Download The airborne infectious disease transmission: recent research

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The Spread of Airborne
.e In Infectious Disease
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ae PhD
Christopher Chao,
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Department of Mechanical Engineering
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The Hong Kong University of Science
2
01 12
Technology, China
2
Motivation of the Work
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Infamous outbreak cases in various environments
.s r A
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20 1
12 2
Airborne transmission disease
M. Tuberculosis [International Union Against
Tuberculosis and Lung Disease]
A TB outbreak case in an economy cabin on a flight
from Chicago to Honolulu in April 1994. (Kenyon et al.
1996)
Measles [Department of Health, HKSAR Govt,]
AMOY garden was the most seriously
affected location during the 2003 SARS
outbreak, with over 300 infected people.
(Li et al. 2004)
Guidelines on Airborne Transmission Disease Control
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ASHRAE’s
WHO NATURAL
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recommendation
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• A strategic research agenda has been
developed to address the role of HVAC systems
in the spread of infectious disease;
• The topic is included in ASHRAE’s future
strategic plans;
• Further research should be conducted to
understand how reducing the energy footprint
of buildings will impact infectious disease
transmission;
• Further research should be conducted on
engineering controls to reduce infectious
disease transmission. The document
summarizes the control strategies available and
the occupancy categories in which these
controls can be used. The research priority for
each control is provided. Filtration and UVGI
controls research are given top priority because
less is known about how these controls can be
applied in buildings and HVAC systems to
decrease disease events.
•
•
•
•
For natural ventilation, the following minimum
hourly averaged ventilation rates should be
provided:
160 l/s/patient (hourly average ventilation rate)
for airborne precaution rooms (with a minimum of
80 l/s/patient) (note that this only applies to new
health-care facilities and major renovations);
60 l/s/patient for general wards and outpatient
departments; and
2.5 l/s/m3 for corridors and other transient spaces
without a fixed number of patients; however,
when patient care is undertaken in corridors
during emergency or other situations, the same
ventilation rate requirements for airborne
precaution rooms or general wards will apply.
ASHRAE. 2009. ASHRAE Position Document on Airborne Infectious Diseases.
WHO. 2009. Natural Ventilation for Infection Control in Health-Care Settings.
Formulation of the Problem
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- Expiratory droplets and droplet
nuclei can be airborne carriers for
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various pathogens (e.g. M.
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Tuberculosis, measles, influenza,
ww a
etc).
w. nd
ea In- Epidemiology studies showed that
infectious diseases can be
t.l dthese
o
indoors following the
th transmitted
o
r A air.
.sventilation
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20 1
12 2
[Department of Medical Microbiology,
Edinburgh University]
Inhalation
Pathogen-laden
Aerosols
Infectious Source
Solid Surface
(Fomite)
Susceptible
Epidemiologic Approach
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o
rk
The epidemiology profession
has developed a number of widely accepted steps
h
to investigate tdisease p
tp outbreaks.
la
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Verify n
the diagnosis
Identify the
Disease
w
d
related
to the
existence of the
outbreak
.e outbreak
I
outbreak
at nd
.lt oo
h. r
Prevent
s
A
e
Create a/case definition
to define
e
r
Develop and
aeis included
who/what
as a case
o
implement control
so
r
o
and prevention
s
l
s
o
systems
s2 20
Map the spread of theloutbreak
01 12
2
Study & refine hypothesis
Develop a hypothesis
•
•
•
•
•
•
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or of airborne infectious disease
Study
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Size distribution
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nd
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How the droplets
.e disperse?
I
at nd
oo
What are their fates?
Exhausted?
.lt Deposited?
h. r
se from
Ae the surfaces?
Any chance to re-suspend
/a ro
What is the infection risk? ero so
s
l
srisk?
o
Any method to reduce the infection
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20 1
12 2
•
•
•
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Studies
oron Expiratory Aerosol Size Distribution
ht kp
tpdroplets
la evaporate to nuclei and the diameter may reduce
Expiratory
c
:
/
e size. The smaller nuclei can be
to around half/w
of the initial
an
suspended in air. w
w
d
Collecting media and microscopic
.e In measurement were applied to
atof expiratory
reveal the size distribution
aerosols by numerous
d
o
.ltand Louden
studies, such as Duguid, 1946
h. or and Roberts, 1967.
Afrom
The geometric mean diameter of s
particles
coughing were 12
e
er Roberts. (Nicas et al.
/a and
μm from Duguid and 14 μm from Loudon
o
e
2005)
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ls 20
20 1
12 2
Duguid J.P. 1946. The size and duration of air-carriage of respiratory droplets and droplet-nuclei. J. Hyg, 4, 471–480.
Loudon R.G, and Roberts R.M. 1967. Droplet expulsion from the respiratory tract. Am. Rev. Resp. Dis., 95, 435–442.
Nicas M, Nazaroff W.W, and Hubbard A. 2005. Toward understanding the risk of secondary airborne infection: emission of respiratory
pathogens. Journal of Occupational and Environmental Hygiene, 2:3, 143-154.
W on Expiratory Aerosol Size Distribution
Studies
or
Measured
ht bykpSMPS and particle counter, tidal
tp flowlarate varied from 0.27 to 0.70l/s,
breathing
:// cranged
exhaled volume
ww e a from 0.35 to 1.70l.
nd
w
The range of Cough
was from 1.6-8.5l/s,
.eflow rate
I
nd from 0.25-1.60l.
at varied
Cough expired volume
.lt oo
h. r
PIV measurement on cough svelocity
Aefor 29 volunteers:
e
Maximum velocity of cough at /different
ae ro distances
ro sowith
from mouth ranged from 1.5 to 28.8m/s,
so ls
average of 10.2m/s.
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20 1
2
PIV Average cough velocity was 11.2 m/s. 1
2
Holmgren H, Ljungstrom E, Almstrand A.C, Bake B, and Olin A.C. 2010. Journal of Aerosol Science, 41, 439-446.
Gupta J.K, Lin C.H, and Chen Q. 2009. Indoor Air, 19:517-525.
VanSciver M, Miller S, and Hertzberg J. 2011. Aerosol Science and Technology, 45:415-422.
Zhu S, Kato S, and Yang J.H. 2006. Building and Environment, 41, 1691–1702.
Studies on Expiratory Aerosol Size Distribution
W
or
Methods
Size (μm)
k
Heymann et h
al.t1899
30-500
pl Solid impaction (glass slide with microscope)
t
a
Strauz et al. 1926p
Solid impaction (glass slide with microscope)
70-85
c
:
//w e
Jennision, 1942
High-speed photography
>100
wwSolidanimpaction (glass slide with microscope)
Duguid et al. 1946
100-125
d
Gerone et al. 1966
Solid
impaction, Liquid impaction
<1.0-1.0
.
I
eSolid
nd (paper with microscope)
Loudon et al. 1967
impaction
55.5
a
t.l Opticaloparticle counter
Papineni et al. 1997
<0.6
th (multi-stages
or impactor)
Fennelly et al. 2004
Solid impaction
≦3.3
.
sAPS,
Ae
Yang et al. 2007
SMPS
0.62-15.9
e
Xie et al. 2009
Solid impaction (glass slide/with
ro Dust monitor 50-75
aemicroscope),
Wainwright et al. 2009
Solid impaction (multi-stages
impactor)
≦3.3
so
r
o
Li et al. 2008
Solid impaction (glass slide with microscope),
Dust
monitor
50-100
s
l
s
ol
Morawska et al. 2008
APS
0.1-1.0
2
s2 0
Chao et al. 2009
IMI
4-8
1
01 2 0.4-10.0
Morawska et al. 2009
APS
2
Li et al. 2010
APS, IMI, microscope
>50
Johnson et al. 2011
APS, droplet deposition analysis
0.7->20
Gralton J, Tovey E, McLaws M.L, and Rawlinson W.D. 2011. The role of particle size in aerosolised pathogen transmission: a
review. Journal of Infection, 62, 1-13.
W
oron Expiratory Aerosol Size Distribution
Studies
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• Cough jet w
velocity
ww an
• Size distribution.e d I
nd
a
t.l Imaging,
– Interferometric Mie
APS, Droplet
o
t
o
h
Deposition Analysis .s r A
e
– Evaporation of droplets /a ero
er s
os ol
– Respiratory activities
s
o
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– Origins
20 1
12 2
W Jet Velocity and Size Profile Measurement
Expiration
or
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th or
.s
e/ Ae
ae ro
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20 1
2
Droplets captured on an
1
2 image
PIV (particle
image by IMI
Laser
80
Laser absorption
paper and window
for camera
Camera
Unit: cm
10
10
15
20
13
velocimetry) & IMI
(interferometric Mie
imaging ) Measurements
WJet Velocity Measurements by PIV
or
ht kp
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w. nd
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Nose exhale
r Aexhale
Coughing
Speaking
.s Mouth
e/ e
ae ro
ro so
so ls
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20 1
12 2
Female
Scale for other activities
Scale for coughing
Male
W
or
Coughing
Speaking
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ht p
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20 1
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Size Profile Measured by IMI
Mean number count per person in 50 coughs
80
Mean number count per person after speaking '1-100' for 10 times
40
90
Coughing 1cm
70
Coughing 6cm
60
Error bars show
maximum and minimum
50
40
30
20
10
35
30
25
Speaking 1cm
20
Speaking 6cm
Error bars show maximum
and minimum
15
10
5
0
0
1.5
3
6
12
20
28
36
45
62.5
87.5 112.5 137.5
Size Class [micron]
175
225
375
750
1500
1.5
3
6
12
20
28
36
45
62.5
87.5
112.5
137.5
175
225
375
750
1500
Size Class [micron]
Chao, Wan, Morawska et al. Journal of Aerosol Science, 40, 122-133, 2009
•
•
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or
Exhaled
droplets
size
modes
k
ht p
tp la
c
:
/
Expiratory /droplets
ww e ageneration modes
– Breathing: bronchiolar
w. ndfluid film burst in the respiratory
bronchioles (~0.8 eμm) In
at d
o
– Laryngeal: vibration of.lthe
vocal
th orfolds in the larynx (~0.81.2 μm)
.s
Ae
e
/a therlips
– Oral: large droplets form between
and the
o
so
epiglottis where saliva is presente(~200
ro μm)
so of lthe
s above
Coughing, speaking are combinations
ls 20
20 1
modes
12 2
Johnson et al. Journal of Aerosol Science. 2011.
Morawska L, Johnson G.R, Ristovski Z.D, Hargreaves M, Mengersen K, Corbett S, Chao C.Y.H, Li Y, and
Katoshevski D. 2009. Journal of Aerosol Science, 40, 256-269.
•
•
•
W
or Fate of the Exhaled Aerosols
The
ht kp
tp la
c
:
/
e
/w droplets
The expiratory
may evaporate to half of
an
w
the initial size within
w. adshort time.
I
Small droplets canebe
suspended
in air for a long
at nd
oofloor in a few
.lt on
time. Large droplets settle
h. r
se A
seconds.
/a ero
er and
Size change occurs during transport
deposition.
s
os ol
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s2 0
01 12
2
Fluid Dynamical Properties
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th or
.s
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20 1
12 2
Drag force
The transport of aerosols in air is a
multiphase fluid mechanical process
Gravitational pull
Turbulent fluctuation
Deposition and resuspension of aerosols is
closely related to air turbulence.
Deposit onto
surface
Boundary layer
Surface
As the droplets are carried by the exhaled air, a number
of forces are involved (gravity, buoyancy, diffusion, drag,
etc).
Modeling of the expiratory aerosol transport have
been performed in Eulerian and/or Langrangian
approach.
Eulerian: the properties in terms of time and space at
fixed points in space. E.g. simulation of airflow
Langrangian: following individual particles and
determine the properties. E.g. particle position
Parienta D, Morawska L, Johnson G.R, Ristovski Z.D, Hargreaves M, Mengersen K, Corbett S, Chao C.Y.H, Li Y, and Katoshevski D. 2011. Theoretical analysis
of the motion and evaporation of exhaled respiratory droplets of mixed composition. Journal of Aerosol Science, 42, 1–10.
•
W Dispersion Characteristics
or
Differenthventilation
strategies
k
p
t
la
– Mixing tp
:// ce
– Displacement
ww a
– Downward flow
nd
w
– Under-floor
.e In
d
– Personalized ventilation at
o
.
l
th or
– Neutral ventilation
.s
e/ Ae
ae ro
ro so
so ls
ls 20
20 1
12 2
Mixing
Displacement
ventilation
Isolation room
Return
Stratification level
Downward flow
WHO
Aircraft cabin
Upper
zone
Lower
zone
Supply
W Dispersion Characteristics
or
kp of droplets were studied in different indoor
ht behavior
Dispersion
tp Various
la studies indicate that droplets can transport
environments.
c
:
/
more than 1m. /Different
ww e aventilation configurations, thermal plume
effect, etc., were investigated.
w. nd
ea In of ventilation systems in
Models to predict the performance
t.l do
buildings:
th or
.s
• Analytical / Empirical
e/ Ae
r
a
er os
• Experiment
Experimental
ossmoke,
olparticle
– Tracer gas,
• Small-scale/ Full-scale
s
o
20
– bacterium-laden
ls aerosol
20 1
Numerical
• Numerical Simulation
2
1
• Multi-zone/ Zonal/ CFD
2
– Multi-phase, discrete phase
Zhao, Zhang, and Li. 2005. Building and Environment, 40,1032-1039.
Chao and Wan. 2006. Indoor Air, 16, 296-312
Chao, Wan and Sze-To. 2008. Aerosol Science and Technology, 42, 377-394
Mui, Wong, Wu and Lai. 2009. Journal of Hazardous Materials, 167, 736-744.
Qian, Li, Nielsen, and Hyldgaard. 2008. Building and Environment, 43,344-354.
He, Niu and Gao. 2011. Building and Environment, 46, 397-408.
Lai and Wong. 2011. Aerosol Science and Technology, 45, 909-917.
WDroplet Dispersion Measurements
o
rkMie Imaging (IMI)
Interferometric
Aerosol
h
method t
spectrometer
tp pla
:// ce
ww a
w. nd
ea In
t.l do
th or
.s
e/ Ae
ae ro
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20 1
12 2
Method
 IMI
Instrument
LaVision
SizingMaster
 Aerosol
Spectrometer
GRIMM Labortechnik
Model 1.108
Specifications
Measurable size range: 2m
(Correspond to 5.1m initial
droplet size for 6vol%)
Frequency: 10Hz
Measurable size range: 0.3 20m in 16 size channels
(Correspond to 0.765 - 51m
initial droplet size for 6vol%)
Frequency: 1Hz
Measurements with the
aerosol spectrometer
Chao and Wan, Indoor Air, 2006; Wan and Chao, AS&T, 2008
Generation of Simulated Expiratory
W
Droplets o
Droplet
generator
head
ht rkp
tp la
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ww a
w. nd
ea In
d
t
oo
Non-volatile content of saliva .lt
h. r
se A
/a ero
er s
os ol
ol s 2
s2 0
01 12
2
Species
Na+
K+
ClLactate
Glycoprotein
Molecular
weight /
Atomic
mass
23g
39.1g
35.5g
89g
N.A.
Molar
concentration/
L water
918mM
6011mM
10217mM
4417mM
Estimated
mass
concentration
/L water
2.10.2g
2.30.4g
3.60.6g
3.61.5g
7618g
[Nicas et al., J. Occup. Environ. Hyg., 2: 143-154, 2005]
Solutes
Molecular
Weight
Concentration
NaCl (salt)
58.5 g/mol
12.0 g/L
Glycerin
92.09
g/mol
76.0 g/L
Recipe of ‘simulated saliva’ solution
1 second of puff release is used to
simulate a cough
Regulators and
gauges
Flowcontroller
Droplet generator setup
Computational Fluid Dynamics (CFD) Modeling
W
o
Carrier phase (Air) -rEulerian
ht kp
tp la
Conservation law
:// ce

(  )  div (   w
 grad )  S
t
ww an
d
Species transport (water vapor):.
I
e
nd
a

m    (  um )    J
t.l o
t
t
o
h
Turbulence closure:
.sDropletrevaporation
A
e
RNG k- model.
/a ero
e C ) s
dn
 c(C r
os ol
Discrete phase (droplets or droplet
dt
s
nuclei) - Lagrangian
cD
o
Nu 
 2.0 l0.6 Re Sc
2
s
D
0
du
f
2
1
u  u   g  F

0
Heat
transfer
due
to
evaporation
dt

12 2
Droplets (Discrete)
- Movement driven by the
continuum phase and its
own body forces
i
i
i
i
Droplet-Air interface
- Momentum exchange
- Energy (heat transfer) and
mass exchange (evaporation)
i
Droplet-droplet
interactions (coagulations)
i
i
i
Air (continuum)
- Flow (driven by ventilation and temperature gradient)
- Transport of water vapor
Surface vapor concentration
[Chao & Wan, Indoor Air, 2006]
Bulk air vapor concentration

s
p
AB
p
p
1
3
1
2
m
D
p
i
p
where
f D (Re p )  1  0.15 Re 0p.687
g – gravitational acceleration
Fi – Thermophoretic force, Brownian diffusion
mpc p
Nu 
dTp
dt
 HDp (T  Tp ) 
HD p
 eff
dm p
dt
1
2
 2.0  0.6 Re p Pr
H fg
1
3
[Ranz and Marshall,
1952]
Modeling Approach
W
or
ht kofp turbulent motions
Stochastic tracking
t
linstantaneous
a
Carrierpphase
u  u  u'
c
:/velocity
/w e a The fluctuating part
ww n
d
.e In u'   u' and u'  2k
at d
3
oo  is a random number sampled from a
.lt Where
h. Gaussian
pdf with zero mean, unit variance
r
se A
e
Near
wall correction
for
/
r
a
os
turbulence
er anisotropy
o
o
ls f  (1  e )
u '  f  us
'
olwhere
20
3 s2
) k
1
define k '  u '  (10 e
2
12 2
2Le
2
2
particle flow path
xp1
d(t1)
up0
xp0 + ut1
xp0 u
Fluid flow path
Time = 0
Time = t1
The particle under the influence of the same eddy
until
1)
t > Te (eddy lifetime)
2)
d(t) > Le (eddy length scale),
0.02 y 
2
v
v
2
time needed = Tcross (eddy crossing time)
0.02 y  2

u '2  v'2  w'2  (1  e 0.02 y )
2
k
3
For y+  80
[ Crowe et al., 1998; Graham, 1996 ; Lu, 1995;
[Gosman
Wang &&James,
Ioannides,
19991981]
]
W
Dispersion
Study in a Hospital ward
or
ht kp
tp la
Floor area = 39m
c
://
Ceiling – mixing type ventilation
ww e a
Room conditions: 21.5 C, 60%RH
w. nd
Experimental setup
ea In
t.l do
th or
.s
e/ Ae
ae ro
ro so
so ls
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20 1
12 2
2
E
o
S
E
S
(75W)
(100W)
z
S
y
E
x
(75W)
(100W)
z
(75W) y
x
(100W)
Manikins with heating wire
(Stripped for demonstration)
Heat boxes
W Airflow Pattern
or
ht kp
tp la
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w. nd
ea In
t.l do
th or
.s
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PIV
20
lsmeasurement
20 1
12 2
Numerical simulation
W
Parameters
or
ht kp
tp orientations
la
Two ‘coughing’
:// ce
ww a
w. nd
ea In
t.l do
th or
.s
e/ Ae
aeLateralrinjection
Vertical injection
o
ro so
so ls
ls 20
Two supply airflow rates
20 1
2
1
- 1060 m /hr (11.6 ACH)
2
Exhaust
Exhaust
Exhaust
Exhaust
x
x
5.9m
3
- 550 m3/hr (6.0 ACH)
5.9m
W motion
Vertical
or
Droplet initial
ht sizekp45m
tp la
:// ce
ww a
w. nd
ea In
t.l do
th or
.s
e/ Ae
ae ro
ro so
so ls
ls 20
20 1
12 2
Mean vertical positions very similar in both supply
airflow rates. Due to mixing effect of thermal plumes
2
Mean vertical position, z [m]
1.5 micron, exp, 11.6 ACH
12 micron, exp, 11.6 ACH
1.5 micron, num, 11.6 ACH
1.5 micron, num, 6.0 ACH
12 micron, num, 11.6 ACH
12 micron, num, 6.0 ACH
Vertical injection
1.8
1.6
1.4
1.2
1
0.8
2.2
Initial push by
the cough jet
0.6
0.4
0.2
1.8
1.6
1.4
Droplets stayed low
1.2
1
0.8
0.6
0.4
0.2
0
0
1
10
100
1000
1
Time after the 'cough', t [s]
2
1.8
1.6
1.4
1.2
1
0.8
0.6
2.2
28 micron, exp, 11.6 ACH
45 micron, exp, 11.6 ACH
28 micron, num, 11.6 ACH
28 micron, num, 6.0 ACH
45 micron, num, 11.6 ACH
45 micron, num, 6.0 ACH
Vertical injection
10
100
Time after the 'cough', t [s]
28 micron, exp, 11.6 ACH
45 micron, exp, 11.6 ACH
28 micron, num, 11.6 ACH
28 micron, num, 6.0 ACH
45 micron, num, 11.6 ACH
45 micron, num, 6.0 ACH
2
Mean vertical position, z [m]
2.2
Mean vertical position, z [m]
Lateral injection
1.5 micron, exp, 11.6 ACH
12 micron, exp, 11.6 ACH
1.5 micron, num, 11.6 ACH
1.5 micron, num, 6.0 ACH
12 micron, num, 11.6 ACH
12 micron, num, 6.0 ACH
2
Mean vertical position, z [m]
2.2
1.8
1.6
1000
Lateral injection
1.4
1.2
1
0.8
0.6
0.4
0.4
0.2
0.2
0
0
1
10
100
Time after the 'cough', t [s]
1000
1
10
100
Time after the 'cough', t [s]
Larger droplets tended to stay lower at the lower supply airflow rate
1000
Mean vertical position, z [m]
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Vertical injection
2.2
87.5 micron, exp, 11.6 ACH
137.5 micron, exp, 11.6 ACH
87.5 micron, num, 11.6 ACH
87.5 micron, num, 6.0 ACH
137.5 micron, num, 11.6ACH
137.5 micron, num, 6.0 ACH
87.5 micron, exp, 11.6 ACH
137.5 micron, exp, 11.6 ACH
87.5 micron, num, 11.6 ACH
87.5 micron, num, 6.0 ACH
137.5 micron, num, 11.6 ACH
137.5 micron, num, 6.0 ACH
2
Mean vertical position, z [m]
2.2
W
or
Vertical
motion
ht kp
tp la
ce
://  87.5m
Droplet initial size
ww a
w. nd
ea In
t.l do
th or
.s
e/ Ae
ae ro
ro so
so ls
ls 20
20 1
2
1
2 due to
Changing the supply airflow rate had insignificant effect on the transport
1.8
1.6
Lateral injection
1.4
1.2
1
0.8
0.6
0.4
0.2
0
1
10
100
1000
Time after the 'cough', t [s]
Short airborne time
the dominance of gravitational settling
1
10
100
Time after the 'cough', t [s]
1000
1.5micron, exp, 11.6 ACH
12 micron, exp, 11.6 ACH
1.5 micron, num, 11.6 ACH
1.5 micron, num, 6.0 ACH
12 micron, num, 11.6 ACH
12 micron, num, 6.0 ACH
1.6
1.4
1.2
1
Lateral dispersion
became slower at
a lower supply
airflow rate
0.8
0.6
0.4
0.2
0
2.2
Lateral injection
2
1.8
1.6
1.4
1.2
1
0.8
1.5 micron, exp, 11.6 ACH
12 micron, exp, 11.6 ACH
1.5 micron, num, 11.6 ACH
1.5 micron, num, 6.0 ACH
12 micron, num, 11.6 ACH
12 micron, num, 6.0 ACH
0.6
Initial push by
the cough jet
0.4
0.2
0
1
2
Mean lateral dispersion distance, |x| [m]
Vertical injection
10
100
Time after the 'cough', t [s]
28 micron, exp, 11.6 ACH
45 micron, exp, 11.6 ACH
28 micron, num, 11.6 ACH
28 micron, num, 6.0 ACH
45 micron, num, 11.6 ACH
45 micron, num, 6.0 ACH
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
1000
Vertical injection
0
10
100
Time after the 'cough', t [s]
10
100
1000
Time after the 'cough', t [s]
0.2
1
1
Mean dispersion distance in x direction, |x| [m]
Mean lateral dispersion distance, |x| [m]
1.8
Mean dispersion distance in x direction, |x| [m]
2
W dispersions
Lateral
or
Droplet initial
ht sizekp45m
tp la
:// ce
ww a
w. nd
ea In
t.l do
th or
.s
e/ Ae
ae ro
ro so
so ls
ls 20
20 1
12 2
1000
2.2
Lateral injection
2
1.8
1.6
1.4
1.2
1
0.8
28 micron, exp, 11.6 ACH
45 micron, exp, 11.6 ACH
28 micron, num, 11.6 ACH
28 micron, num, 6.0 ACH
45 micron, num, 11.6 ACH
45 micron, num, 6.0 ACH
0.6
0.4
0.2
0
1
10
100
Time after the 'cough', t [s]
1000
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
2.2
87.5 micron, exp, 11.6 ACH
137.5 micron, exp, 11.6 ACH
87.5 micron, num, 11.6 ACH
87.5 micron, num, 6.0 ACH
137.5 micron, num, 11.6 ACH
137.5 micron, num, 6.0 ACH
Mean latera dispersion distance, |x| [m]
Mean lateral dispersion distance, |x| [m]
2
W
or Lateral dispersion
ht kp
tp la
ce
://  87.5m
Droplet initial size
ww a
w. nd
ea In
t.l do
th or
.s
e/ Ae
ae ro
ro so
so ls
ls 20
20 airborne
12 time.
•Lateral dispersion was minor due to slow dispersion rate and short
12
2
1.8
1.6
1.4
1.2
1
0.8
87.5 micron, exp, 11.6 ACH
137.5 micron, exp, 11.6 ACH
87.5 micron, num, 11.6 ACH
87.5 micron, num, 6.0 ACH
137.5 micron, num, 11.6 ACH
137.5 mciron, num, 6.0 ACH
0.6
0.4
0.2
0
1
10
100
Time after the 'cough', t [s]
1000
1
10
100
Time after the 'cough', t [s]
•Again, changing the supply airflow rate had insignificant effect due to the
dominance of gravitational settling
1000
W Dispersion in Aircraft Cabin (DTU Study)
Droplet
or
ht kp
tp la
:// ce
ww a
w. nd
ea In ‘Coughing’ point
t.l do
th or
.s
e/ Ae
ae ro
ro so
so ls
ls 20
20 1
12 2
200 L/s
Y
100 L/s
20 L/s
2-4
micron
1s
4-8
micron
1s
8-16
micron
1s
16-32
micron
Concentration Contour
W
or
ht kp
tp la
:// ce
ww a
w. nd
ea In
t.l do
th or
.s
e/ Ae
ae ro
ro so
so ls
ls 20
20 1
12 2
3s
3s
3s
5s
5s
5s
10s
10s
10s
20s
20s
20s
30s
30s
30s
120s
120s
120s
10000
8000
6000
3000
1500
800
250
100
50
0
360s
200000
50000
15000
5000
2000
1000
500
100
50
0
360s
200000
50000
15000
5000
2000
1000
500
100
50
0
360s
50000
25000
10000
5000
2000
1000
250
100
50
1s
100 L/s, Middle Injection
3s
5s
10s
20s
30s
120s
360s
0
No. of Aerosols
/Liter of Air
W Droplet Deposition Measurements
or technique to study
Use of fluorescence
ht ( kp
Polyethylene film
droplet deposition
tp la
:// ce
ww a
w. nd
ea In
t.l do
th or
.s
e/ Ae
ae ro
ro so
so ls
ls 20
20 1
Photospectrometry is employed to determine the
2
amount of fluorescent dye in the solvent so as to
1
2
determine the amount of droplets deposited on the
Thatcher et al. 1996; Lai
and Nazaroff 2005)
Deposition
Surface covered
with detachable film
Simulated expiratory
droplet with
fluorescent dye
Solvent
Photospectrometry
surface under concerned.
Aircraft Cabin (Mock-up of B-767 at Technical University of Denmark )
Droplet Deposition (Aircraft Cabin)
W
o
Deposition byr percentage mass
ht kp
tp la
:// ce
ww a
w. nd
ea In
t.l do
th or
.s
e/ Ae
ae ro
ro so
so ls
ls 20
20 1
12 2
Wan, Sze To, Chao, Fang, Melikov. Aerosol Science and Technology, 43, 322-343, 2009
Sze To, Wan, Chao, Fang, Melikov. Aerosol Science and Technology, 43, 466-485, 2009
Exposure and Infection Risk Assessment for Respiratory
W
Diseases
o
ht rkp
tp la
Air Turbulence
c
://
ww e a
w. nd
ea In
t.l do
th or
.s
e/ Ae
a ro
Generation
Transport
Exposure e
ro so
-Air turbulence plays an important role on both the
so ls
(Respiratory
transport and intake of aerosolized pathogen;
20
lsIntakeDeposition)
20 1
-Any estimated exposure level to aerosolized
12 2
pathogens should be regarded as an expected value
rather than an exact value;
-Exposure and/or risk assessment models should be
able to consider these randomness.
W Existing Infection Risk Model
or
Wells-Riley equation
h kp person inhales a number of infectious droplets or
• When eachtsusceptible
tp a “quantum”,
la 63.2% of the population of susceptible
nuclei equal to
:// c[Wells,
people will be infected.
e 1955]
w
an
• Adopt the steady-state
well-mixed
air assumptions: Infectious
wand
d
particles are distributedw
evenly in
air.
.e In  Iqpt
a
Q
PI t.1l  edo
th or
Modified Wells-Riley Models
.s
e/ Ae
ae ro
 pIq N  e  1 
 s
f Iqt 
r

P  1  exp  
o

P  1  exp   o 
N
 V

so  ls
ls 20
20 1
12 2
Average volume fraction of
room air that is exhaled
breath
Non-steady-state and imperfect mixing
 Nt
I
2
Air change rate
[Gammaitoni and Nucci, 1997 ]
I
Total number of
people in the
premises
[Rudnick and Milton, 2003]
Gammaitoni L, and Nucci M.C. 1997. Using Maple to analyze a model for airborne contagion. MapleTech, 4, 2–5.
Rudnick S.N, and Milton D.K. 2003. Risk of indoor airborne infection transmission estimated from carbon dioxide concentration. Indoor Air, 13, 237–245.
Dose-Response Model
W
o
Dose-response type infection
rk risk assessment models require experimentally obtained
h
infectious dose data
pl the dose-response relationship.
ttpto construct
a
:// ce
Exponential model:
ww a P  1  exp  rN 
w. nd
ea In  N 
Beta-Poisson model:
t.Pl  1 d1o  
th or
.
se A
Dose-response model for airborne disease transmission
/a ero
er s
os ol


P ( x, t )  1  exp o
 r  s
f t cp  vx, t  f (t )dt 
 IGpt 

P  1  exp  
20
 ls

Q 

20 1
12 2
I
Intake dose
Fitting
parameter

I
For tuberculosis:
For both airborne and droplet transmission modes:
Pathogen generation rate
Respiratory deposition fraction
m
I
I
[Nicas, 1996 ]
o
j 1
j
j
s o
to
0
j
[Sze To et al., 2008]
Nicas M. 1996. An analytical framework for relating dose, risk, and incidence: an application to occupational tuberculosis infection,
Risk Anal., 16, 527–538.
Sze To G.N, Wan M.P, Chao C.Y.H, Wei F, Yu S.C.T, and Kwan J.K.C. 2008. A methodology for estimating airborne virus exposures in
indoor environments using the spatial distribution of expiratory aerosols and virus viability characteristics. Indoor Air, 18,
425–438.
Sze To G.N. and Chao C.Y.H. 2010. Review and Comparison between the Wells-Riley and Dose-response Approaches to Risk
Assessment of Infectious Respiratory Diseases, Indoor Air, 20, 2-16.
W
or
ht kp
tp la
:// ce
ww a
w. nd
ea In
d
t
Exposure after n hand-to.lt oo   
 
mucous membrane contact:
h. r  
s
A
e
  





/a ero
er s


os ol
o









 s
ls 20


20 1







 

12 2
Integral form:
Indirect Contact Pathway
fs, fh, fm : frequency of coughing/hand-to-contaminated
surface contact/ hand-to-mucous membrane contact.
1
Nx : amount of pathogen on the contaminated surface
after a cough.
3
2
cm, ch : fraction of pathogen transferred to the mucous
membrane from hand/transferred to hand from the
contaminated surface.
b : a constant related to the survivability of the
pathogen on hand.
b
b



fh

fm
fm
fs
1  ch fm  e
1  e

Em n 
N x cm 
A  1  ch
B
b
b


fh
fh
1  e fh

1

c

e
h


th
A: n 
B:
n  1 ch 1  cm e
1  1  ch
1  1  ch

b
fm
 nch 1  cm e
2
b
fm
b



fm
1  1  cm e 


n
fh
fm
fh
fm
ch 1  cm e


b
fm
 ch 1  cm e
Em,t  cm f m  N h dt
0
  n 1
b
fm
2

1  1  c h


1  1  c h

t
n
n
fh
fm
fh
fm
b


fm
1  1  c m e

b


fm
1  1  c m e

b
n
 
  1  1  c n e f m
m
 
 

 1  c
h


fh
fm

1  1  c
h


b


fm 
 1  cm e


fh
fm




fN 
ch f h  N s 0  s x 
ch f h  a 
ch f h f s N x

exp  ch f h  a t   exp  cm f m  bt 
1  exp  cm f m  bt 
Nh 
ch f h  a cm f m  b
cm f m  b  ch f h  a 
Multiple-pathway
Dose-response Model
W
o
“Escaping the
concept :
ht rkinfection”
p
P  1  t1p
 P 1laP 1  P 
:// ce
wwP are
anthe infection risk via the 1 , 2 and m
where P , P and
exposure pathways respectively.
w. d
ea In
Beta-Poisson model:
t.l do
P      1  exp  r N  r N th
 r o
.s N rARr Rr Rr dr dr dr
e/ e
ae ro
Exponential model:
so
P  1  exp  r N  r N    r N  ro
so ls
ls 20 and
where r , r , r , N , N and N stand for the fitting parameters
2
12
intake doses for the 1 , 2 and m exposure pathways,0
respectively.
12
I
I ,1
I,1
I
I ,2
I,2
1 1
1
0 0
0
1
2
m
st
I,m
1
I
1
I ,m
1
1
1
2
2
st
2
2
2
nd
m
m
m
m
1
2
nd
m
th
1
2
m
m
th
Exponential model is used since it only requires a single set of fitting
parameters.
Sze To & Chao. Indoor Air, 20, 2-16, 2009
Target Pathogen: Influenza A virus
Parameter
W
or
ht kp
tp la
:// ce
ww a
w. nd
ea In
t.l do
th or
.s
e/ Ae
ae ro
ro so
so ls
ls 20
20 1
12 2
Value
Remarks
Reference
c virus concentration
5  105 TCID50
Median concentration from 7 patients
Murphy et al., 1973
r
Fitting parameter
ID50 = 1.8 TCID50
r = 0.385
Infectious dose for aerosols  3 m
(mean value of the range: 0.6-3.0)
β = 0.6.
Alfard et al., 1966
ID50 = 223.5 TCID50
r = 0.0031
Nasal infectious dose, for larger
aerosols and mucous membrane
(mean value of the range: 127320)
Douglas, 1975
75% after aerosolization, 1%/min
additional decay within 15
minutes
Extrapolated from Figure 2B. Under
21oC, 5% RH.
Schaffer et al., 1976
fs Cough frequency
18 cough/hr
Median cough frequency of 60 patients
Loudon & Brown, 1967
fh Hand contact
frequency
3 /hr
Assumption
fm
Nasal/eye membrane
contact frequency
0.7 /hr
Frequency of eye-rubbing and nosepicking of 124 adults
Hendley et al., 1973
ch
Hand transfer efficiency
0.00251Af/Ac
Af: Area of the fingerpad
Ac: Area of the contaminated surface.
Transfer efficiency of influenza virus to
fingerpad from porous material is
around 0.251%
Beam et al., 1982
cm
Membrane transfer
efficiency
1
Assumption
b Viability on hand
~6.4 /hr
Influenza virus survived on skin for
more than 0.03 day
f(t)
Viability
Walther & Ewald, 2004
Risk
W Assessment in an Isolation Ward
or
ht kp
tp la
:// ce
ww a
w. nd
ea In
t.l do
th or
.s
e/ Ae
ae ro
ro so
so ls
ls 20
20 1
12 2
Case 1
Current
situation
Case 2
Lower ACH
(6ACH)
Case 3
Bed
allocation
Case 4
Inlet vent
position
Case 5
Higher RH
(70% RH)
W Risk Assessment in Aircraft Cabin
or
ht kp
tp la
:// ce
ww a
w. nd
ea In
t.l do
th or
.s
e/ Ae
ae ro
ro so
so ls
ls 20
20 1
12 2
Inhalation Pathway
Indirect Contact Pathway
Estimated infection risk via inhalation at seat
Case
A4
Case
A3
A2
A1
Estimated infection risk via hand contact at seat
A4
A3
A2
A1
100 L/s
0.016
0.013
0.010
0.002
100 L/s
1.50  10-7
7.87  10-8
2.74  10-8
3.08  10-8
200 L/s
0.005
0.012
0.017
0.016
200 L/s
1.62  10-7
9.06  10-8
2.70  10-7
2.96  10-7
Fail (20 L/s)
0.032
0.045
0.018
0.014
Fail (20 L/s)
1.50  10-7
1.39  10-7
3.36  10-8
2.80  10-8
B4
B3
B2
B1
B4
B3
B2
B1
0.050
0.013
4.38  10-6
3.63  10-6
1.18  10-7
1.19  10-7
1.15  10-7
1.10  10-7
100 L/s
0.659
0.132
200 L/s
4.51  10-6
3.75  10-6
0.127
0.033
0.040
Fail (20 L/s)
4.97  10-6
3.56  10-6
1.18  10-7
1.13  10-7
C3
C2
C1
C4
C3
C2
C1
0.094
0.037
0.034
5.34  10-6
1.27  10-7
1.14  10-7
5.57  10-6
1.21  10-7
1.13  10-7
5.33  10-6
1.20  10-7
1.10  10-7
0.070
Fail (20 L/s)
0.643
C4
200 L/s
Fail (20 L/s)
patient
Aisle
0.030
0.580
Index
100 L/s
0.018
200 L/s
100 L/s
Aisle
100 L/s
0.024
0.041
0.029
200 L/s
0.095
0.052
0.023
Fail (20 L/s)
Index
patient
A2 A1
-Passengers seated closed to index case have much higher risk than
the others
B4 B3
B2 B1
-Risk contributed by inhalation pathway is higher than the risk
contributed by indirect contact pathway by 4-5 order of magnitude
C3
C2 C1
A4 A3
Index patient
-Increase in supply air flow rate reduces the average risk of all
passengers, but enhances the dispersion of expiratory droplets; thus,
some passengers seated far from the index case have higher risk under
higher supply air flow rate.
Use of Risk Assessment Model in
W
Retrospective
o Analysis

rk
h
Risk assessment
model
plcan be used to perform retrospective analysis on known cases to
t
t
relieve important
p:information.
ac
//w e
ww an
d
.e In
at d
.lt oo
h. r
se A
/a ero
er s
os ol
ol s 2
s2 0
01 12
2
Risk Assessment Model
Known Parameters
Infection Risk
Risk Assessment Model
Unknown Parameters
Attack Rate
Assuming every susceptible person has
the same risk (or assuming a well-mixed
air).
(a)
Spatial Distribution of
Infectious Particles
Risk Assessment Model
Known Parameters
Spatial Infection Risk
Spatial Distribution of
Infectious Particles
???
Unknown Parameters
???
Risk Assessment Model
(b)
Spatial Pattern of
Infection Cases
To consider the heterogeneous infection
risk (or to consider the spatial infection
pattern).
W of Infection
Likelihood
or
ht kp
tp la
c
:
N
/
e
L p     p 1  p/w
n
 
ww an
d
.e In
at d
.lt oo
h. r
se A
/a ero
er s
os ol
ol s 2
s2 0
01 12
2
 When a person has a certain infection risk, there are two possible
outcomes: the person will either be infected or remain uninfected.
Binomial probability can be used to describe the event:
n
N n
Infected
Index
case
Uninfected
L(p): probability of having p as the infection risk of the susceptible in the outbreak,
ranges from 0 to 1. It is also referred to as the likelihood. The first parenthesis in the
right hand side is the binomial coefficient, N: The total number of susceptible
people. n: number of susceptible who acquired the infection.
The concept can be used to perform retrospective analysis of infection cases. By
grouping different susceptible into different groups according to their risk level, this
method can supplement risk assessment model in analyzing spatial infection pattern.
Unknown
Parameter, α1
Unknown
Parameter, α2
Unknown
Parameter, α3
Unknown
Parameter, α4
Unknown
Parameter, α5
Spatial Infection
Risks, P1
Spatial Distribution of
Infectious Particles
Risk Assessment
Model
Spatial Infection
Risks, P2
Spatial Infection
Risks, P3
Spatial Pattern of
Infection Cases
L1
L2 = Lmax
Likelihood
Estimation
L3
Spatial Infection
Risks, P4
L4
Spatial Infection
Risks, P5
L5
Sze To, and Chao (2010). Use of Risk Assessment and Likelihood Estimation to Analyze Spatial Distribution
Pattern of Respiratory Infection Cases, Risk Analysis, 31(3), 351-369.
Retrospective
analysis on infectious source strength
W
or
 The Approach
ht kp
tp la
:// ce
ww a
w. nd
ea In
d
t
o
.
Information
Exposure Level
l
t
o
collection of the
& Intake Dose
CFD Simulation
h
r
outbreak
Fraction
.s
A
e/ e
ae ro
ro soRisk Assessment
l&sLikelihood
Infectious s
Future Studies
o
Source Strength
20
ls Estimation
20 1
12 2
Data from medical
record; Indoor
environmental
condition
Impacts of human
movements, size profile
of pathogen-laden
droplets, etc.
Geometry construction;
Simulation of airflow
and droplets injection
MLE is used to
estimate the most
likely infectious
source strength
Convert the
simulation data into
intake dose fraction
Adopting Dose-response
model for risk
assessment; Likelihood
Estimation
W using an outbreak case
Example
•
•
or
ht kp
tp la
:// ce
ww a
w. nd
ea In
t.l do
th or
.s
e/ Ae
ae ro
ro so
so ls
ls 20
20 1
12 2
The outbreak case happened in the economic cabin on a flight from Chicago to Honolulu in April 1994;
The flight lasted for 8.75 hours;
•
•
•
The case on the Boeing 747-100
(Kenyon et al., 1996)
•
Among the 15 contacts with positive test
results in the investigation afterward, 6 had
no other risk factors which indicated that they
were very likely to be infected by the index
case during the trip.
Only the blue area in the picture was
simulated in this study because the infectious
strength was substantially weakened beyond
this area due to the spatial distance.
3 passengers were considered as secondary
cases infected by the index passenger. Since
the 3 infected passengers seated near the
index case.
No.12 was a seat for the crew member who
seldom sat there so it was not included in the
simulation.
Kenyon et al., (1996) Transmission of multidrug-resistant mycobacterium tuberculosis during a long
airplane flight”. J.Medicine
CFD Simulation
W
or
ht kmodeling
Geometry
of the aircraft cabin
p
tp la
Air outlet slots
:// ce
(0.10m × 7m,
ww a
2Nos.)
n
w. d
ea In
t.l do
th or
.s
e/ Ae
ae ro
ro so
so ls
ls 20
20 1
Supply air slots
12 2
Photo Showing the air inlet slot in
Boeing 747-100B (Backer et al., 2006)
Air Exchange
Rate (filtered air)
(0.04m × 7m, 2Nos.)
Ventilation
capacity per
passenger
(filtered air)
20.0 h-1
(Hocking, 1998)
7.9L/s
(in simulated cabin)
3-D version of simulation cabin part
Backer et al., (2006), Validation for CFD Prediction of Mass Transport in Aircraft Passenger Cabin FAA,
http://www.faa.gov/library/reports/medical/oamtech
Hocking (1998), Indoor air quality: recommendations relevant to aircraft passenger cabins, Am. Ind. Hyg. Assoc. J.
CFD Simulation
W
or of droplet injections
Simulation
kp
tp la
:// ce
ww a
w. nd
ea In
t.l do
th or
.s
e/ Ae
aedropletsrwere
• 10,000 tracer
injected for each size;
o
sowas considered in
Evaporation ofrodroplets
so ls
simulation;
20 droplets
ls to tracing
• Transient mode was adopted
20 1
movement in cabin.
12 2
ht
•
Droplets size spectrum in a real cough (Chao et al. 2009).
6.7 mg of droplets was generated in a cough on average.
Droplet size
(Initial Diameter)
[μm]
3
Measured Number
in one cough
Droplet nuclei
[μm]
212
1.2
6
967
2.4
12
363
4.8
20
156
8.0
Chao, Wan, Morawska, Johnson, Ristovski, Hargreaves, Mengersen, Corbett, Li, Xie, Katoshevski. (2009).
Characterization of Expiratory Air Jets and Droplet Size Distributions immediately at the Mouth Opening,
Journal of Aerosol Science.
W
or
Risk Assessment
and Likelihood Analysis
ht kp
t
l
p
ac
Exposure:Level
//w e N  p d b v(t) dt
&

a
v 
w
n
Intake Dose Fraction w
d
.e In
at d
.lt oo
h. r
Risk Assessment
se A


/a ero
er s
os ol
s
o
Likelihood
20
Estimation
  ls2
  
01 12
2
Relative intake dose of each susceptible passenger:
r
r
c
i
t
i i
i
• Group the susceptable passengers according to their
relative intake dose
PI ( xi, to)  1  exp  (Qp  N r  to)
• Infection Risk:
•Likelihood of infection

Lr P  

m
i 1

N i Lr pi 

1
m
Likelihood
Risk Assessment
& Likelihood Analysis
W
or
ht kp
tp la
:// ce
ww a
w. nd
ea In
t.l do
th or
.s
e/ Ae
ae ro
ro so
so ls
ls 20
20 1
12 2
• Average TB infection rate of hospital
employees was about 1% (Price et al. 1987).
In these nosocomial cases, the exposure time
of employees to infector was much longer
than in the aircraft cabin case. Short
exposure time and high infection rate
indicated that the index case in the cabin was
very probably a super spreader.
Q (viable bacillus/hr)
Maximum Likelihood Estimation Curve
•
MLE of the infectious source strength: 17.2 millions of viable bacilli/hr
•
95% confidence interval: 2.29 mil – 153.4 mil viable bacilli/hr
•
If using Well-mixed air approach
PI 
 Iqpt 
C
 1  exp  

S
Q 

Estimated infectious source strength is 127 quanta/hr.
(1 quantum = 1 viable bacillus for TB (Huebner et al. 1993) )
Huebner et al. (1993) The tuberculin skin test, Clinical Infectious Disease.
Price et al. (1987) Tuberculosis in Hospital Personnel, Infection Control.
Yeager et al. (1967) Quantitative studies of mycobacterial populations in
sputum and saliva. American Review on Respiratory Disease.
• Some TB patients can have more than 30
millions TB bacilli/ml in their respiratory
fluid (Yeager et al. 1967). It is possible for
an infector to generate millions of bacilli per
hour.
• The difference between the two approaches
may be caused by:
Only small infectious particles can remain
suspended in air, which only constitute
1/5000 volume in the total droplets volume
generated by coughing. However, the wellmixed air approach considers all sizes.
The gas phase assumption in Well-mixed
air approach ignores the respiratory
deposition of infectious particles in alveolar
region. In fact, only 1%-10% of droplet
nuclei could be deposited and to commence
infection.
Use of Bacteriophage in Exposure & Risk Assessment
W
or
ht kp
tp la
:// ce
ww a
w. nd
ea In
t.l doPlaques formed by E. Coliphage
th or
.s
e/ Ae
ae ro
ro so
so ls
ls 20
20 1
12 2
Electron micrograph of multiple
bacteriophages [Adrian, 1985]
70
Proposed method (Along Supply Vent)
Bacteriophage exposure (pfu)
60
Biological sampling (Along Supply Vent)
Proposed method (Along Exhaust Vent)
Biological sampling (Along Exhaust Vent)
50
40
30
The
infecto
r
20
10
0
0
0.5
1
1.5
2
2.5
3
Lateral distance from aerosol injection point (m)
Validating an exposure assessment model
Assessing infection risk of a hypothetical
case
Sze To, Wan, Chao et al. Indoor Air, 18, 425-438, 2008
Use ofWBenign Bacteria in Containment Assessment
or
ht kp
tp la
:// ce
ww a
w. nd
ea In
t.l do
th or
.s
e/ Ae
ae ro
ro so
so ls
ls 20
20 1
12 2
New isolation ward with the highest standard in Hong Kong.
Each cubicle has an anteroom with interlock system. 100% fresh air supply, 5-10 Pa negative pressure, upper room
UVGI, and at least 12 Air Change Per Hour are maintained.
Containment performances against tuberculosis (TB) bacilli and influenza virus are concerned.
Leakages of the airborne pathogens during door open/door close/entry and exit of health care worker were
assessed.
Tuberculosis bacilli are rod-shape bacteria. A benign strain of E. Coli bacteria, also rod-shape, was used to
simulate TB-laden aerosols.
E. Coli collected and cultured on a plate
Layout of one cubicle
Use ofWBenign
Bacteria in Containment Assessment
o
•
ht rkp
tp la
c
:
/
Artificial saliva
/wwitheE.acoli was
aerosolized andw
the droplets
nd
w
were collected by an.eviable I
at zonend
impactor at the adjacent
.lt oo
h. r
Transport of aerosols by door
Ae
opening, human movement se
/a ro
er s
os ol
ol s 2
s2 0
01 12
2
Particle counter
Aerodynamics
particle size
•
Impactor
Nozzle
Transport of pollutant by human
entering isolation room
Use ofWBenign Bacteria in Containment Assessment
or
ht kp
tp la
:// ce
ww a
w. nd
ea In
t.l do
th or
.s
e/ Ae
ae ro
ro so
so ls
ls 20
20 1
12 2
Results
Cas
e
Injection
Point
Measurement
Location
Door
Closed
Door
Opened
With
Entry/Exit
1
Cubicle
Anteroom
0.06%
0.28%
0.46%
2
Anteroom
Corridor
1.0%
1.0%
2.7%
3
Corridor
Anteroom
6.9%
3.0%
3.2%
4
Anteroom
Cubicle
20.0%
18.3%
20.7%
5
Corridor
Nurse Station
0.001%
0.001%
0.003%
Inter-zone transport of bacteria was observed in all situations.
Human movement enhances the leakage of airborne pathogen.
Anteroom, negative pressure, high ACH, etc, cannot 100% prevent inter-zone transport of airborne
pathogen.
Due to negative pressure, airborne pathogens leaking out from one cubicle will be drained into another
cubicle efficiently.
Nurse station of the ward is quite well-protected, since it is under positive pressure with respect to the
corridor. However, the health care workers may still be exposed to pathogen in a greater magnitude when
they travel through the corridor.
Use of bacteriophage to assess the containment performance against aerosolized virus, e.g. influenza virus,
can be a good tool for assessing health risk.
Leung, Sze-To, Chao, Yu, and Kwan. 2012. Study on the Inter-zonal Migration of Airborne Infectious Particles in an Isolation
Ward using Benign Bacteria. Indoor Air. Revised Version Submitted.
W
UVGI (254nmoUVC)
in
r
k
isolation h
room, exhaust
ttp pla
duct to inactivate
pathogens
:// ce
ww a
HEPA filter to remove
nd
airborne infectious
w
particles/ Air cleaning, etc. .
ea In
t.l do
th or
.s
e/ Ae
ae ro
ro so
so ls
ls 20
20 1
12 2
Treatment Technology and Their Effectiveness
•
Approximate market share of
different UVGI systems
Microbial growth
control 32%
•
Only inactivate pathogens in
upper part of the room?
Room
circulation 17%
Upper room
air 25%
In-duct systems
27%
UVGI can inactivate airborne bacteria
with effectiveness ranging from 4698%. Linear relationship was found
between the UVGI inactivation rate
and UV irradiance level.
About 60% of UVGI systems were
used in health care facilities.
Xu P, Peccia J, Fabian P, Martyny J.W, Fennelly K.P,
Hernandez M, and Miller S. 2003. Efficacy of
ultraviolet germicidal irradiation of upper-room air in
inactivating airborne bacterial spores and
mycobacteria in full-scale studies. Atmospheric
Environment, 37, 405-419
50
40
30
%
20
CFU decreased by 30-40% after
the UV lamps were switched
on.
Wu, C.L., Yang, Y., Wong, S.L., and Lai, A.C.K. 2011.
A new mathematical model for prediction irradiance
field of upper-room ultraviolet germicidal systems.
Journal of Hazardous Materials, 189, 173-185.
10
0
hospitals
shelters
prisons
clinics
other
Kowalski W.J, and Bahnfleth W.P. 2000. UVGI
design basics for air and surface disinfection.
Heating/Piping/Air Conditioning Engineering.
72, 100-110.
Beggs C.B, Noakes C.J, Sleigh P.A, Fletcher
L.A, and Kerr K.G. 2006. Methodology for
determining the susceptibility of airborne
microorganisms to irradiation by an upperroom UVGI system. Journal of Aerosol
Science, 37, 885-902.
W
or
k
h
pl
Personalized
ttp ventilation
a
– Provide clean
and
ceair close to the
:// cool
occupants
ww a
nd
– Improve perceived air
quality
w
– Improve peoples’ thermal
.e comfort
I
at ofnd
– Protection from and minimizing
.lt oo
airborne transmission of infectious
h. r
agents
-Dissatisfied
percentage decreased
s
A
e/ -Acceptability
– Individual control
e
of air increased
r
a-Reported
symptoms decreased
er oSBS
s
-Local
increased
osthermalocomfort
ls
o
20air quality when
lsperceived
PV improves
20 ventilation
compared to mixing
12
12
Treatment Technology and Their Effectiveness
Personalized
ventilation
•
Kaczmarczyk J, Melikov A, and Fanger P.O. 2004. Human response to personalized ventilation and mixing ventilation. Indoor Air, 14, 17-29.
Melikov A.K. 2004. Personalized ventilation. Indoor Air, 14, 157-167.
W
or
ht kp
t
l
p
a
Probability of infection
:// cdecreased
e
27%-65% with PV w
ww an
d
.e In
at d
.lt oo
h. r
se Air distribution,
Ae
applicability and energy:
/aTransportrofopollution with PV and other
er system.
ventilation
Energy saving by using
s
o
PV. o
so ls
ls for 2PV0(and other
Control strategies
20 1
ventilation systems)
12 2
Treatment Technology and Their Effectiveness
Pantelic J, Sze To G.N, Tham K.W, Chao C.Y.H,
and Khoo Y.C.M. 2009. Personalized ventilation
as a control measure for airborne transmissible
disease spread. J.R. Soc. Interface, 6, S715-S726.
Melikov A.K. 2004. Personalized ventilation. Indoor Air, 14, 157-167.
W
or
k
Requirement:
Coatinghon
surface
to inactivate
p
t
la
Response to body temperature,
pathogens tp
c
:
moisture, light, etc.
/w evirusa
Survival time of /bacteria,
Long duration: refill monthly?
ww n
– day? Week?
Easy to refill the coating?
d
Surface with antimicrobial.coating
ea toIn
Non-toxic, etc
inactivate pathogens and reduce
t.lthe do
infection risk from indirect contactth
or
.s B.Asubtilis S. aureus E. coli
Surfaces:
e
er
– Lift buttons, door handles, keyboards /a
er os
os ol
ol s 2
s2 0
01 12
2
Treatment Technology and Their Effectiveness
•
•
•
Inactivate 99% of bacteria in 1min
•
1
3
2
Reduce infection risk
from indirect contact
Li Y, Leung W.K, Yeung K.L, Lau P.S, and Kwan J.K.C. 2009. A
multilevel antimicrobial coating based on polymer-encapsulated
ClO2. Langmuir, 25(23), 13472-13480.
W work: Resuspension of infectious droplets
Recent
•
or
ht kp
tp la
c
:
/
/w ofeinfectious
Re-suspension
particle, ultrafine particle
an
w
w. vacuum
– Origin - Walking,
cleaning, sweeping, bed
d
ea In
making
t.l do
or
• Wind turbulence,th
vibration
.s
A
e
– Mechanism - Lifting/ Sliding/
er
/a Rolling
o
e
so
ro pathogen-laden
– Material - Solid particle, droplet,
so ls
droplet
ls 20
20 1
12 2
W
or Roadmap to investigate
of infectious droplets
kp
ht Resuspension
tp la
:// ceResuspension of infectious droplets by human activities
ww a
w. nd
In
eaWind turbulence
Vibration
t.l do
th or
.s
Wind tunnel experiment
e/ Ae Vibration experiment
ae ro
ro so
so forceslsin normal
Require the removal
and tangentialldirections
s2 20
01 12
2
Centrifuge experiment
W
or
Centrifuge
Experiment
k
ht p
t
l
p
a
Centrifuge experiment
:// ceto determine the removal forces distribution.
wwis smaller
The removal force
in tangential direction than normal
a
nd
direction.
w
Smaller removal force to
.eweightInratio for larger particles.
at Polystyrene
d
51um 16um
o
.lParticle
th (PS)
or
.s
e/ Ae
ae ro
ro so
so ls
ls 20
20 1
12 2
Rrmaining fraction (%)
100
75
Normal (51um)
Tangential (51 um)
50
Normal (16um)
Tangential (16um)
25
0
1
100
10000
log10 (RW2/g)
1000000
(Removal force/ weight)
W
or
Centrifuge
Experiment
k
ht p
tp particle,
la droplets may split into two portions
Unlike solid
c
and only one:/portion
from the substrate.
/w edetaches
ww an
d
.e In
at d
.lt oo
h. r
se A
/a ero
er s
os ol
ol s 2
s2 0
01 12
2
Change of average size of droplets at
initial size of 30μm
Remaining volume fraction of 30μm
glycerol droplets from acrylic substrate
W Resuspension Modeling
or
ht kpby Wind Turbulence
tp la
:// ce by rolling, angular velocity is described by
• Assume resuspension
ww a
(Lift)
w. I dnd bF  a F  a F  bmg sin   a mg cos 
dt In 2
2
2
e
(Drag) a
t.l do
•Adhesion
th forceoFar was found by centrifuge
.s with normal
experiments
force
A
e
•Ratio of a toeb/ is the ratio
of tangential force to
r
ae o experiments
normal force by Centrifuge
ro so
so ls
Angular velocity is modeled by Langevin equation.
20 process.
s2 Wiener
The fluctuating angular velocity is represented by a white lnoise
12
0
2
12
dt
2
FL

D
dp
b

a
mg
L
a
FD
P
x
Fa
d t    t 
Timescale for energy
dissipation during rolling
T

T
dW t 
Model constant
W Resuspension modeling
or
ht kpby Wind Turbulence
tp la
ce when ω is
:// occurs
Resuspension
ww value
larger than a critical
an
w. modeld
A better fit than the RRH
eaand In
(Reeks, Reed & Hall 1988)
d
Rock’n Roll model (Reeks & tHall
.lt oo
2001)
h. r
se A
/a ero
er s
os ol
ol s 2
s2 0
01 12
2
Particle at rest,  = 0
•
t t+t
•
Particle unmoved,
=0
1
Find.
No
fraction remaining after 1s
Yes
Particle rolling
RRH model
0.8
 > 0?
C0=0
0.6
No
C0=1e-3
0.4
Rock'n Roll model
0.2
 > c?
Yes
Particle is resuspened
0
10
-1
0
10
friction velocity (m/s)
10
1
Algorithm of the Monte Carlo simulation
Fu, Chao, et al. 2012. Particle Resuspension in a Wall Bounded Turbulent Flow. Journal of Aerosol Science,
under revision after review.
W
or
Wind
tunnel
Experiment
k
ht p
tp la
:// ce
Microscope
ww Wind
tunnel
with camera
an
w. d
ea In
Wind
t.l do
th or
.s
Test
A
e/ e
section
Cross-section: 20mm X
r
a
200mm
er os
The wind tunnel is 3m long
os ol
before the test section to
s
o
ls 20
have a fully developed
20 1
turbulence at the test
12 2
section. The particles were
assumed to be in the viscous
sub-layer.
The particles were aerosolized using a nebulizer
and deposited on a substrate. The substrate was
put in the test section for the experiment
W
Wind
oWind
tunnel Experiment
direction
r
k
h
ttp pla
Force acting on
the particle
:// ce
51μm PS
ww a particles on
n
acrylic
w
Wind
d
in
.e Isubstrate
turbulence
ndwind
at the
.lt tunneloo
h. r After 1min of about 20m/s
se A wind flow in the wind
ertunnel
The particle resuspends when the moment /a
er os
is larger than a critical value.
os ol
ol s 2
s2 0
12
0
12
Some particles
FL

dp
b
m
 g
a
FD
P
x
Fa
were resuspended
from the
substrate
W
Final Remark
or
ht kp
• Differenttpthoughts
la between medical community and
ce
://community
engineering
ww a
w. nd
ea In
do
Collaboration
t.between
lth o
different
.s r A
expertisee
Scientists
/a ero
er s
os ol
Medical
ol s 2
expert,
s2 0
clinicians
01 12
2
Education
Architects,
engineers