meta-analysis found that masking, handwashing, and us-
ing gowns and/or gloves can reduce the spread of respira-
tory viruses, although evidence for any individual one of
these measures is still of low certainty (Burch and Bunt,
2020). Currently, the lowest recorded daily growth rates
in COVID-19 infections appear to be found in countries
with a culture of mass face mask wearing, most of whom
have also made mask wearing in public mandatory during
the epidemic, and most of whom are not currently locked
downan observation that we study systematically in sec-
tion 5.
Outside of East Asia, support for universal masking is
emerging elsewhere across the globe. The Czech Repub-
lic was the first non-Asian country to embrace and im-
pose mandatory universal masking on March 11, 2020.
The Czech policy swiftly inspired various initiatives from
citizens, journalists and scientistse.g., De Kai (2020),
Howard and Fast.ai team (2020), Manjoo (2020), Abaluck
et al. (2020), Feng et al. (2020), Fineberg (2020), Tufekci
(2020)and created global movements such as #masks4all
and #wearafuckingmask. Their arguments build on the
ability of the COVID-19 virus to spread from pre- and
asymptomatic individuals who may not know that they
are infected, and to linger in airborne droplets.
Leading political and medical experts who early were
advocated masking included Chinese CDC director-
general Prof. George Fu Gao (Servick, 2020), former
FDA commissioner Scott Gottlieb and Prof. Caitlin
Rivers of Johns Hopkins (Gottlieb and Rivers, 2020), and
the American Enterprise Institute’s roadmap (Gottlieb et
al., 2020).
In early April 2020 a rapidly increasing number of
governments from countries without a previous culture
of mask wearing require or recommend universal mask-
ing including the Czech Republic, Austria and Slovakia.
Additionally, public health bodies in the USA, Germany,
France (Acadmie nationale de mdecine, 2020) and New
Zealand have moved toward universal masking recom-
mendations (Morgunov et al., 2020), as shown below in
Figure 6.
The World Health Organization (2019) previously is-
sued guidelines discouraging the use of masks in the pub-
lic. However in early April 2020 the World Health Or-
ganization (2020) modified the guidelines, allowing self-
made masks but rightly stressing the need to reserve med-
ical masks for healthcare workers (Nebehay and Shalal,
2020), and to combine masking with the other main NPI
needed to combat the pandemic. The policy shifts of the
WHO and other CDCs reflect advances in our scientific
understanding of this pandemic, and help legitimise the
altruistic “mask resistance” of civil society in this global
effort against COVID-19.
3 SEIR modelling of universal
masking impact
In the first of our two new theoretical models, we em-
ployed stochastic dynamic network based compartmen-
tal SEIR modeling to forecast the relative impact of
masking compared to the two main other societal non-
pharmaceutical interventions, lockdown, and social dis-
tancing.
The SEIR simulations were fit to the current timeline in
many Western countries, with a lockdown imposed March
the 24th (day 1) and planned to be lifted on May 31st.
Universal masking is introduced in April. The simulation
continues for 500 days from day 0, or around 17 months.
The experimental results strongly support the need for
3
universal masking as an alternative to continued lockdown
scenarios. For this strategy to be most effective, the vast
majority of the population must adopt mask wearing im-
mediately, as most regions outside East Asia are rapidly
approaching Day 50.
In a SEIR model, the population is divided into com-
partments which represent different states with respect
to disease progression of an individual: susceptible
(S), exposed (E), infectious (I) and recovered (R).
A susceptible individual may become exposed if they
interact with an infectious individual at rate β(rate of
transmission per S-Icontact per time). From E, the
individual progresses to being infectious (I) and even-
tually recovered (R) with rates σ(rate of progression)
and γ(rate of recovery), respectively. Additionally,
individuals in Iare removed from the population (i.e.,
die of the disease) at rate µI(rate of mortality).
We used a SEIR model implemented1on a stochastic
dynamical network that more closely mimics interactions
between individuals in society, instead of assuming
uniform mixing as is the case with deterministic SEIR
models. Furthermore, such approach allows setting
different model parameters for each individual, which
we use to model masking. In a network model, a graph
of society is built with nodes representing individuals
and edgestheir interactions. Each node has a state
S,E,I,R, or F(the latter added to represent dead
individuals). Adjacent nodes form close contact networks
of an individual, while contacts made with an individual
from anywhere in the network represent global contacts
in the population. Varying the parameters affecting the
two levels of interaction, as well as setting network
properties such as the mean number of adjacent nodes
(“close contacts”) allows us to model the degree of social
distancing and lockdown measures.
Formally, each node iis associated with a state Xi
which is updated based on the following probability
transition rates:
1https://github.com/ryansmcgee/seirsplus
Pr(Xi=SE)=[pβI
N+(1p)βPjCG(i)δXj=i
|CG(i)|]δXi=S
(1)
Pr(Xi=EI) = σδXi=E(2)
Pr(Xi=IR) = γδXi=I(3)
Pr(Xi=IF) = µIδXi=I(4)
where δXi=A= 1 if the state of Xiis A, or 0if
not, and where CG(i)denotes the set of close contacts
of node i.
3.1 Experimental model
We implemented SEIR dynamics on a stochastic dynamic
network with a heterogeneous population. We assumed
an initial infected population of 1% and modelled the as-
sumed effects of social distancing, lockdown, and univer-
sal masking over time on the rates of infection in the pop-
ulation.
All SEIR models were built using the SEIRS+ mod-
elling tool2, version 0.0.14. The baseline model param-
eters are fit to the empirical characteristics of COVID-19
spread, as documented in the SEIRS+ distributed COVID-
19 notebooks. Specifically, we set β= 0.155,σ= 1/5.2
and γ= 1/12.39. This parameterisation describes a
SEIR model with best estimates for COVID-19 dynam-
ics.
The initial infected population (initi) was set to 1%,
and all others to 0%. The size of the total population was
set to 67,000 (a representative typical case, that is a factor
of 1,000 from the population of the UK).
Social distancing. In the model, social distanc-
ing was defined as the degree distribution of the con-
tact network of an individual. Default interaction net-
works were used, constructed as Barabasi-Albert graphs
with m= 9 and processes using the package function cus-
tom exponential graph with different scale parameters.
Normal graph (scale=100) with mean degree 13.2, dis-
tancing graph (scale=10) with mean degree 4.1 and lock-
down graph (scale=5) with mean degree 2.2.
Lockdown stringency. Lockdown stringency was
modelled considering no stringent lockdown (i.e. only
2https://github.com/ryansmcgee/seirsplus
4
Figure 1: Simulation results for a representative scenario: universal masking at 80% adoption (red) flattens the curve
significantly more than maintaining a strict lockdown (blue). Masking at only 50% adoption (orange) is not sufficient
to prevent continued spread. Replacing the strict lockdown with social distancing on May 31 without masking results
in unchecked spread.
social distancing) or stringent lockdown using the local-
ity parameter p, which was set to 0.02 during lockdown
and 0.2 during social distancing phases. This dictates the
probability of individuals coming into contact with those
outside of their immediate network. Assuming that indi-
viduals have around 13 contacts in normal everyday life,
social distancing will reduce this to 4 and lockdown to
only 2.
Mask wearing. A gradual increase in mask wearing
was modelled using a linear increase in the proportion
of individuals randomly allocated with a reduced rate of
transmission. The factor by which βwas reduced was
conservatively set to 2. The period of time over which the
mask wearing went from 0 to maximum % was set to 10
days. 50% and 80% maximum values were considered.
Date fitting. The progression in the number of deaths
was used to fit the model to an approximate calendar date
representing Day 0. For the representative typical case of
the UK, this corresponded to Mar 23.
3.2 Experimental results
Figure 1 shows the simulation results for a representative
scenario: universal masking at 80% adoption (red) flattens
the curve significantly more than maintaining a strict lock-
down (blue). Masking at only 50% adoption (orange) is
not sufficient to prevent continued spread. Replacing the
strict lockdown with social distancing on May 31 without
masking results in unchecked spread.
Our model suggests a substantial impact of universal
5
Figure 2: Simulation results for a representative scenario: universal masking at 80% adoption (red) results in 60,000
deaths, compared to maintaining a strict lockdown (blue) which results in 180,000 deaths. Masking at only a 50%
adoption rate (orange) is not sufficient to prevent continued spread and eventually results in 240,000 deaths. Replacing
the strict lockdown with social distancing on May 31 without masking results in unchecked spread.
masking. Without masking, but even with continued so-
cial distancing in place once the lockdown is lifted, the
infection rate will increase and almost half of the popu-
lation will become affected. This scenario, rendered in
grey in Figure 1, would potentially lead to over a mil-
lion deaths in a population the size of the UK. A contin-
ued lockdown, illustrated in blue colour, does eventually
result in bringing the disease under control after around
6 months. However, the economic and social costs of a
“full body lockdown” will be enormous, which strongly
supports finding an alternative solution.
In the model, social distancing and masking at both
50% and 80% of the populationbut no lockdown beyond
the end of Mayresult in substantial reduction of infec-
tion, with 80% masking eventually eliminating the dis-
ease. Figure 2 shows the simulation results for a repre-
sentative scenario: universal masking at 80% adoption
(red) results in 60,000 deaths, compared to maintaining
a strict lockdown (blue) which results in 180,000 deaths.
Masking at only a 50% adoption rate (orange) is not suf-
ficient to prevent continued spread and eventually results
in 240,000 deaths. Replacing the strict lockdown with
social distancing on May 31 without masking results in
unchecked spread.
4 Agent based modelling of univer-
sal masking impact
In the second of our two new theoretical models, we
employed stochastic individual agent based modelling
(ABM) as an alternative Monte Carlo simulation tech-
nique for understanding the impact of universal masking.
Agent based models have roots in various disciplines. A
stochastic agent program can be defined as a agent func-
tion f:pPr(a)which maps possible percept vec-
tors to a probabilistic distribution over possible actions
(or to states that influence subsequent actions). In AI,
Russell and Norvig (2009) summarise five classes of in-
telligent agents: simple reflex agents, model-based reflex
agents, goal-based agents, utility-based agents, and learn-
ing agents; note, however, that agents may also be sus-
6
ceptible to imperceptible environmental factors such as
viruses. Holland and Miller (1991) discuss artificial adap-
tive agents for modeling complex systems in economics.
Bonabeau (2002) surveys agent based models for simulat-
ing human systems.
As in other disciplines, ABM approaches in epidemiol-
ogy (see, e.g., Hunter et al. (2017). Tracy et al. (2018),
or Hunter et al. (2018)) have several advantages com-
pared to compartmental models which group undifferen-
tiated individuals into large aggregates (like in the above
SEIR simulation). First, because the behavior and char-
acteristics of each agent is independent, they can simulate
complex dynamic systems with less oversimplification of
rich variation among individuals. Second, because agents
can be simulated in physical two- or three-dimensional
spaces, they can better simulate the geometry of contact
between individuals, which is highly relevant in epidemi-
ology. Third, the randomization on each run makes the
statistical variance more apparent than in the SIR fam-
ily of models, whose smooth curves often misleadingly
convey more certainty than warranted. Fourth, ABMs
lend themselves well to visualization, as seen in Figure 5,
which helps convey the non-linear behavior of complex
dynamic systemsan especially relevant advantage when
the exponential effect of masking can be counterintuitive
in many cultures due to pre-existing cultural biases (Le-
ung, 2020) and unconscious cognitive biases (De Kai,
2020).
4.1 Mask characteristics
The ABM approach allows us to put masks on individual
agents and to assign properties to those masks, to shed
light on the question of how face maskseven nonmedical
cloth maskscarry the promise to be so surprisingly effec-
tive. The objective is to examine how even a small barrier
to individual infection transmission can multiply into a
substantial effect on the level of communities and popula-
tions.
Face masks work in two ways: They can protect an
infected person from spreading the virus (transmission),
and they can limit how much the non-infected individual
is exposed to the virus (absorption). Traditionally, masks
are worn to protect the wearer from being infected by an
ill person when in close and prolonged contact. In such
classic situations, for instance in hospitals and elderly
homes, only medical masks combined with other protec-
tive equipment provide protection. Comparing different
mask materials, medical masks have been found to be
up to three times more effective in blocking transmission
compared to homemade masks (Davies et al., 2013). Sur-
gical masks most efficaciously reduce the emission of in-
fluenza virus particles into the environment in respiratory
droplets. Still, although masks vary greatly in their abil-
ity to protect, using any type of face mask (without an
exploratory valve) can help decrease viral transmission
(Sande et al., 2008).
However, the effect of universal masking does not re-
quire full protection from disease to be effective in low-
ering infection rates of COVID-19. Masks may be es-
pecially crucial for containing the COVID-19 pandemic,
since many infections appear to come from people with
no signs of illness. For instance, around 48% of COVID-
19 transmissions were pre-symptomatic in Singapore and
62% in Tianjin, China (Ganyani et al., 2020). This sug-
gests that masking needs to be universal and not restricted
to individuals who think they may be infected.
Furthermore, the SARS-CoV-2 virus is known to
spread through airborne particles (Leung et al., 2020) and
quite possibly via aerosolised droplets as well according
to Service (2020), van Doremalen et al. (2020), Santarpia
et al. (2020), and Liu et al. (2020). It may linger in the air
for and travel several meters, which is why social distanc-
ing rules require at least 2 meters between individuals to
be effective.
4.2 Experimental model
As a contrastive baseline we employed a compartmental
SEIR model with the same parameters as given for our
SEIR experiments of section 3.
For the new agent based model, we implemented an
environment consisting of a square wraparound two-
dimensional space, within which a population of individ-
ual agents reside in four states: susceptible (S), exposed
(E), infectious (I) and recovered (R). The wraparound
space means that agents who move outside a border re-
enter the square from the opposite side. As in our SEIR
models, the initial infected population (initi) was set to
1%, and all others to 0%. The size of the total population
was set to 200, but the wraparound feature of the two-
dimensional space in effect represents arbitrarily larger
7
Figure 3: Three successive randomised runs of the agent based model for 300 days, with no mask wearing. Blue is sus-
ceptible, orange is exposed, red is infected, and green is recovered. The contrastive SEIR baseline model’s predicted
curves are shown in thinner, fainter lines. The ABM runs produce curves with a fine granularity of randomisation,
centering on average around the ODE based SEIR curves.
spaces that are approximated by replicated square tiles,
thus giving more accurate dynamics without boundary ef-
fects from small spaces.
To best fit the same empirical characteristics of
COVID-19 spread as our SEIR models, we again set σ=
1/5.2and γ= 1/12.39. Note that βis inapplicable in
the ABM since infection transmission between individu-
als arises from physical proximity, which is more realistic
than randomly infecting other individuals anywhere with
some probability βwith no regard to their physical lo-
cation. In the baseline Monte Carlo simulation, agents
decide on a random destination location within a parame-
terised radius of their current point, then proceed at a pa-
rameterised speed to move there, and then repeat the pro-
cess iteratively. We adjusted such ABM-specific parame-
ters, as well as physical exposure distance, to optimise fit
to the baseline SEIR model curves, assuming none of the
population to be wearing masks. Again, this was done so
as to best approximate known COVID-19 dynamics.
ABM runs were for 300 days from the onset of the out-
break since empirically, the emergent SEIR curves sta-
bilise before the 300th day.
To model the impact of masking, the following mask-
ing parameters can be varied:
Mask wearing. Gradual increases (or decreases) in
mask wearing can be modelled using parameterised rates
of masking M(or unmasking U) in the proportion of un-
masked (or masked) individuals. The parameters mmin
and mmax also allow modelling the minimum and maxi-
mum absolute numbers of masked agents. These masking
parameters can be dynamically adjusted any time during
any ABM run, to simulate varying policy decisions and
cultural mindset shifts.
Mask characteristics. Varying degrees of mask effec-
tiveness are modelled by the mask transmission rate T
and mask absorption rate A, which denote the proportion
of viruses that are stopped by the mask during exhaling
(transmission) versus inhaling (absorption), respectively.
We set T= 0.7and A= 0.7to model the use of inexpen-
sive, widely available, and even nonmedical or homemade
8
Figure 4: Four ABM runs under varying masking scenarios. (a) 100% of the population wearing masks from the onset
of the outbreak, with excellent suppression of infection spread. (b) 0% of the population initially wearing masks, but
instituting near universal masking of 90% of the population at day 50, still with significant suppression of infection
spread. (c) 0% of the population initially wearing masks, and instituting some masking of 50% of the population at
day 50, with not much impact on infection spread. (d) 0% of the population initially wearing masks, but instituting
near universal masking of 90% of the population at day 75 with not much impact on infection spread.
9
masks with only 70% effectiveness for universal masking,
and not higher quality N95, N99, N100, FFP1, FFP2, or
FFP3 masks which in many regions need to be reserved
for medical staff.
4.3 Experimental results
ABM simulation shows that universal masking can signif-
icantly reduce virus spread if adopted sufficiently early,
even if the masks are nonmedical or homemade.
Figure 3 shows three successive runs for the baseline
m= 0 case with zero mask adoption. Each dot (which is
in motion during simulation runs) represents an individ-
ual agent, who may become exposed to the virus through
proximity to other agents who are infectious. Blue dots
are healthy susceptible agents, orange dots are exposed
agents, red dots are infected agents, and green dots are
recovered agents. A dot with a white rectangle on it rep-
resents an agent who is wearing a mask.
The three baseline ABM runs show how chance plays
a significant role in the dynamics of virus spread. Since
each simulation run is randomised, to decrease variance
requires observation over multiple runs. On average, the
baseline case with zero mask adoption adheres to the sim-
pler SEIR model’s predicted curves.
Figure 4 compares typical runs for four scenarios
that simulate how COVID-19 spreads among individual
agents under different masking scenarios, with the con-
trastive baseline SEIR model curves shown in thin lines as
a reference: (a) m0= 100% meaning that the entire pop-
ulation adopts mask at the onset of the outbreak on day
0; (b) m0= 0%, m50 = 90% meaning that none of the
population is wearing masks at the onset but that nearly
universal masking is instituted on day 50; and (c) m0=
0%,m50 = 50% meaning that none of the population is
wearing masks at the onset but that half of the population
adopts masks on day 50, and (d) m0= 0%, m75 = 90%
meaning that none of the population is wearing masks at
the onset but that nearly universal masking is instituted on
day 75.
In scenario (a), a dramatic decrease in the number of
infections is evident as a result of universal masking at the
onset of the outbreak. Unfortunately, most regions outside
East Asia missed the time window for scenario (a).
In scenario (b), even though the population is not ini-
tially wearing masks, if universal masking is instituted by
day 50, good chances of dramatic suppression of infec-
tion rates can still be achieved. Fortunately, this option is
within reach of most regions at the time of writing.
In scenario (c), again the population is not initially
wearing masks. On day 50, half the population dons
masks, but unlike scenario (b) which succeeds with 90%
universal masking, unfortunately 50% is an insufficient
level of mask adoption to suppress infection rates to a sig-
nificant degree.
In scenario (d), the population again is not initially
wearing masks, but unlike scenario (b) the 90% univer-
sal masking is not instituted until day 75, instead of day
50. Waiting too long unfortunately greatly decreases the
degree to which infection rates can be suppressed.
To help policy makers and the general public gain a
more concrete feel for how masks impact the dynamics
of virus spread, we have made available online3an inter-
active visualisation tool for the ABM simulation model,
as shown in Figure 5. The default view allows direct ad-
justment in real time of the percentage of masked individ-
ual agents through a slider control. Optional advanced
controls allow playing with various scenarios: whether
masking is used, the adoption rate of masking, virus trans-
mission and absorption rates through masks of varying
quality, as well as other modelling parameters such as the
initial numbers of susceptible, exposed, infected, or re-
covered agents, and the contrastive baseline SEIR model
parameters.
5 Evaluation of model predictions
against empirical data on univer-
sal masking impact
For validation of the foregoing SEIR and ABM predic-
tive models it is necessary to compare against what little
historical macro scale empirical data is available, since
precise numbers are not yet known for masking rates,
mask transmission and absorption rates, and infectious
but asymptomatic cases.
3http://dek.ai/masks4all
10
Figure 5: Interactive visualisation tool for the ABM simulation model to help policy makers and the general public gain
a more concrete feel for how masks impact the dynamics of virus spread, available online at http://dek.ai/masks4all.
5.1 Validation data set
We collected a new data set describing the degree of suc-
cess in managing COVID-19 by countries or regions seg-
mented by the prevalence or enforcement of universal
masking. The data set covers (a) a selection of 38 coun-
tries or provinces in Asia, Europe and North America that
have similar, high levels of economic development (based
on World Bank GDP purchasing power parity per capita),
(b) detected COVID-19 cases from Jan 23 to April 10,
2020, and (c) characteristics of universal masking culture
and/or universal masking orders or recommendations by
governments.
5.2 Feature extraction
From our data set’s 38 selected countries, we computed
(a) the daily growth of confirmed cases, as well as (b)
reduction from peak of new cases. Sorted in increasing
order of the daily growth, Figure 6 presents these figures
alongside features extracted from our data set denoting
each country or region’s (c) masking culture, (d) univer-
sal masking policy, and (c) lockdown policy. Additional
clarification on definitions of a couple of these features
follow.
Masking culture is defined as an established prac-
tice by a significant section of the general population to
wear face masks prior to the start of the Covid-19 pan-
demic. A cursory review of the scientific literature and
the general press has identified Japan, Thailand, Vietnam
(Burgess and Horii, 2012), China’s urban centers (Kuo,
2014), Hong Kong (Cowling et al., 2020), Taiwan, Sin-
gapore and South Korea (Yang (2014), Jennings (2020))
as countries with such a consistent practice, at least in the
decade predating the Covid-19 pandemic. Nevertheless,
the notion of ”culture” should not imply that the prac-
tice of face mask wearing has been extensive and consis-
tent throughout time. For example, though this practice
11
Figure 6: Epidemic daily growth and reduction from
peak daily growth, together with masking culture, uni-
versal masking policy, and lockdown policy, from Jan-
uary 23 to April 10, 2020 for selected list of countries or
provinces with high GDP PPP per capita in Asia, Europe
and North America. Universal masking was employed in
every region that handled COVID-19 well. Sources: John
Hopkins, Wikipedia, VOA News, Quartz, Straits Times,
South China Morning Post, ABCNews, Time.com, Chan-
nel New Asia, Moh.gov.sg, Reuters, Financial Times,
Yna.co.kr, Nippon.com, Euronews, Spectator.sme.sk
may have fit with preexisting Taoist and health precepts
of Chinese traditional medicine, its actual emergence may
be relatively recent, starting with the industrialization of
Japan at the start of the XXth century and both the flu
pandemics of the XXth century as well as the rise of par-
ticle pollution (Yang, 2014). The rest of the above-listed
east Asian countries has followed the same course in the
second half of the XXth century, including China as it
was confronting a severe particle pollution crisis in the
first part of the 2010s (Kuo (2014), Li (2014), Hansstein
and Echegaray (2018)). Beyond price, availability and
government recommendation, the actual practice of mask-
ing in the Asian general population may be mediated by
factors such as social norms or peer-pressure, perception
of one’s competence, past behaviors or perception of the
danger (Hansstein and Echegaray, 2018). As an exam-
ple of the latter, in Hong Kong, masking was practiced by
79% of the general population during the 2003 SARS out-
break, but by only a maximum of 10% of the general pop-
ulation during the Influenza A pandemic in 2009 (Cowl-
ing et al., 2020).
Universal masking policy. Additionally, to the extent
that government recommendations or mandatory orders
may shape perceptions and assist in masks availability, it
may amplify the masking practice in the general popula-
tion. It can thus be assumed that the maximum potency
of universal masking in the context of epidemics may be
reached when a government issues a mandatory or highly
recommended order to the general population, issued at
an early date, supported by the availability of face masks
and amplified by a pre-existing ”masking culture”. In that
case, we make the reasonable assumption that such na-
tional situations may be used to validate our SEIR and
ABM predictive models at maximum values (80-90%) for
the percentage of the general population wearing masks.
We also computed two additional meta-features to clas-
sify successful management of the epidemic outbreak.
These meta-features help to highlight both (a) success in
suppressing growth from the start (e.g., Hong Kong or
Taiwan) or (b) success in managing the epidemic by re-
ducing the number of new cases after a peak (e.g., South
Korea).
Successful suppression of daily growth is defined as
being below 12.5% daily growth (equivalent to number of
cases doubling at the slower pace of 6 days or more) once
the number of detected cases first reached 30. These daily
12
Figure 7: Daily growth curves showing the impact of universal masking on epidemic control: epidemic trajectory after
30 detected cases in universal masking selected countries and provinces (green) vs. others (grey). Masking is nearly
perfectly correlated with lower daily growth or strong reduction from peak growth of COVID-19. Sources: John
Hopkins, Wikipedia, VOA News, Quartz, Straits Times, South China Morning Post, ABCNews, Time.com, Channel
New Asia, Moh.gov.sg, Reuters, Financial Times, Yna.co.kr, Nippon.com, Euronews, Spectator.sme.sk
growth rates are highlighted in green in Figure 6.
Successful reduction from peak is defined as a re-
cent, significant (>60%) reduction of new cases calcu-
lated as the average of the last five days before April 10,
2020 compared to the average of the three highest num-
ber of daily new cases up to April 10, 2020 starting from
the date when the number of detected cases first reached
30. Again, these reductions from peak are highlighted in
green in Figure 6.
5.3 Validation results
Results bear out the predictions made by our SEIR and
agent-based models as described in sections 3 and 4.
In Figure 6, the green (successful supression of daily
growth and/or reduction from peak) areas show that as
of April 10, 2020, an overwhelming majority of coun-
tries or regions that have best managed COVID-19 out-
breaks were countries or regions with either (1) estab-
lished universal masking cultures or (2) mandatory or-
ders or government recommendations supported by sig-
nificant and early mask production destined for the gen-
eral population. These countries or regions include Tai-
wan, South Korea, Singapore, Japan, autonomous special
administrative regions such as Hong Kong or Macau, and
Chinese provinces such as Beijing, Shanghai, or Guang-
dong. In effect, masking in public has been required in
Taiwan, metropolitan areas in China such as Shanghai
and Beijing (as well as Guangzhou, Shenzhen, Tianjin,
Hangzhou, and Chengdu), Japan, South Korea, and other
countries (Morgunov et al., 2020). On the other hand,
the red (strict lockdown without universal masking) ar-
eas show that most of the countries which have adopted
mass testing, tracking and quarantining, but lack a univer-
sal masking culture and clear recommendations and avail-
ability for universal masking, have not achieved an equiv-
13
alent level of COVID-19 epidemic control as of April 10,
2020. This nearly perfect correlation between early uni-
versal masking and successful management of COVID-19
outbreaks bears out our SEIR and ABM predictions.
In Figure 7, daily growth curves were extracted from
our data set in order to reveal the impact of universal
masking on epidemic control on a time axis. Results show
that universal masking is nearly perfectly correlated with
lower daily growth rates of COVID-19 cases over time,
again validating the predictions from our SEIR and agent
based models.
In Figure 8, daily growth was plotted against versus
percentage reduction from peak daily daily growth. Green
points, representing countries or regions with early uni-
versal masking, disproportionately fall within the two
lower quadrants which represent successful management
of COVID-19 outbreaks. Red points, representing coun-
tries with strict lockdowns but not universal masking,
nearly all fall in the two upper quadrants which repre-
sent less successful management of COVID-19 outbreaks.
Light green points, representing countries or regions with
late universal masking, tend to fall in the middle regions.
Again, the strong correlation of universal masking with
successful control of COVID-19 case growth bears out
our SEIR and agent based models’ predictions.
Validation of the need for universal masking. These
validations highlight the gradual nature of the protection
against COVID-19 achieved with a higher fraction of the
population practicing masking, as observed in the SEIR
and ABM simulations when comparing situations with
80-90% universal masking versus only 50% masking or
none. In countries or provinces with masking culture
and universal masking orders or recommendations be-
fore March 15, 2020, the average daily growth was 5.9%
and the reduction from peak was 74.6%. In the coun-
tries without masking culture and universal masking or-
ders or recommendations after March 15, 2020, the aver-
age daily growth was 14.2% and the reduction from peak
was 45.8%. Finally, for the rest of the other countries, the
average daily growth was 17.2% and the reduction from
peak was 37.4%, the lowest results of the sample. The
latter group includes countries that have gone into ”strict
lockdown” (or mass home quarantine) for 20 out of 27
countries (74%). This is much higher than for the inter-
mediate group of countries without masking culture and
”late” universal masking orders (2 out 4, or 50% of the
sample), or the first group of countries and provinces with
masking culture and ”early” universal masking orders. In
that first group, no countries or provinces had to endure
”strict lockdown”.
Validation of the need for early universal masking.
Yet even within this first group, the strength of early uni-
versal masking recommendations from the government
may impact the proportion of the general population actu-
ally wearing masks and thus the level of epidemic control,
as per our models’ SEIR and ABM predictions. For exam-
ple, Singapore initially encouraged people to wear masks
only when feeling unwell. Then, on April, 5, the govern-
ment changed policy and decided to distribute reusable
face masks to all households (Cheong, 2020). On the
other end, Hong Kong decided by January 24, 2020 to
advise the general population to wear surgical masks in
crowded places and public transports (Hong Kong Depart-
ment of Health, 2020). As can be observed from Figure 6,
as of April 10, 2020, the characteristics for epidemic con-
trol in terms of daily growth and peak from reduction are
better for Hong Kong than for Singapore. These varia-
tions may be related to levels of adherence to masking
by the general population. Though there are no available
data as of April 10, 2020 as per adherence to universal
masking in Singapore, telephone surveys in Hong Kong
done in February 11-14, 2020 and then in March 10-13,
2020, both after Department of Health public advice, have
shown declared masking adherence at the very high levels
of 97.5% and 98.8% respectively when going out (Cowl-
ing et al., 2020). Assuming the adherence level to mask-
ing was lower in Singapore since the general population
order came much later, this would support our SEIR and
ABM predictions of the need for early institution of uni-
versal masking.
Although these correlations may also be sensitive to
other unobserved factors, the theoretical SEIR and ABM
predictions as empirically validated in the various ways
described here call for urgent policy and public action
even as further enquiry is pursued into the effects of mask-
ing. Our results also confirm and amplify other previ-
ous findings. A recent macro-level regression analysis by
economists at Yale University, taking into account mask-
ing cultures and times of country COVID-19 policy re-
sponses, estimated that growth of COVID-19 rates only
half that of mask wearing countriesthe growth rate of
confirmed cases is 18% in countries with no pre-existing
14
Figure 8: Visual representation of epidemic daily growth versus percentage reduction from peak daily daily growth
in quadrants showing the impact of universal masking on epidemic control: and reduction from peak, from January
23 to April 10, 2020 for selected list of countries or provinces with high GDP PPP per capita in Asia, Europe and
North America. Masking is nearly perfectly correlated with lower daily growth or strong reduction from peak growth
of COVID-19. Sources: John Hopkins, Wikipedia, VOA News, Quartz, Straits Times, South China Morning Post,
ABCNews, Time.com, Channel New Asia, Moh.gov.sg, Reuters, Financial Times, Yna.co.kr, Nippon.com, Euronews,
Spectator.sme.sk
mask norms and 10% in countries with such norms, while
the growth rate of deaths is 21% in countries with no mask
norms and 11% in countries with such norms. The authors
note that such a 10% reduction in transmission probabili-
ties could correspond to a per capita gain of $3,000-6,000
per each additional cloth mask, and that the economic
benefits of each medical mask for healthcare personnel
could be substantially larger (Abaluck et al., 2020).
6 Conclusion: Universal masking
needs broad support and clear
guidelines
Our SEIR and ABM models suggests a substantial im-
pact of timely universal masking. Without masking, but
even with continued social distancing in place once the
lockdown is lifted, the infection rate will increase and al-
most half of the population will become affected. This
scenario would potentially lead to over a million deaths
in a population the size of the UK. Social distancing and
15
masking at both 50% and 80-90% of the populationbut
no lockdown beyond the end of Mayresult in substantial
reduction of infection, with 80-90% masking eventually
eliminating the disease.
Moreover, for a significant chance of mitigating in-
fection growth rates, universal masking must be adopted
earlyby day 50 from the onset of COVID-19 outbreaks.
Without masking, lifting lockdown after nine weeks
while keeping social distancing measures will risk a major
second wave of the epidemic in 4-5 months’ time. How-
ever, if four out of five citizens start wearing cloth masks
in public before the lockdown is lifted, the number of new
COVID-19 cases could decline enough to exit lockdown
and still avoid a second wave of the epidemic. If only ev-
ery second person starts wearing a mask, infection rates
would also decline substantially, but likely not by enough
to prevent the second wave.
Combined with the correlational empirical evidence,
our results highlight the need for mass masking as an al-
ternative to a continued lockdown scenario. For this strat-
egy to be most effective, the vast majority of the popula-
tion needs to adopt mask wearing immediately. When a
well-timed “mouth-and-nose lockdown” accompanies the
current “full body lockdown”, both the human and eco-
nomic costs of the COVID-19 pandemic can be signifi-
cantly lowered.
Our theoretical and empirical results are in line with
previous studies suggesting that a high rate of masking
may be needed in a population to provide efficient pro-
tection from influenza (Yan et al., 2019) and that masking
can be an effective intervention strategy in reducing the
spread of a pandemic (Tracht et al., 2010).
Furthermore, universal masking can reduce stigmatiza-
tion of ethnic groups, risk groups, or the sick and con-
tribute to public solidarity (Feng et al., 2020).
We urge governments and international bodies who
have not yet done so to consider masking as one of the key
tools in population policy after the COVID-19 lockdowns
and until the virus is under control. The analysis presented
here supports recent studies (Abaluck et al., 2020), sug-
gesting that the effectiveness of universal masking is com-
parable to that of social distancing or a societal lockdown
with closed workplaces, schools, and public spaces and
limited geographical mobility. The results from our sim-
ulation help explain the dynamics behind the perplexing
advantage in the Asian experience of tackling COVID-19
compared to the situation elsewhere.
Our analyses lead to the following key policy recom-
mendations:
1. Masking should be mandatory or strongly recom-
mended for the general public when in public trans-
port and public spaces, for the duration of the pan-
demic.
2. Masking should be mandatory for individuals in es-
sential functions (health care workers, social and
family workers, the police and the military, the ser-
vice sector, construction workers, etc) and medical
masks and gloves or equally safe protection should
be provided to them by employers. Cloth masks
should be used if medical masks are unavailable.
3. Countries should aim to eventually secure mass
production and availability of appropriate medical
masks (without exploratory valves) for the entire
population during the pandemic.
4. Until supplies are sufficient, members of the general
public should wear nonmedical fabric face masks
when going out in public and medical masks should
be reserved for essential functions.
5. The authorities should issue masking guidelines to
residents and companies regarding the correct and
optimal ways to make, wear and disinfect masks.
6. The introduction of mandatory masking will benefit
from being rolled out together with campaigns, citi-
zen initiatives, the media, NGOs, and influencers in
order to avoid a public backlash in societies not cul-
turally accustomed to masking. Public awareness is
needed that “masking protects your communitynot
just you”.
The effectiveness of universal masking in a given popu-
lation is likely to depend on (a) the type of masks used, (b)
the acceptance of masking in the population, (c) the level
of contagion of the virus, and (d) what other interven-
tions have been applied. From this perspective, the Cen-
tral European experience will be highly informative, since
it represents the first major shift to universal masking in
a formerly non-masking culture. The effects of this pio-
neering intervention on infection rates and fatalities will
16
appear only in the forthcoming weeks, although Slovakia
and Slovenia are currently showing early indications of
progress (see Figure 7). In any case, they illustrate that a
country with no prior history of mask wearing in public
may rapidly change course, and quickly adopt masks as
a non-stigmatisedeven street smartway to express caring
and solidarity in the community.
The medical and social risks of increased infections
need to be countered by proper advice in the public do-
main. Some studies do indicate negative effects of naive
improper cloth mass use, for instance higher risks of in-
fection due to moisture retention, reuse of poorly washed
cloth masks, and poor filtration in comparison to medi-
cal masks (MacIntyre et al., 2015). To address concerns
that lay individuals may use both medical and/or cloth and
paper masks incorrectly, masking techniques and norms
need to be taught with targeted information to different
demographics, just as proper handwashing and social dis-
tancing techniques have been taught.
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... In Scenarios II and III, with the use of PPE, we did not observe the rapid increase of prevalence since the beginning of reopening; however, in Scenario IV without the protection of PPE, the within-business prevalence started to increase and exceed the population average on day one. This result was consistent with the recent study by Kai et al. [53], which demonstrated the significant effect of universal use of facial masks (e.g., at least 80% population wear masks) on impeding the spread of infections. ...
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The sudden onset of the coronavirus (SARS-CoV-2) pandemic has resulted in tremendous loss of human life and economy in more than 210 countries and territories around the world. While self-protections such as wearing masks, sheltering in place, and quarantine policies and strategies are necessary for containing virus transmission, tens of millions of people in the U.S. have lost their jobs due to the shutdown of businesses. Therefore, how to reopen the economy safely while the virus is still circulating in population has become a problem of significant concern and importance to elected leaders and business executives. In this study, mathematical modeling is employed to quantify the profit generation and the infection risk simultaneously from a business entity's perspective. Specifically, an ordinary differential equation model was developed to characterize disease transmission and infection risk. An algebraic equation is proposed to determine the net profit that a business entity can generate after reopening and take into account the costs associated of several protection/quarantine guidelines. All model parameters were calibrated based on various data and information sources. Sensitivity analyses and case studies were performed to illustrate the use of the model in practice. The results show that with a combination of necessary infection protection measures implemented, a business entity may stand a good opportunity to generate a positive net profit while successfully controlling the within-business infection prevalence under that in the general population. The use of personal protective equipment (PPE) is also found of significant importance, especially at the early stage of business reopening.
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Identification of biomedical and socioeconomic predictors for the number of deaths by COVID-19 among countries will lead to the development of effective intervention. While previous multiple regression studies have identified several predictors, little is known for the effect of mask non-wearing rate on the number of COVID-19-related deaths possibly because the data is available for limited number of countries, which constricts the application of traditional multiple regression approach to screen a large number of potential predictors. In this study, we used the hypothesis-driven regression to test the effect of limited number of predictors based on the hypothesis that the mask non-wearing rate can predict the number of deaths to a large extent together with age and BMI, other relatively independent risk factors for hospitalized patients of COVID-19. The mask non-wearing rate, percentage of age ≥ 80 (male), and male BMI showed Spearman's correlations up to about 0.8, 0.7, and 0.6 with the number of deaths per million from 22 countries from mid-March to mid-June, respectively. The observed number of deaths per million were significantly correlated with the numbers predicted by the lasso regression model including four predictors, age ≥ 80 (male), male BMI, and mask non-wearing rates from mid-March and late April to early May (Pearson's coefficient = 0.918). The multiple linear regression models including the mask non-wearing rates, age, and obesity-related predictors explained up to 79% variation of the number of deaths per million. Furthermore, 56.8% of the variation of mask non-wearing rate in mid-March, the strongest predictor of the number of deaths per million, was predicted by age ≥ 80 (male) and male BMI, suggesting the confounding role of these predictors. Although further verification is needed to identify causes of the national differences in COVID-19 mortality rates, these results highlight the importance of the mask, age, and BMI in predicting the COVID-19-related deaths, providing a useful strategy for future regression analyses that attempt to contribute to the mechanistic understanding of COVID-19.
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Agent-based models are a tool that can be used to better understand the dynamics of an infectious disease outbreak. An infectious disease outbreak is influenced by many factors including vaccination or immunity levels, population density, and the age structure of the population. We hypothesize that these factors along with interactions of factors and the actions of individuals would lead to outbreaks of different size and severity even in two towns that appear similar on paper. Thus, it is necessary to implement a model that is able to capture these interactions and the actions of individuals. Using openly available data we create a data-driven agent-based model to simulate the spread of an airborne infectious disease in an Irish town. Agent-based models have been known to produce results that include the emergence of patterns and behaviours that are not directly programmed into the model. Our model is tested by simulating an outbreak of measles that occurred in Schull, Ireland in 2012. We simulate the same outbreak in 33 different towns and look at the correlations between the model results and the town characteristics (population, area, vaccination rates, age structure) to determine if the results of the model are affected by interactions of those town characteristics and the decisions on the agents in the model. As expected our results show that the outbreaks are not strongly correlated with any of the main characteristics of the towns and thus the model is most likely capturing such interactions and the agent-based model is successful in capturing the differences in the outbreaks.
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Background Wearing a pollution mask is an effective, practical, and economic way to prevent the inhalation of dangerous particulate matter (PM). However, it is not uncommon to observe negligence in adopting such behaviour, and this especially among young segments of the population. Using the Theory of Planned Behaviour (TPB) as conceptual framework, this study explores the role of socio-cognitive factors that affect the decision of wearing a pollution mask in the context of young educated people. This is done by selecting a sample of college students in urban China, a country that has seen air quality as one of the major challenges in the last decades. While young urban college students might be expected to be receptive to standard attempts to be influenced through reason-based cognitive stimuli, it is often found that this is not the case. The empirical analysis was articulated it in two steps. Structural Equation Modelling (SEM) was first used to examine the relationships among the conceptual constructs derived from the TPB conceptual model, and second Step-Wise Ordinary Least Squares Regressions (SWOLS) were employed to observe the partial effect played by each item on the decision to wear a mask. Results Results show that, while reason-based stimuli play a role, attitude, social norm, and self-efficacy were the most important predictors of the behavioural intention (p < 0.01). The role of past behaviour was also acknowledged as strongly associated with the dependent variable (p < 0.01). Overall, the likelihood of wearing a pollution mask increases with the importance of others socio-cognitive and psychological factors, which could help understand behavioural biases, and explain the relative role of several mechanisms behind the decision to wear a mask. Conclusions While tackling pollution requires multiple and synergic approaches, encouraging self-prevention using pollution mask is a simple and effective action, implementable at negligible costs. Resistance among younger, well-educated cohorts to wear masks can be overcome by stressing the social desirability of action and the sense of empowerment derived from its usage. This study has the potential to inform policies aimed at changing suboptimal behavioural attitudes by identifying triggers for change, and it could serve in improving the tailoring of health promotion messages aimed at nudging healthy behaviour. Electronic supplementary material The online version of this article (10.1186/s12992-018-0441-y) contains supplementary material, which is available to authorized users.
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Outbreaks of influenza represent an important health concern worldwide. In many cases, vaccines are only partially successful in reducing the infection rate, and respiratory protective devices (RPDs) are used as a complementary countermeasure. In devising a protection strategy against influenza for a given population, estimates of the level of protection afforded by different RPDs is valuable. In this article, a risk assessment model previously developed in general form was used to estimate the effectiveness of different types of protective equipment in reducing the rate of infection in an influenza outbreak. It was found that a 50% compliance in donning the device resulted in a significant (at least 50% prevalence and 20% cumulative incidence) reduction in risk for fitted and unfitted N95 respirators, high‐filtration surgical masks, and both low‐filtration and high‐filtration pediatric masks. An 80% compliance rate essentially eliminated the influenza outbreak. The results of the present study, as well as the application of the model to related influenza scenarios, are potentially useful to public health officials in decisions involving resource allocation or education strategies.