1. 1 INTRODUCTION
Today’s regulatory air quality standards for
healthcare facilities effectively maintain heating,
cooling, and ventilation requirements within acute
patient room by fulfilling overall air change rates.
American Society of Heating, Refrigeration, and Air
conditioning Engineers or ASHRAE require that air
changes occur to keep fresh air continuously enter-
ing spaces, however this guideline does not suggest
how to effectively remove contaminated air that may
be caught in dead zones effected by room design.
The standard does not consider that the air being
mixed in the ventilated spaces is allowing clean air
to be mixed with contaminated air.
These airflow patterns created by the ventilation
contribute to air-temperature, distribution, thermal
comfort, air quality, and the possibility of airborne
pathogen transmission within a space or spaces. Air-
flow patterns of existing rooms need to be studied in
order to understand pre-existing relationships be-
tween location and type of supply diffusers, supply
airflow rates, supply temperature, air return location
and size, infiltration, furniture arrangement, heat
sources and location of the patient and others in the
room.
An example used to look at risk of infection due
to room design is Influenza. Influenza was chosen in
part because of its seasonal and continued yearly ef-
fects on populations as well as its lower quantum
level. The quantum level explained more fully in the
risk assessment method portion, is a measure of the
infectious material present and the pathogenicity of
that material (Wagner et al. 2009). Other airborne
threats such as Tuberculosis, and SARS would yield
a larger quanta production rate.
Studies have been done proving influenza as an
airborne threat within hospitals at close range like
that in patient rooms (Blachere et al. 2009,
Brankston et al. 2007). However, there have only
been a few significant studies showing how airflow
patterns can be affected by supply and return vents
as well as room configuration. A review article lead
by Dr. Yuguo Li in 2007 found only 40 studies fo-
cusing on ventilations impact on airborne transmis-
sion in the built environment databased between
1960 and 2005. Of these forty studies only ten were
deemed conclusive by a team of multidisciplinary
reviewers. These conclusive studies had met the
teams determined evidentiary threshold, and provide
strong evidence on ventilation effects of airborne
transmission of diseases including Tuberculosis,
SARS, Influenza and chickenpox(Li et al. 2007).
Most recently a study using computational fluid
dynamic modeling was completed by Khankari. Two
mechanical placements were tested in one patient
room to show resultant airflow patterns (Khankari
2015). The information gathered from this study can
be significantly furthered by looking at two common
The impact of patient room design on airborne Hospital-Acquired
Infections
A. Copeland & A. Sharag-Eldin
Kent State University, Kent, Ohio, United States
ABSTRACT: Transmission of airborne diseases in healthcare facilities is an increasingly important concern.
This, in part, is due to the reduction in funding from insurance companies for hospital-acquired infections
(HAI) and the consequent economic impact of an influenza outbreak in a hospital. With increasing cases of
HAI in the USA, it became necessary to examine the current ventilation standards for healthcare facilities.
While design guidelines have focused on recommending appropriate ventilation rates, it ignored the delivery
of conditioned air to occupied spaces and the impact of room layout relative to the placement of air supply and
return. The paper will also estimate the probability of infection based on air change effectiveness, and the rela-
tive spatial relationship between HVAC air delivery system and room design. The resulting outcome is a
guideline intended to support health facility designers.

2. patient room designs and computational fluid dy-
namic models to test a plethora of supply and return
air locations in relation to patient, caregiver and
door.
The following study using a calibrated model veri-
fied by field measurements of air distribution pat-
terns in patient rooms. In addition to airflow pat-
terns, the CFD algorithm is used to determine the
age of the air to measure the effectiveness of the
recommended air changes in the standards delineat-
ing through graphical and numerical means the po-
tential location for concentrated airborne contamina-
tion. It is also intended to encourage the
development of design codes and standards that take
into account airborne pathogen transmission in plac-
es where the patient is most vulnerable to infection
from visitors or staff.
2 RESEARCH METHOD
2.1 Room Criteria and Selection
The geometries selected and tested in this paper cor-
respond with the general trends in the healthcare de-
sign field. While shapes and sizes of rooms can dif-
fer drastically, three average room sizes were
selected to correspond with small, medium and large
patient room sizes.
The room sizes are as following 21.4 m2
(230 ft2),
27.87 m2
(300 ft2
) and 34.84 m2
(375 ft2
) all inclu-
sive. The sizes address the possibility of the imple-
mentation of universal or acuity adaptable rooms in
all sizes and critical care patients in the larger room
size (Brown & Gallant, 2006).
The patient rooms used for the simulation follow the
FGI Guidelines for Hospitals and Outpatient Facili-
ties 2014, thus having private bathrooms, and glaz-
ing that is no less than 8% of the floor area of the
room served (2014 FGI Guidelines for Hospitals and
Outpatient Facilities 2014).
Each room tested is rectangular in shape and pro-
pose either an inboard bathroom located at the en-
trance of the patient room or outboard bathroom, lo-
cated along the exterior wall of the room and
building. The bathrooms are placed on the headwall,
or where the patients head is located near the wall in
bed with supply air being placed above the patient
bed and return located near the door to the patient
room. The supply diffuser is a 4 way diffuser supply
air at 360 degrees at 6 air changes per hour calculat-
ed by room’s volume from finish floor to 6ft above.
2.2 Computational Fluid Dynamic Modeling
Computational Fluid Dynamic Simulation focuses
on the numerical and algorithm modelling of fluid
flow and heat transfer processes. Given boundary
conditions including climate, internal energy
sources, and HVAC systems CFD simulates a build-
ing’s likely internal or external airflow.
The numerical solution of the airflow simulation
uses differential equations to find values of flow
quantities at certain points of a system. These values
are then converted to a system of algebraic equation
that represent the interdependency of the flow at
each point (Sayma 2009). The points correspond to a
grid or mesh used by the modelling system.
2.3 CFD Modeling
IES VE has been tested and fully validated under
ASHRAE Standard 140. The IES VE program al-
lows for dynamic thermal simulation, including de-
sign load calculations, and simplified computational
fluid dynamic modelling. IES VE primarily focuses
on integrated graphics-driven results, however does
allow for simple numerical analysis based on points
located within the graphic model.
IES VE uses the k-epsilon turbulence model to
find the turbulent viscosity of a 3D space. The
standard k-epsilon model is widely accepted due to
its applicability to wide ranging flow situations pri-
marily looking at internal flows with weak stream-
line curvature models. The equation solves for the
kinetic energy turbulence and the dissipation of the
kinetic energy (Awbi 2003).
While the standard k-epsilon turbulence model is
accepted it has limitations when more complex
flows are being studied and the length scale for the
determination of turbulence can only be speculated.
The RNG k-ε or renormalization group model and
low Reynolds number k-ε model are more accurate
when looking at forced convection or mixed convec-
tion turbulence models while the Large Eddy-
Simulation (LES) model is a more accurate predictor
of flows with strong streamline curvatures (Awbi
2003).
IES VE’s MicroFlo 3D Computational Fluid Dy-
namic modeling application was used to simulate
different conditions. Boundary conditions such as
America’s northeast climate were applied to the
model through the apache application that uses
weather data to provide an environment for the eval-
uation of a building and system design. Thermal
comfort conditions were regulated by specifying
HVAC system and placement based on ASHRAE

3. 170, 2013 healthcare patient room standards includ-
ing required air changes per hour (ACH) and humid-
ity.
MicroFlo produces visual representation of an in-
ternal space’s airflow based on velocity, concentra-
tion of CO2
and local mean age of air. Local mean
age of air (LMA) is the amount of time a parcel of
air has been in certain areas of the simulation space.
Higher values indicated areas with poor ventilation.
2.4 Calibration
In order to calibrate IES VE an educational nursing
simulation room was tested with field measurements
to prove that results gathered from IES VE would be
correct. The nursing simulation room was located in
Northeast Ohio and had an overall area of 34.19 m2
(368 ft2
). The field measurements were taken on
two days November 2nd and November 23rd, 2015.
Figure 1. (Left) Simulation room layout, (Right) simulation
room ceiling plan
2.4.1 Field Measurements
Field measurements were performed in the nursing
simulation room, 5.43 m (17.83 ft.) (L) x 6.4 m
(21.0 ft.) (W) x 3.05 m (10 ft.) (H), with the bed lo-
cated on the same wall as the bathroom shown in
figure 1. The air is supplied and returned through
linear slot diffusers located at the ceiling. The room
is illuminated by six 2’x4’ fluorescent lights, one lo-
cated within the bathroom. Measurements were tak-
en at 3’ grid intervals at .15 m (6in.), 1.1 m (3.5 ft.),
1.8 m (6ft.) above the floor using TSI indoor air
quality meters model 8386A and 8760 to gather
temperature, humidity, carbon dioxide and air veloc-
ity at nine specific points.
The boundary conditions that would be completed
by IES VE were also collected like wall surface
temperature, outside temperature and weather.
IES VE run of simulation room. The simulation
room’s CFD run had boundary conditions collected
from the field measurements, including outside tem-
perature as well as surface temperatures. The supply
air was delivered through slot diffusers that was
equivalent to 360 cfm, approximately 6 ACH. The
simulation room also used perimeter radiators locat-
ed on the exterior walls, causing higher surface tem-
peratures on the respective walls.
2.5 CFD Runs of Patient Rooms
The selected six room designs for patient rooms
were tested through IES VE, similar to the simula-
tion room specific data was collected through the
micro flow runs at three specific heights .15 m
(6in.), 1.1 m (3.5 ft.), 1.8 m (6ft.). Each room was
supplied with 6 ACH, the required ACH per
ASHRAE 170. This required 252 cfm for the small,
315 cfm for the medium, and 400 cfm for the large
patient room’s in order to satisfy the required 6
ACH.
The results all pertain the same climatic influence,
boundary conditions that coincide with February 8th.
Each result is reflecting sixty minutes of time, the
length of time for the required amount of air changes
to take place.
Table 1: Boundary Conditions for Patient Rooms
Flowrate Temperature
CFM* °F °C
Small Room 252 70 21.1
Medium Room 315 70 21.1
Large Room 400 70 21.1
*CFM based on required 6 air changes/ hour per ASHRAE 170
2.6 Data Analysis
2.6.1 Risk Assessment: Wells-Riley Equation vs.
Gammaitoni-Nucci Equation
Local mean age of air and Velocity of air vectors in
the different patient rooms are analyzed to see if any
significant differences were seen in different room
size or layouts.
The risk of transmission is evaluated using the
steady state Wells-Riley equation and non-steady
state Gammaitoni-Nucci equation. Exposure times
can vary in patient rooms from days for a patient, to
as little as a few minutes for a staff member, thus the
risk of transmission is influenced by chance events
(Beggs et al. 2010).
The Gammaitoni-Nucci equation is a modified
version of the Wells-Riley equation that reflects the
exponential increase in number of new cases of in-
fections in a room. The Gammaitoni-Nucci equation
takes into consideration the transient behavior of
transmission over shorter periods of time, and that

4. not every newly infected person may infect another
due to their short time in a particular space
(NOAKES et al. 2006). The equation considers
room volume, which varies per room size, ventila-
tion rate, and the quanta production rate. The quanta
production rate is best explained as the infections
dose required to infect 63.2% of the people in the af-
filiated enclosed space.
The mean quanta production rate for influenza
that is used for the Gammaitoni-Nucci equation is
100.0 quanta/h, a value for a highly contagious case
(Rudnick & Milton 2003). However, there is still
dispute over an official range of quanta production
rate for Influenza, as found by Rudnick and Milton it
can vary from 15-128 q/h depending on which
steady or non-steady state Wells-Riley equation used
to determine the quanta production rate.
The equation assumes ventilation as a large factor
in particle removal, while it is a significant source,
many other sources also factor in pathogen settle-
ment (Rudnick & Milton, 2003). For Influenza A, a
large factor in pathogen removal is inactivation
through settling, thus removed from the air but not
the space entirely. The equations do not take into
consideration that outside factors such as tempera-
ture, humidity and settling can have an impact on a
person’s risk of airborne infection.
Table 2: Simulation Room CFD Simulation compared to field
measurements
3 RESULTS
Each CFD run resulted in velocity quantities and lo-
cal mean age of air approximations that differed
based on room size and layout, inboard vs. outboard.
The LMA or local mean age of air and known vol-
ume of the rooms were then used for a risk analysis
of Influenza A in those respective rooms using the
Wells-Riley and Gammaitoni-Nucci equations.
3.1 Simulation Room CFD Run
The simulation room’s CFD model ran closely to the
documented field notes. The boundary conditions
were controlled by the data taken from the field
measurement like, surface temperature and input air
temperature. Table 2 shows the averages for each
differing height level for the simulation room CFD
model compared to that of the field measurements.
3.2 Room Comparison
3.2.1 Size
The selected rooms varied in three sizes considered
small, medium and large. Figure 2 shows the differ-
ences of air age in the different patient rooms. Over-
all the size did not have a large impact on velocity
and air age, rather design had an impact on the mean
air age at different areas within the room.
3.2.2 Layout
3.2.2.1 Inboard Patient Room
The inboard CFD model show that the air is circu-
lated relatively consistently among all room sizes
and the local mean age of air is similar throughout
the rooms, ranging from 10.9 minutes to 12.7
minutes. Local mean age of air is older or higher in
the bathroom if the door remains open, resulting in
Height
Average Velocity -
Simulation
Average Velocity -
Field Measurements
ft.-in. ft. /mins. ft. /mins.
0'-6" 4.74 5.625
3'-6" 8.15 10.375
6'-0" 12.11 12.5
Figure 2. Small outboard room (left: 0’-6” AFF, middle:
3’-6” AFF, right: 6’-0” AFF) LMA in minutes

5. air age of 14.5 minutes. This on average would ap-
proximately result in 6 ACH per hour, each air
change taking on average 10 minutes.
3.2.2.2 Outboard Patient Room
The outboard patient room is relatively different
when it comes to local mean age of air throughout
the space. The CFD model shows that the back zone
in all the outboard rooms have older air parcels as
shown in figure 2. This area has an air that is an av-
erage of 15 minutes old, which would be the equiva-
lent of 4 ACH, while the area around the patient bed
has an average air age of 10.5 minutes which would
result in the required 6ACH.
This air change discrepancy is significant the risk
of transmission will significantly increase in the are-
as of poor circulation. The following mathematical
risk assessments show the increased risk associated
with the poor circulation.
3.3 Risk Assessment
The Wells-Riley steady rate and Gammaitoni-Nucci
non-steady rate are mathematical models that predict
the rate of airborne transmission. The Gammaitoni-
Nucci model is better suited for short term exposure
models, however both will be used and compared to
understand the risk of transmission. Each room is
analyzed using the average ACH, thus the outboard
will be broken into zones and analyzed according to
the different LMA associated with the different
zones of the room. The differing LMA would result
in a different ventilation rate for that space, so in-
stead of the required 6 ACH there may be less in
poorly ventilated areas.
3.4 Local Mean Age of Air
The mathematical risk models predict the transmis-
sion of airborne disease and are based off of flow
rate or the ACH. However, a dimensionless equation
that shows how efficiently different room airflow
structures remove contaminants regardless of the
flow rate can also be utilized (Bartak et al. 2001).
The following table 3 shows the efficiency of the air-
flows in the six rooms. The outboard rooms were
broken into different volumes based on average local
air age to understand the changing efficiency rates
with relation to the local mean age of air at points
within the room.
3.5 Comparison between Mathematical Risk
Assessment Models
It is important to understand the above table
shows three drastically different equations, and in
turn drastically different results. The Wells-Riley re-
sults show the potential for new cases in exponential
increase or decreasing form. If the results are less
than 1 (a < 1) the pathogen would be removed or
settled before infecting any of the susceptible targets,
while the results that are greater than 1 (a > 1) would
result in increasing pathogen transmission. There-
fore, the zones that were identified as poorly venti-
lated in the outboard design have an increasing
chance of infection, while the inboard room and bet-
ter ventilated areas in the outboard would not.
The Gammaitoni-Nucci results are based on per-
centages, thus the results that are shown as decimals,
could also be shown as the equivalent percentage
(i.e. large inboard: 0.0611= 6.11%). Again, like the
Wells-Riley model the inboard rooms have well
mixed air resulting in a low percentage of people
that will contract Influenza through the airborne
Table 3: Mathematical Risk Assessment Models and Efficiency of Airflow in Patient Rooms
Room Design and Zone Wells-Riley *
Wells-Riley
with required
6 ACH
Gammaitoni-
Nucci
Gammaitoni-
Nucci with re-
quired 6 ACH
Airflow Efficiency
(Bartak et al.,
2001)**
Small Outboard 0.1061
43.89 m3
@ 10.5 LMA 0.9451 0.1560 2.0170
27.47 m3
@ 15 LMA 1.7696 0.5303 0.3544 10.61% 4.6040
Small Inboard 0.6045 0.0991 1.3588
Medium Outboard 0.0858
69.38 m3
@ 11.5 LMA 0.6206 0.1017 1.1147
19.82 m3
@ 13 LMA 2.0399 0.4289 0.3376 8.58% 4.4109
Medium Inboard 0.4898 0.0801 0.8670
Large Outboard 0.0682
91.17 m3
@ 10.9 LMA 0.4564 0.0754 0.6332
22.1 m3
@ 14.5 LMA 2.0411 0.3410 0.4087 6.82% 3.4752
Large Inboard 0.3708 0.0611 0.5097
*
Solves for P, a dimensionless exponential increase of new cases
**
The lower the number the higher the efficiency of airflow

6. route. Contrastingly, the particular outboard room
zones that have nominal ventilation result in a prob-
ability of infection of 35-40%. It is important to note
that this model looked at the probability of an indi-
vidual who has been in the room with the infected
for an hour.
Furthermore, if 15 minutes was used as the expo-
sure time rather than an hour the probability of a
susceptible person being infected would decrease
(i.e. Small outboard patient room, the poorly venti-
lated area decrease by 86% from a 35% probability
of infection to a 5% probability). The required 6
ACH in a well-mixed and ventilated room would
drastically decrease the probability of a person be-
coming infected, however depending on room design
certain areas are not getting properly ventilated and
the air age is increasing.
Lastly, the equation used by Bartak and others
takes the relationship between air age in the room of
certain volumes to find the efficiency of the airflow,
while not considering the ventilation rate. Similar to
the Gammaitoni-Nucci model, if 6 ACH were
achieved everywhere in the room regardless of the
design, the airflow efficiency in the rooms would be
improved.
4 DISCUSSION AND CONCLUSION
4.1 Significance
The main regulatory source for healthcare facilities’
ventilation systems is ASHRAE 170, it is also in-
cluded as part of the FGI Guidelines for Hospitals
and Outpatient Facilities 2014. According to
ASHRAE, non-critical patient rooms should receive
6 ACH, with a minimum of 2 outdoor ACH. Many
studies have been completed that have been the basis
of ASHRAEs regulations that dictate 6 ACH. How-
ever, from this study not all patient rooms can be
considered suitable with 6 ACH. The design has to
be taken into consideration when writing and con-
sidering minimum ventilation standards.
While the airflow in the inboard patient rooms
were effective shown by the local mean age of air,
the outboard patient room airflow was less then ef-
fective for circulating fresh air. The rooms tested
were linear in design and less suitable room designs
may be found if tested.
The outboard design’s least effective space is
what is used for family and visitor zones. The regu-
lations primarily have supply outlets located over the
patient beds to protect the patient from airborne in-
fection. However, this leaves visitors and staff more
susceptible to airborne infection like Influenza A as
well as other more transmissible diseases.
The airflow effectiveness is important to prevent
airborne disease within the room. As seen using the
mathematical risk assessment models the air age has
a large impact on Influenza A infection probability.
Thus, ventilation with room design in considera-
tion can effectively be a primary measure against
airborne infections like Influenza A or other airborne
disease. However ventilation regulation needs to fo-
cus on how design impacts spaces and its result on
the ventilation needs.
4.2 Limitations and considerations
This study primarily focused on two designs of pa-
tient rooms which leaves a lot of geometry’s untest-
ed, thus it would be suggested that room designs are
tested to understand ventilation properties for better
efficiency and prevention measures
The supply and return air were also only tested at
one location with the supply air above the patient
bed and the return near the exit. Other placement
could have shown more effective ventilation strate-
gies, and should be considered to maximize effective
airflow. While other ventilation strategies should be
explored, 6 ACH have been found to be adequate as
long as the room is receiving the adequate airflow
that allows for the required ACH, thus possible add-
ed supply to evenly distribute air may also be an ide-
al strategy.
Limitations for the CFD modeling include the
software used IES VE is a simplified CFD program
that is based more on visual representation, thus
LMA numbers are based on produced contours and
not actual data points. Furthermore, a more complex
system could produce more data per cell to better
understand small air vector characteristics like ed-
dies instead of the general room level airflow analy-
sis. The Standard k-epsilon turbulence models also
is found to over-predicts the gas turbulence kinetic
energy which results to a high turbulence viscosity,
and an estimated over mixing. Thus the result could
be showing slightly better conditions then what actu-
ally exists in the rooms (Tian et al. 2007).
The mathematical equations assumed an hour of
exposure, while many times visitors or staff are in
the areas for shorter periods of time. The quanta
used for Influenza, q = 100, is suggested as a highly
infectious strand. There is controversy over the actu-
al quantum of a disease due to large variance a quan-
ta can have. The quanta itself measures the infec-
tious material present and the pathogenicity of that
material, however the consideration for droplet
evaporation and settlement is still disputed(Wagner
et al. 2009).
The equations are also based on an assumption
that the room air is well mixed and has a proportion-
al distribution of quanta or infectious material
throughout (NOAKES et al. 2006). Due to this as-
sumption the equations were used on the different
zones that have visibly different airflow efficiency as
shown by the CFD models.
Lastly, other airborne diseases could have a sig-
nificantly higher or lower risk of infection based on

7. their quantum level. A study by Qian estimated that
the SARS quantum level ranged from 2460-4680
contingent on filter efficiency. These quanta levels
are drastically higher than the tested Influenza quan-
ta level and would lead to a faster and more devas-
tating transmission rate (Qian et al. 2009).
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