COPELAND, ALEXA, M.S. MAY 2016 THE IMPACT OF PATIENT ROOM DESIGN ON AIRBORNE HOSPITAL-ACQUIRED

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1 COPELAND, ALEXA, M.S. MAY 2016 ARCHITECTURE THE IMPACT OF PATIENT ROOM DESIGN ON AIRBORNE HOSPITAL-ACQUIRED INFECTIONS (HAI) Thesis Advisor: Adil Sharag-Eldin 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 recent cases of HAI in the USA, it became necessary to examine the current ventilation standards for healthcare facilities. Among the organizations that set standards for the prevention of disease transmission are Facilities Guidelines Institution and the American Society of Heating Refrigeration and Air-Conditioning Engineers (ASHRAE). These standards have played an important role containing airborne transmitted diseases such as Influenza A in North American hospitals. 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. Airflow is often designed to contribute positively to air-mixing and distribution, improvement of thermal comfort and air quality conditions. Unfortunately, air distribution contributes to airborne pathogen transmission as well. Few studies focused on the relative location of air supply and as well as the impact of the movement of people, but none found focused on the patient room design and layout relative to recommended airflow patterns. This research focuses on a development of a guideline to help designers understand the effect of room design on the distribution of airborne diseases and the Influenza virus in particular. The paper will present the outcomes of several Computational Fluid Dynamic (CFD) simulations of typical room configurations and layouts under many room use scenarios. The model used for simulation is calibrated and verified by field measurements of air distribution patterns in patient rooms. In addition to airflow patterns, the CFD algorithm is used to determine the age of the air to measure the effectiveness of the recommended air changes in

2 the standards. The paper will delineate through graphical and numerical means the potential location for concentrated contaminations and scenarios in which such concentrations may happen. The paper will also estimate the probability of infection based on air change effectiveness, and the relative spatial relationship between HVAC air delivery system and room design. The resulting outcome is suggestions intended to support health facility designers. It is also intended to encourage the development of design codes and standards that take into account airborne pathogen transmission in the room at which the patient is most vulnerable to infection from visitors or staff.

3 THE IMPACT OF PATIENT ROOM DESIGN ON AIRBORNE HOSPITAL-ACQUIRED INFECTIONS (HAI) A thesis submitted To Kent State University in partial Fulfillment of the requirements for the Degree of Masters of Science in Architecture and Environmental Design by Alexa Copeland May, 2016 Copyright All Rights Reserved

4 Thesis written by Alexa Copeland B.S. Arch, Kent State University, 2014 M.Arch., Kent State University, 2015 M.S. in Architecture and Environmental Design, Kent State University, 2016 Approved by Adil Sharag-Eldin, Ph.D., Masters Advisor, College of Architecture and Environmental Design Adil Sharag-Eldin, Ph.D., Coordinator, College of Architecture and Environmental Design Douglas Steidl, Dean, College of Architecture and Environmental Design

5 TABLE OF CONTENTS LIST OF FIGURES... v LIST OF TABLES... v ACKNOWLEDGEMENT... vi CHAPTER I: OVERVIEW Introduction General Problem and Consequence Background and Need Background Literature Research Need Objective Scope of the study... 4 CHAPTER II: LITERATURE REVIEW Introduction Regulatory Codes ASHRAE FGI Guidelines Hospital-Acquired Infection Influenza Airborne Influenza Other Airborne Threats Risk Assessment Ventilation Systems and Design Filtration Ultraviolet germicidal irradiation Acute Patient Rooms Size Layout Conclusion CHAPTER III: METHODOLOGY Introduction iii

6 3.2. Room Selection Computational Fluid Dynamic Modeling CFD Modeling Calibration Field Measurements IES VE run of nursing simulation room CFD Runs of Patient Rooms Data Analysis Risk Assessment: Wells-Riley Equation vs. Gammaitoni-Nucci Equation Conclusion CHAPTER IV: RESULTS Introduction Simulation Room CFD Run Room Difference Size Layout Inboard Patient Room Outboard Patient Room Risk Assessment Local Mean Age of Air Comparison between Mathematical Risk Assessment Models Conclusion CHAPTER V: DISCUSSION Introduction Significance Limitations and Considerations Future Studies Conclusion REFERENCES.48 iv

7 LIST OF FIGURES Figure 1. Simulation room layout Figure 2. Simulation Room field measurement points Figure 3.1 Local mean age of air in small outboard patient room Figure 3.2 Local mean age of air in small inboard patient room Figure 3.3 Local mean age of air in medium outboard patient room Figure 3.4 Local mean age of air in medium inboard patient room.. 35 Figure 3.5 Local mean age of air in large outboard patient room Figure 3.6 Local mean age of air in large inboard patient room.. 36 Figure 3.7 Air velocity in small outboard patient room Figure 3.8 Air velocity in small inboard patient room Figure 3.9 Air velocity in medium outboard patient room Figure 3.10 Air velocity in medium inboard patient room Figure 3.11 Air velocity in large outboard patient room Figure 3.12 Air velocity in large inboard patient room LIST OF TABLES Table 1. Patient Room Boundary Conditions Table 2. Simulation Room CFD Model compared to field measurements Table 3. Standard values used for the Risk Assessment mathematical equation. 39 Table 4. Comparison of Risk Models v

8 ACKNOWLEDGEMENT I would first like to thank my thesis advisor Dr. Adil Sharag-Eldin of the College of Architecture and Environmental Design at Kent State University. The door to Prof. Sharag- Eldin s office was always open whenever I had a question about my research or writing. In addition he has consistently promoted my growth as an individual researcher and informed architecture student. I would also like to thank Kimberly DePaul from the College of Nursing at Kent State University who was involved in the validation of the CFD modeling program for this research project. Without her time and involvement the validation of the CFD program used would not have been successful. I would also like to acknowledge Dr. Margaret Calkins of the College of Architecture and Environmental Design at Kent State University as the second reader of this thesis, and I am gratefully indebted to her for her very valuable comments on this thesis. Without her mentorship and passion for the healthcare industry this paper would not have been possible. Finally, I must express my very profound gratitude to my parents for providing me with unfailing support and continuous encouragement throughout my years of graduate study and through the process of researching and writing this thesis. This accomplishment would not have been possible without them. Thank you. Author Alexa Copeland vi

9 CHAPTER I: OVERVIEW 1.1. Introduction This chapter will cover the general problem that the thesis intends to address and mitigate. This includes general background of the issues related to hospital-acquired infections and airborne disease as well as the standards in place to negate them in healthcare settings. It will outline the focus of the paper as well as state the key research questions that will be answered throughout the paper General Problem and Consequence Today s air quality standards effectively maintain heating, cooling, and ventilation requirements within healthcare settings, specifically acute patient rooms by fulfilling overall air change rates (ACH). The American Society of Heating, Refrigeration and Air-Conditioning Engineers or ASHRAE requires that air changes occur to keep fresh air continuously entering spaces. However, this guideline does not suggest how to effectively remove contaminated air that may be caught in dead zones or poorly circulated areas, depending on room configuration. This standard may also be causing more damage by requiring air to be mixed in ventilated spaces, allowing for clean air to be mixed with contaminated air. The created air flow patterns from ventilation systems contribute to air-temperature, distribution, thermal comfort, air quality, and the possibility of airborne pathogen transmission within a space or spaces. Air flow of existing rooms needs to be studied in order to understand pre-existing airflow patterns between location and type of supply diffusers, supply airflow rates, supply temperature, air return 1

10 location. Size, infiltration, furniture arrangements, heat sources and location of patient and others in the room may also effect the air flow patterns in patient rooms. The standards do not regulate ventilation changes based on room configuration, only by function. For example, patient isolation rooms require more air changes and maintenance of positive pressure, because of function, not design. While function in acute patient rooms stays constant, design does not and has recently vastly fluctuating due to new trends in the healthcare field like patient centered care and the use of acuity adaptable rooms. Influenza is used as an example of possible infection due to its seasonal reoccurrence as well as the fluctuating virus strains. The virus is also less contagious then airborne viruses and bacterium such as Tuberculosis and SARS that have a quanta rate, or risk of transmission through airborne means, that are 6 to 120x more pathogenic (Qian, Li, Nielsen, & Huang, 2009). The influenza virus shows the significant impact a lower risk virus can have on people in patient rooms due to design and ventilation Background and Need The Affordable Care Act includes provisions designed to transform the healthcare delivery system, moving it away from fee for service to a coordinated care effort. The fee for service incentives fail to promote reductions in the occurrence of hospital-acquired infections and never events. By limiting reimbursement to hospitals for services needed due to hospital-acquired infections an incentive is created promote infection control and coordinated care (Koch &Sahni, 2010) Background Literature Studies have been done proving influenza as an airborne threat within hospitals, as well as pathogen spread through the air (Blachere et al., 2009); (Brankston, Gitterman, Hirji, Lemieux, & Gardam, 2007). However, there have only been a few significant studies showing 2

11 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 focusing on ventilation system s impact on airborne transmission in the built environment, published between 1960 and Of these forty studies, only ten were deemed conclusive by a team of multidisciplinary reviewers. These conclusive studies 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). More recently a study using only computational fluid dynamic modeling by Khankari, tested two mechanical placements in patient rooms to show air flow patterns (Khankari, 2015). Information garnered from this study may be significantly furthered by looking at two common patient room configurations and using actual measurements and computational fluid dynamics to test the efficiency of ventilation placement when looking at room design. This new data will significantly increase our knowledge on where and how supply air and return air should be handled when considering design, not to only protect the patient from airborne disease but the caregivers as well Research Need The detrimental impact of HAIs (Healthcare Associated Infections) in general and Influenza A in particular may lead to more prescriptive measures in building codes for healthcare facilities, including the FGI Guidelines for Design and Construction of Hospital and Outpatients Facilities and American Society of Heating, Refrigeration and Air-Conditioning Engineers. The study will indicate if current standards are effectively protecting populations in healthcare settings and show gaps in research as well as correct application of ventilation systems. One advantage to this approach is that these design recommendations can be implemented before disease transmission starts to occur in a hospital, and can then remain as the base of the risk 3

12 reduction hierarchy of control while other measures such as effective handwashing can remain as secondary personal safety measures Objective The study is intended to identify if the current standard is adequate for different patient room configurations and sizes, as well as ventilation configurations. Additionally it addresses the causal impact on the transmission of Influenza A pathogens by airborne means in patient rooms Scope of the study Chapter one covers general airborne infection control concerns and the premise of the study. Chapter two looks at influencing factors of air quality and infection control in healthcare settings. This literature review is comprised of codes and regulations that dictate the built environment, ventilation studies in patient rooms, patient room design, influenza and risk assessment models. This chapter sets the boundaries of the research including the patient room designs tested and ventilation standard tested. Chapter three explains the methodology used which includes CFD modelling. The CFD modelling program was validated using a nursing simulation room at Kent State University Trumbull Campus in Northeast Ohio. Due to time limitations, two days worth of data were collected and used in order to set boundary conditions and calibrate the CFD model for validation. Chapter four includes the results of the simulation room used for validation and the tested patient rooms. The results are explained as well as shown visually through an age of air contouring image of each room design. This chapter also includes a comprehensive table of risk assessments of influenza modeling the likelihood of infection based off of the age of air in the different patient rooms. 4

13 Chapter five discusses the implications of the study, as well as the limitations. More ventilation placements and schemes could be analyzed if time permitted. The literature review and the scope of this study was defined between September, 2014 and May, The breadth of the thesis research, and analysis was completed from October March,

14 2.1. Introduction CHAPTER II: LITERATURE REVIEW This study focuses on inpatient hospital rooms and the prevention of airborne diseases, specifically Influenza A, from transferring to the patient, or any other person spending time in the room. Other airborne threats like tuberculosis, and SARS will also be mentioned due to the large amounts of literature looking at the specific airborne risk associated with the diseases. It should be noted that combination airborne infectious isolation/protective environment (AII/PE) rooms are not being included in this study Regulatory Codes Healthcare facilities are regulated by two main institutes, the Facilities Guidelines Institute (FGI), and American Society of Heating, Refrigeration and Air-Conditioning Engineers frequently referred to as ASHRAE. The minimum standards for design and construction of patient rooms is primarily regulated by the FGI Guidelines for Design and Construction of Hospital and Outpatients Facilities, a product of the Facilities Guideline Institute, while ASHRAE provides specific standards in ASHRAE 170 for mechanical ventilation for patient rooms ASHRAE HVAC systems influence the distribution patterns of airborne particles by diluting or concentrating them, accelerating or decelerating the rate of growth of airborne bacteria, or moving them into or out of the breathing zones of prone persons (HVAC Design Manual for Hospitals and Clinics, 2013). However, there are few regulations allocated by ASHRAE currently. ASHRAE recommends four (4) ACH and two (2) outdoor ACH, with a maximum 6

15 relative humidity of 60% and a design temperature of 70 F -75 F. There are no pressure requirements for patient rooms and adjacent spaces as per ASHRAE 170 ( ASHRAE 170 Ventilation Of Healthcare Facilities, 2013). Many studies focus on the required air changes per hour and how it dilutes the concentration of possible infectious agents, however ACH does not increase ventilation effectiveness. A study completed by Memarzadeh and Xu demonstrates the multiple factors, other than ACH, that have impacts on ventilation efficiency and infectious agent transmission rate including air input and output placement, and spatial considerations (Memarzadeh & Xu, 2012). The few regulation that ASHRAE provides have to be tested in order to understand their potentially detrimental impact on a person s interaction with airborne microorganisms within a patient room without regard to design or other factors FGI Guidelines for Design and Construction of Hospitals and Outpatient Facilities The FGI Guidelines offer performance-oriented minimum requirements as suggested standards for patient room design within hospitals and outpatient facilities. The Guideline is broken into four parts including General, Hospitals, Outpatient Facilities, and Ventilation of Health Care Facilities. In part one general risk assessments are outlined including a safety risk assessment, infection control risk assessment and patient handling and movement assessment. These assessments are intended to identify hazards and risks and prevent underlying conditions of the built environment from having adverse safety affects. For the purpose of this paper, part 2 Hospitals was referenced for a standard room design used in the CFD modeling. The FGI Guidelines allow semi-private rooms, that is, patient rooms with two patient beds, but it suggests private rooms in intermediate and medical/surgical nursing units (FGI Guidelines, 2014). Due to the suggestion of private rooms and the trend in the field to implement private rooms over semi- 7

16 private rooms for disease prevention measures the study focuses on private room configurations. For the purpose of this paper part 2 Hospitals was referenced for a standard room design used in the CFD modeling. The FGI Guidelines allow semi-private rooms, that is, patient rooms with two patient beds, but it suggests private rooms in intermediate and medical/surgical nursing units (FGI Guidelines, 2014). Due to the suggestion of private rooms and the trend in the field to implement private rooms over semi-private rooms for disease prevention measures the study focuses on private room configurations. Other studies have looked at semi-private room infection transmission compared to private rooms (Qian et al., 2009) Hospital-Acquired Infection Hospitals serve a vital role in communities around the world; however a large problem facing the practice of medicine in general and patient wards are Healthcare-Associated Infection (HAI). For every 25 patients that go to receive medical treatment at hospitals one (1) acquires a disease or infection from the hospital, either from another patient or an airborne pathogen. This number does not include the employees who may also many times acquire diseases while working among the patients and 17 states that do not require hospitals to report HAI cases (Blachere et al., 2009). While the number of infected is large, the main concern is the resulting number of deaths from the infections acquired at the facilities. In 2011 the estimated number of infections acquired from long term stays in hospitals was millions. Based on those estimates from the CDC, infections of only long-term care patients (defined by Medicare as patients who have a stay of 24 days or longer) may have resulted in 23,100 to 70,000 deaths that year in the United States (CDC, 2011). As of 2009 many public and private insurance companies stopped covering HAIs due to their preventable nature and the fact they most often, result from hospital mistakes. The financial burden to cover the patient costs related to these infections now rest upon the hospitals 8

17 themselves, resulting in $33 billion in excess medical cost every year (CDC, 2011). These avoidable complications are estimated to add an average of 9.4%- 9.7% to inpatient costs per year (Fuller, McCullough, Bao, & Averill, 2009). It is important to note that this number includes a large range of HAIs, not just respiratory infections from bacteria and viruses. The Center for Disease Control recommends HAI prevention measures and supports States and local organizations to push these measures as well as creates incentives through funding opportunities. The prevention measures listed now by the CDC are physical measures including extensive cleaning, hand washing and vaccination. Little to no emphasis has been placed on the buildings capabilities for reducing and eliminating HAI. Influenza has been seen in many hospital outbreaks and usually occurs during annual peak season for influenza activity. Patients, healthcare workers and visitors are all sources of the infection and can cause nosocomial influenza outbreaks. In a report by Eibach and others, an influenza outbreak was monitored in order to clarify the transmission chain and find preventable measures (Eibach et al., 2014). Findings suggest infected healthcare workers substantially increase the impact an influenza outbreak in a hospital can have not only for the patients, but the healthcare delivery system as well (Devereaux, 2015)(Farr, Hall, Hayden, & Salgado, 2002). The prevention measured focused for this paper will be utilizing and understanding inpatient room design s impact on ventilation within patient rooms and looking at ASHRAE s requirements to determine if they are sufficient to help prevent HAI s (Schaffer, Soergel, & Straube, 1976) Influenza Seasonal influenza leads to a large number of nosocomial infections. In this research the most common strain of influenza, H3N2 (Influenza A), commonly known as the seasonal flu will be used. This strain will be the pathogen used to document results based on its yearly influence 9

18 in hospitals. Influenza A has progressively since its first appearance in humans in the 1300 s. The evolving strains result in failing vaccinations due to miscalculated assumptions of locally persisting strains or strains seeded from different regions (Russell et al., 2008). Not only is Influenza A still a persistent problem yearly, but other strains including H1N1 are becoming increasingly important to consider, although they have not been seen to transfer through the airborne route. The transmission of Influenza A has been seen in predominantly direct and indirect contact as well as droplet transmission through coughing and sneezing. It has not been until recently that the theory of transmission for influenza through the airborne route has been more readily accepted. The understanding of pathogen transmission is important in order to address and develop preventable measures for healthcare facilities (Brankston et al., 2007). Influenza strains vary based on their surface proteins, hemagglutinin (H) and neuraminidase (N). There are 16 H subtypes and 9 N subtypes that make up various strains like H1N1 (Swine Flu), H5N1 (Avian Flu) and H3N2 (Hong Kong Flu). In a 2011 study of 11,282 patients, 504 (4.4%) acquired an HAI. Of that, 64% patients were infected with pneumonia or lower respiratory tract infection which many times is the secondary cause after influenza (Magill et al., 2014)(Farr et al., 2002). Influenza associated deaths are often a result of pneumonia and occur predominantly in the elder population or 65 years of age and older (Farr et al., 2002) Airborne Influenza The transfer of the influenza virus is seen primarily as direct contact or contact with contaminated objects. Therefore, the CDC prevention measures focus primarily on isolation and sanitation, not ventilation. Influenza s main route of transmission is by contact or droplet spread, however it can opportunistically be airborne, a route that has yet to be studied thoroughly. The 10

19 relative impact of airborne dissemination is challenging to measure due to the large number of factors that have been found to influence airborne transmission. These include, temperature, humidity, and survival rate of microbes (Jesse Jacob, Altug Kasali, James Steinberg, Craig Zimring, 2013). In a study measuring the amount of airborne influenza virus present in an emergency department, 53% of the detectable influenza virus was in the respirable aerosol fraction. In order to be considered an airborne virus, the particle size must be between 1-4 µm. The airborne particles are small particles evaporated from larger droplets that have settled. A study using an Occupational Safety and Health 2-Stage Cyclone aerosol sampler measured how long influenza virus particles remained in the air. This sampler filters out large particles (> 4 µm) in the first phase, medium (1-4 µm) particles in the second stage and lastly particles less than 1µm in a filter (Blachere et al., 2009). This study found that influenza particles can be transmitted through the airborne route and that many particles stay in the respirable aerosol fraction (< 4 µm). Due to influenza infecting the lining or columnar epithelium cells of the respiratory track, it is seen as both an upper and lower airway infection (Taubenberger & Morens, 2008). It is worth noting that while 50% of influenza infections are asymptomatic, the carrier can still infect others (Carolyn Bridges, Matthew J Kuehnert, 2003). Thus, prevention measures should always be taken place and reliance on isolation rooms can no longer be the only means of prevention, especially during the seasonal flu period. Other measures that need to be considered are ventilation systems or controlling the flow and quality of air in order to filtrate, decontaminate, and minimize exposure time (Jesse Jacob, Altug Kasali, James Steinberg, Craig Zimring, 2013). The speed and devastation of the virus spread can be seen in Moser s study. Moser reported an influenza outbreak on a Boeing 737 where 25 of the 54 passengers contracted 11

20 influenza from one passenger who became ill within 15 minutes of boarding. This early study illustrates the potential threat airborne influenza transmission has on a population as well as the importance of air changes in relation to pathogen transmission (Moser et al., 1979). It has also been found that infection may be spread through tidal breathing, or the amount of air used when at rest and not limited to becoming airborne through the evaporation of droplets. Twelve subjects who were confirmed to have Influenza A or B through the rapid flu test provided exhaled breath filter samples. Of those samples, 4 subject s exhaled breath held influenza virus RNA. The concentration varied from those subjects from 48 to 300 influenza RNA particles per filter, or 3.2 to 20 influenza virus RNA copies per minute. This suggests that influenza can readily transfer through the airborne route even when not all influenza RNA particles are contagious (Fabian et al., 2008). Lastly, pathogenicity of the influenza virus can vary as well as the survival rate of the microbe. The relative humidity guideline in ASHRAE 170 calls for a maximum 60% relative humidity when studying influenza in the air. However, while many completed studies conclude that low humidity allows for longer pathogen survival rates, contradicting results have been found when looking at medium to high relative humidity. The contradictory results suggest that influenza has a moderate survival rate at high humidity or a low survival rate as an aerosol particle due to quick settling rates. For the discussion of humidity, the low range is defined as 0-39% RH, Medium 40-60% RH, and high 60-80% RH (Schaffer et al., 1976; Weber & Stilianakis, 2008). Many investigators have found that the aerosolized influenza virus survives well at low relative humidity due to the ability for droplets to almost instantaneously shrink in size because of evaporation, however the effect of medium and high humidity have yet to be agreed upon 12

21 (Weber & Stilianakis, 2008). Schaffer suggests that aerosol particles are least stable at moderate humidity and accordingly inactivate fastest at around 40-50% humidity, but recover faster in high and low relative humidity (Schaffer et al., 1976). Others propose that high relative humidity causes droplets to have a greater settling velocity and thus accelerating the removal rate of pathogens from the air (Yang & Marr, 2011). Another study disregards relative humidity completely, implying that it is not an influential factor on influenza virus transmission and survival, asserting rather it is absolute humidity. The relative humidity is the measure of how close the air is to moisture saturation in relation to temperature, while absolute humidity is the amount of water vapor in the surrounding air factors dependent of temperature (Shaman & Kohn, 2009). Shaman and Kohn s study of absolute humidity show the similar patterns of seasonal flu activity and absolute humidity seasonally. However, more studies need to be completed in order to address the appropriate range for absolute humidity in hospitals as well as the direct effect on inactivation of viruses like influenza. Regardless of the many factors that can influence the survival rate and transmission of airborne influenza it is seen as a growing and predominate threat in healthcare settings. The potential for outbreaks and pandemics due to mutating influenza strains and other infectious airborne diseases needs to be addressed with preventative action including active design strategies that can help negate virus transmission Other Airborne Threats While seasonal influenza is used as a primary factor for this study, other diseases that are transmitted through the airborne route can also be detrimental to healthcare settings if the recommended ventilation guidelines are not adequate. Influenza was selected for this study due to the seasonal occurrence of the virus as well as the multiple outbreaks reported in hospitals. 13

22 Other studies have looked at the risk and impact of diseases such as measles, tuberculosis and SARS through aerosolized particles (Beggs, Shepherd, & Kerr, 2010)(Li et al., 2007). These infectious diseases cause social and economic concerns associated with an outbreak; for example the SARS outbreak killed more than 700 people, spread rapidly to multiple countries and cost $18 billion (Aliabadi, Rogak, Bartlett, & Green, 2011). Tuberculosis is known to spread rapidly as a aerosolized particle and consequently has been a factor in hospitals initializing HEPA filters in ventilation systems, as well as Ultraviolet Germicidal Irradiation (UVGIs) as preventative measures (Gammaitoni & Nucci, 1997). These additional measures help reduce transmission spread through ventilation systems, however they do not address the pathogen transmission once it has contaminated air within a patient room Risk Assessment Risk assessments have been implemented to consider airborne pathogen transmission risk from person to person. There are two existing mathematical models that describe the circulation of airborne contagions indoors, however many studies have modified the equations creating a vast number of models that take on different considerations. The first model was developed by William Wells in 1955 who also introduced the quantum theory which is a basis for determining infection probability. The quantum theory determines pathogen s quantum rate, or infectivity rate, for 63% of susceptible and exposed people (Wells, 1955). The quantum rate considers many factors including the physical measure of infectious material present, which comprises quantity and pathogenicity of the particle as well as the number of susceptible persons present (Wells, 1955). While Riley et al. assumes that the biological decay of a pathogen could be neglected, the biological decay of the airborne pathogen during aerosolization is calculated in the quanta rate for that particular pathogen (Riley, Murphy, & Riley, 1978) 14

23 The second model elaborated on the Wells model to consider the probability of a susceptible person becoming infected by inhalation of the quanta. This modification of the Wells model by Riley et al. in 1978 established the Wells-Riley equation, which is more applicable to many diseases. The Wells-Riley interpretation is more applicable because, unlike the original interpretation, it considers that not all inhaled particles will infect a susceptible person (Sze To & Chao, 2010). The Wells-Riley equation considers pathogenicity, as well as susceptible and infected people and like the original model assumes a well-mixed room and a steady state of particle concentration. A limitation of steady state particle concentration does not consider ventilations effect on the dispersal and removal of infected pathogens (Riley et al., 1978). Subsequently, Gammaitoni and Nucci modified the Wells Riley equation to consider a time-weighted average pathogen that incorporates a non-steady state condition (Gammaitoni & Nucci, 1997). This modification allows for air change rate or disinfection rate to be considered, but still assumes a well-mixed model. Despite other modifications that can be used, including Rudnick and Milton s modification that uses exhaled air volume fraction to determine quanta, the Gammaitoni and Nucci as well as the Wells-Riley models are used in this study to assess infection risk (Rudnick & Milton, 2003). These two models implicitly consider the influential factors that are being assessed in this study including air ventilation rate and room size. The Gammaitoni-Nucci is used in addition to the Wells-Riley model to illustrate the importance and influence of spatial distribution of airborne particles on risk of infection Ventilation Systems and Design Thoughtfully designed ventilation can be an effective method at dispersing airborne particles with consideration to furniture and HVAC intake and outtake placement. Natural 15

24 ventilation, while used in a case study in the tropics, is not a possible consideration in the United States at this time due to the lack of control over pathogen infiltration (Yau, Chandrasegaran, & Badarudin, 2011). While ASHRAE requires mechanical ventilation in all functional spaces, other countries promote the use of natural ventilation in all areas besides principle medical treatment area (Aliabadi et al., 2011). In order to accommodate the potential benefits natural ventilation could provide, ASHRAE requires a minimum of 2 fresh air ACH (Air changes per hour), along with a minimum of 4 ACH that can be recycled air from indoors. More air changes in a patient room can reduce ones risk of infection due to the dilution of particles. This reduces the exposure time of a pathogen generated within the room assuming a perfectly mixed room (HVAC Design Manual for Hospitals and Clinics, 2013). The ventilation system has to consider multiple factors in order to effectively design and place input and output air sources, including door placement and movement, corridor impact, and human movement. Thus while mechanical systems can dilute particles, if outside factors and room design are not considered, it can also spread the pathogens in unintended airflows. ASHRAE 170 gives three options for supply placement in patient rooms, defined as Group A, Group D, and Group E. Group A requires outlets to be mounted in or near the ceiling that discharge air horizontally, group D requires outlets mounted near or in the floor discharging air horizontally, and lastly group E requires outlets located in or near the ceiling that project air primary vertically. There have been a few studies looking at the placement of supply and return outlets in relation to particle transmission with all assuming a well-mixed room. Many also assume the patient as the infector as the majority of rooms studied are not typical inpatient rooms, but rather 16

25 isolation rooms. Ceiling mounted return ducts are seen as the most practical, not floor level return ducts, as laminar flow from downward ventilation is not achieved. (Qian & Li, 2010). Supply air is generally located over the patient bed much like the required placement of supply air in protective environment rooms. This allows for filtered air to enter from one side of the room, flows across the patient's bed, and exits from the opposite side of the room. Displacement ventilation could be a strategy to reduce infection, as it introduces clean cool air near the bottom of a room and relies on the surrounding warm objects to cause the air to rise and be removed from the room through return grilles located at the ceiling. This method is not allowed by ASHRAE 170 due to the focus of diluting the air, however it is suggested by the Center of Disease Control (Aliabadi et al., 2011). A study done by Kishor Khankari, using the now accepted chilled beam system evaluated patient room airflow and the most effective air return placement. The results showed the placement right above the patient bed would be most effective however, again this study was assuming the patient was exhaling the pathogen (Khankari, 2015). Studies similar to the ones described above need to be conducted in order to show the significance of hallway traffic, door movement and human or equipment movement in and out of a space have on the air flow within a room. Furthermore, individual studies need to be completed for specific cases, due to the influence of room configuration, ventilation placement and size on airflow (Mendez, San Jose, Villafruela, & Castro, 2008) Filtration Generally speaking clean air can be introduced into patient rooms from outside air and filtered air. The United States standards require a combination, 2 outdoor ACH and filtration of the other 4 mandatory ACH. ASHRAE uses a minimum efficiency reporting value (MERV) rating to rate the effectiveness of air filters. ASHRAE 170 requires inpatient care to have two 17

26 filters, Filter bank number 1 has a 7 MERV rated filter, while the filter bank number 2 has a 14 MERV rated filter. The filter bank 1 is placed upstream of the heating and cooling coils while the second filter bank is installed downstream of the supply fan. Other areas, based on function, require higher rated filters like the use of high-efficiency particulate air (HEPA) filters, equivalent to 17 MERV rating. The HEPA filters allows for the recirculation of indoor air, and the prevention of microbes to travel through the mechanical airways into protective environment rooms or airborne isolation rooms (Aliabadi et al., 2011). However, filters with a higher MERV rating also need to be cleaned and replaced on a regular basis, occasionally medium MERV rated filters are substituted to offset the high operational cost of replacing and cleaning the higher rated MERV filters (Aliabadi et al., 2011). The filters can adequately prevent transmission of microbes of >.5 µm through the ventilation systems, however again does not address pathogens that are introduced in the room or adjacent spaces (Bolashikov & Melikov, 2009) Ultraviolet germicidal irradiation Ultraviolet germicidal irradiation (UVGI) is similar in use to the filtration systems in place, due to it primarily targeting pathogens entering the room through the ventilation system or upper portion of the room. The UVGI system releases ultraviolet light that damages the DNA/RNA of many pathogens ultimately making them harmless. However the effectiveness of UVGI is primarily based on the wavelength or frequency and exposure time, which is restricted due to potential health impacts the light can have on occupants (Bolashikov & Melikov, 2009). Therefore, the effectiveness of UVGI is still questionable due to the limited places it can be placed, including ceiling mounted or in-duct applications, as well as the intensity of UV light it can emit. Other factors affect the efficacy of UVGI use as well, including humidity and air velocity and lack of penetration into surfaces (Kujundzic, Hernandez, & Miller, 2007). 18

27 2.9. Acute Patient Rooms Patient rooms have gone through a great flux in the past 10 years; while once multi-bed wards were common, single patient rooms with private bathrooms are now the standard. It is important to understand the impact that the configuration of the room has on all users including the patient, visitors, nurses, doctors, therapists and cleaning staff. The trends are in direct relation to considerations including patient safety, staff efficiency, circulation, infection control, patient consideration, and family amenities (Pati et al., 2009). Studies have primarily reflected patient outcomes and are limited when addressing staff related outcomes (Lavender et al., 2015). A study completed by Pati and others, asked participants, including nurses and patients, to rank design issues by importance. Access points to patient and bathroom were seen as important considerations, while standardization and visual privacy from corridor were seen as less important in relation to the other listed concerns (Pati et al., 2009) Size Medical/Surgical Nursing Unit patient room size is dictated by FGI guidelines and are required to be 120 sf. Many patient rooms tend to be larger to allowing additional visitor space as well as space around the patient bed (Lavender et al., 2015). Another trend that is increasing room size significantly is the move to acuity adaptable or universal rooms, where patients could stay regardless of condition. These rooms serve a patient through their whole stay from critical condition to release. The use of acuity adoptable rooms looks to address the shortage of nurses, increased use of emergency department and increased hospital occupancy rates. This can allow for rooms use to fluently change and accommodate the large number of patients and healthcare related problems. Some of the healthcare related 19

28 problems that acuity adaptable rooms can address are more constant staffing, fewer patient falls as well as less terminal cleaning due to fewer patient transfers (Hendrich, Fay, & Sorrells, 2004) Layout There are many factors that can influence the layout of patient room. A primary concern during design is the placement of the bathroom within the patient room. The bathroom can either be located on the headwall, the wall where a patients head will be - or foot wall, the wall closest to the patient s feet. Besides the bathroom being located in relation to the patient s head or feet, the bathroom may be located on the façade wall of the building (outboard) or the interior corridor wall (inboard). There are a few other configurations including midboard, where bathrooms are clustered near the midpoint of the room. Each configuration has its benefits to the patient and caregiver as well as its negative impacts. While caregivers may prefer outboard configuration, due to increased visibility of the patient, the patient may feel like their privacy is diminished. There is a significant lack of empirical evidence to support one layout over another. Many layouts are chosen by stakeholder perspectives and are not articulated in relation to available literature (Pati et al., 2009). Overall, there is a lack of research to determine the right size, layout and organizational content that allows for patients as well as occupational stakeholders to work effectively and provide a healing environment (Lavender et al., 2015) Conclusion The impact of airborne influenza and other transmissible airborne diseases in the healthcare system is a large concern, especially when acquired in the hospital. While there are measures in place to address airborne disease, like the use of UVGI and filtration within ventilation systems, there are few preventative measures in place to address the transmission of airborne diseases within patient rooms. 20

29 Despite the ability of ventilation to dilute pathogen density, it can also cause detrimental airflows that may create stagnant air and increase risk of infection. Many factors have to be considered in order to fully understand the transmission of certain airborne infectious diseases as well as the inactivation of them. In order to fully comprehend how to prevent transmission, a multi-level study will have to comprehensively address ventilation, patient and others movement, design factors and the pathogen itself, individually as well as in relation to each other. The following completed study looks at one ventilation layout in relation to two room layouts and how those two primary factors would affect the transmission of an airborne pathogen. 21

30 CHAPTER III: METHODOLOGY 3.1. Introduction Research design is imperative and requires evaluation independently of results to confirm the results credibility. Therefore, this chapter is concerned with the methodology chosen to gather results as well as the impact it has on the process and outcome of the research. The selection of room design and size serve as a base to the research finding and are discussed first, while the verification of the simulation program follows. The verification process included TSI indoor air quality meters to compare actual room data to computed room data through the chosen simulation program. Finally, the risk assessment equations used in the later part of the study to understand infection risk of people in a patient room is discussed in detail to allow the reader to understand the differences between the three risk models that are used and compared. The design of the research was focused on the computational fluid dynamic outputs and using that data to acquire actual data including air age, velocity, and airflow efficiency in relation to room design. This use of Computation Fluid Dynamics (CFD) was chosen because of its ability to run many comparative simulations within a shorter time frame and low cost compared to mock ups or other methods. CFD also provides detailed flow patterns and air distributions within a space, and the calculations can include other processes such as heat transfer from surfaces and transient behavior. The transient behavior like human equipment and door movement is not focused on in this study, rather the concentration of the study is on the produced airflow patterns and distribution within the selected spaces. Potential causes of error could be 22

31 caused by the chosen simulation program s visual output rather than data driven output leaving room for translation error between the visual representative and inferred data. The details of the methodology are explained in detail in this chapter Room Selection The geometries selected and tested in this paper correspond to the general trends in the healthcare design field. Despite the large variation in room finishes, room geometries are relatively constant thus the same geometrical room shape with differing bathroom locations and sizes were used for simulations. Different room sizes were selected to see if there would be a significant difference in airflow efficiency. Room sizes have started to vary due to new trends that encourage more family and visitor areas within patient rooms as well as acuity-adaptable rooms requiring more space for varying equipment and staffing needs (Brown & Gallant, 2006). The room sizes reflecting these trends are as followed 21.4 m 2 (230 ft 2 ), 27.9 m 2 (300 ft 2 ) and 34.8 m 2 (375 ft 2 ) including bathrooms. The patient rooms used for the simulation follow the FGI Guidelines for Hospitals and Outpatient Facilities 2014 for medical-surgical rooms and intermediate care, therefore each room tested in the simulation program has a private bathroom, and glazing 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 simulated also has more than the minimum required square footage. The ventilation placement as well as amount of air supplied are again retrieved from the ASHRAE 170 minimum allowable. Despite patient rooms having three allowable placement of supply air, this study only focuses on one option for supply and return air. The chosen supply placement is ceiling mounted above the patient bed, with return located near the door, mimicking PE Room 23

32 ventilation strategy. This placement is frequently used to create airflow away from the susceptible patient (Aliabadi et al., 2011). The simulation models representing hospital patient rooms include an inboard or outboard bathroom and are rectangular in shape. Inboard rooms have the bathroom located along the same wall as the entrance to the room, while outboard rooms have bathrooms located along the exterior wall. The bathroom doorways are placed on the headwall, or the wall where the patient s head is located nearest. Supply air is located above the patient bed and return located near the door of the patient room. While ASHRAE allows for three different placements of supply air, the design tested in this study was chosen due to its regular use in healthcare facilities as it is a predominate design for protecting patients against airborne risks (Khankari, 2015; Qian et al., 2009). The air supply diffuser is a 4-way diffuser, supplying air at 360 degrees and the equivalent to 6 air changes per hour based on room volume. These room layouts as well as are supply and return placements represent general standards required by ASHRAE and the FGI Guidelines, but are not the only allowable layouts for designers. If airflow pattern inconsistencies are found in the selected simulations models then it can be hypothesized that more complex and different layouts can also have a significate effect on airflow and in turn infection risk from airborne diseases Computational Fluid Dynamic Modeling Computational Fluid Dynamic (CFD) modeling solves the numerical solution of the air flow simulation using 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 (Anderson & Wendt, 1995). The equations are linearized and CFD solves them using numerical analysis., An iterative calculation procedure is then used 24

33 where equations are re-linearized until the solution is in the original numerical form (Jones & Whittle, 1992). These equations are solved on a grid or mesh that defines the elements in the control volume or cells. The solution variables are obtained from these points or cells, therefore each cell usually has at least one equation for each cell including velocity from two to three dimensional, pressure, temperature, and turbulence (Jones & Whittle, 1992). CFD simulation focuses on the numerical and algorithm modelling of fluid flow and heat transfer processes (Chow, 1996; Linden, 1999). In order for the program to predict building airflow the boundary conditions must be defined. The boundary condition can be achieved by defining the geometry and the location of supply, and exhaust air as well as flow rates, and temperature of the supply air. The geometry is defined by specifying climate, use and internal energy sources (Jones & Whittle, 1992). With this defined information the simulation can predict the buildings likely internal or external air flow. For this study internal airflow, or airflow within an enclosed geometry will be analyzed rather than external airflow, or the airflow at a buildings surface CFD Modeling The program used, Integrated Environment Solutions Limited Virtual Environment (IES VE 2015), is validated under ASHRAE Standard 140. The IES VE program allows for dynamic thermal simulation, including design load calculations, and simplified computational fluid dynamic modelling (IES VE, 2015). IES VE primarily focuses on integrated graphicsdriven results however, does allow for simple numerical analysis based on points located within the graphic model (IES VE, 2015). IES VE uses the k-epsilon turbulence model to find the viscosity of each grid cell in a 3Dgenerated model. The standard k-epsilon model is widely accepted due to its applicability to wide-ranging flow situations primarily looking at internal flows with weak streamline curvature 25

34 models. The equation solves for the kinetic energy turbulence and the dissipation of the kinetic energy, overall describing the transport and dispersion of energy in small-scale flow (Awbi, 2003; Costola, Blocken, & Hensen, 2009). While the standard k-epsilon turbulence model is widely 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 convention or mixed convection turbulence models while the Large-Eddy-Simulation (LES) model is a more accurate predictor of flows with strong streamline curvatures (Awbi, 2003; Jones & Whittle, 1992) IES VE s MicroFlo 3D Computational Fluid Dynamic modeling application was used to simulate different conditions within patient rooms. The different conditions included are temperature, air velocity, and pressure within the room. Temperature is based on recommendations by ASHRAE and illustrate the efficiency of supply air being circulated around the room. Airflow again based on the supply air depends largely on placement of supply air and the room design to dictate direction and speed of the air. The Airflows large dependency on outside factors listed previously can be seen through the CFD modeling using air flow as a dependent factor and layout, supply placement and other factors as independent influences. 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 evaluation of a building and system design. Thermal comfort conditions were regulated by specifying HVAC system and placement based on ASHRAE 170, 2013 healthcare patient room standards including required air changes per hour (ACH) and humidity. 26

35 The MicroFlo module in IES VE allows for visual representation of airflow as well as the concentration of CO2 and local mean age of air (LMA) within internal spaces. The CO2 concentration can sufficiently be used to measure exhaled breath as well as measure the average ventilation rate (Gilkeson, Camargo-Valero, Pickin, & Noakes, 2013; Rudnick & Milton, 2003) Local mean age is the amount of time a parcel of air has been in certain areas of the simulation space. Higher values indicated areas with inadequate ventilation. The CO2 concentration is primarily used in showing the impact occupants have on room airflow and if ventilation is effective to remove the excess CO2. This study does not look at human impact on airflow thus does not incorporate visual C02 concentration and analysis as part of the Figure 1: (Above) Simulation Room Layout, (Below) Simulation Room ceiling plan study Calibration In order to calibrate IES VE the simulation procedure, an educational nursing simulation room was used for the field measurements to verify that results generated from IES VE. The nursing simulation room is located in Northeast Ohio and had an overall area of m 2 (368 ft 2 ). The field measurements were taken on two days November 2 nd and November 23 rd, The conditions on the two days of data collection varied, November 2 nd was cloudy and 68 F, while November 23 rd was cloudy and 33 F. 27

36 Field Measurements Field measurements were performed in the nursing simulation room; ft. (L) x 21.0 ft. (W) x 10 ft. (H), with the bed located on the same wall as the bathroom are shown in figure 3.1. The air is supplied and returned through linear slot diffusers located at the ceiling (see figure 3.1). The room is illuminated by six 2 x4 fluorescent lights, one located in the bathroom. Measurements were taken at 3 grid Figure 2: Points where measurements were taken intervals at 6, 3 6 and 6 above the floor using TSI indoor air quality meters model 8386A and 8760 to gather temperature, humidity, carbon dioxide and air velocity at nine specific points (see figure 3.2). The boundary conditions that would be generated in IES VE were also collected like wall surface temperature, outside temperature and humidity. After gathering this information it was compared and used to verify the CFD simulation of that constructed room IES VE run of nursing simulation room The simulations room CFD model was assigned the boundary conditions collected from the field measurements including outside temperature as well as surface temperatures. The supply air was delivered through slot diffusers equivalent to 360 cfm, approximately 6 ACH. The simulation room also used perimeter radiators located on the exterior walls, causing higher surface temperatures on the respective walls CFD Runs of Patient Rooms The selected six room designs for patient rooms were tested through IES VE. Much like the simulation room data from the CFD modeling, the data for the patient rooms were collected through the micro flow module at three specific heights.15 m (6in.), 1.1 (3.5 ft.), 1.8 m (6ft.). 28

37 Each room was supplied with 6 ACH, the required ACH per ASHRAE 170. In order to achieve the 6 ACH the following velocity was needed for the supply air: 252 cfm for the small, 315 cfm for the medium, and 400 cfm for the large patient room (Table 3.1). The results all pertain the same climatic influence, boundary conditions that coincide with February 8 th. Each result is reflecting sixty minutes of time, the length of time for the required amount of air changes to take place. Variations in the results will then be reflecting the layout and size influence not varying outside influences like differing air supply placement and outdoor temperature. Table 1: Boundary Conditions for Patient Rooms Boundary Conditions Flowrate (CFM) Temperature Small Room Medium Room Large Room Data Analysis After the validation of the software used, the geometries representing different patient room sizes and layouts were tested using IES VE. The data generated from the simulation or equations were velocity, temperature turbulence for each of the 500 iterations. This data was then averaged for each level of the room, 0-6, 3-6, and 6-0. This data can be compared to each other and start generating standard deviation and variance for each run and set of data points. The visual outputs of local mean age of air and velocity was interpreted and re-processed into contour maps illustrating the visual outputs in a numerical way. The contour maps were processed using a program called Rhinoceros. Rhinoceros is a software focused on 3D computer graphics and computer-aided design (Rhino3D). Within Rhino the commands heightfield and 29

38 contour were used to take the images produced from IES VE and create a topographical map dependent on color discrepancies. The corresponding LMA contours can be used in the risk assessment equations to understand the risk associated with in each room and different zones. However, it also shows if the rooms are being adequately ventilated and mixed as per ASHRAE standards. Differences in each rooms that can be used in the risk assessment equations 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; Gammaitoni & Nucci, 1997). The Gammaitoni-Nucci equation is a modified version of the Wells-Riley equation that reflects the exponential increase in some new cases of infections in a room. The Gammaitoni- Nucci equation takes into consideration the transient behavior of transmission over shorter periods of time, and that not every newly infected person may infect another due to their short time in a particular space (Gammaitoni & Nucci, 1997; Hethcote, 2000). The equation considers room volume, ventilation rate, and the quanta production rate. The quanta production rate the infectious dose required to infect 63.2% of the people in the affiliated enclosed space (Gammaitoni & Nucci, 1997). The quanta rate is a vital indicator of a virus s pathogenicity and infectivity defined by William Wells in 1934 (Wells, 1934). The quantum is the number of infectious droplet nuclei required to infect 63% of exposed individuals and have now been used extensively in assessing airborne virus risk (Wagner, Coburn, & Blower, 2009). 30

39 There is still dispute over an acceptable range of quanta production rate for influenza, as found by Rudnick and Milton it can vary from q/h depending on which steady or nonsteady state Wells-Riley equation used to determine the quanta production rate. For this study, the mean quanta production rate for influenza that is used for this study is quanta/h, a value for a highly contagious case (Rudnick & Milton, 2003). The equation assumes ventilation as a large factor in particle removal while it is a significant source, many other sources also factor in pathogen settlement(rudnick & Milton, 2003). For Influenza A, a significant factor in pathogen removal is inactivation through settling. Despite the pathogen being removed from the air it is not removed from the space entirely. The risk equations do not take into consideration that outside factors such as temperature, humidity and settling can have different impacts on a person s risk of airborne infection Conclusion The above methods discuss how the study was implemented and what programs were used in order to gather data. While IES VE completed the needed computational tasks, other CFD modeling programs could more efficiently output data rather than focusing on visual representation of the result. If this study was to be restricted a more powerful program could show detailed airflow patterns and eddies within patient room layouts instead of generalized airflow. Despite the criticism, the methods can easily be repeated in other building or room types, as well as revealed that CFD modeling can be easily implemented in any building project in order to compare projected air flow patterns and actual patterns. 31

40 CHAPTER IV: RESULTS 4.1. Introduction In this chapter, the results from the methods described in the previous chapter will be presented. The results include the simulation lab CFD results and validation of the IES VE method as well as the CFD results from the different patient rooms selected. Each CFD run resulted in velocity quantities and local mean age of air approximations that differed based on room size and layout, inboard vs. outboard. The LMA and known volume 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 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 1 shows the averages for each differing height level for the simulation room CFD model compared to that of the field measurements. Table 2: Simulation Room CFD Model compared to field measurements Height Average Velocity - Simulation Average Velocity - Field Measurements ft.-in. ft. /mins. ft. /mins. 0'-6" '-6" '-0"

41 4.3. Room Difference Size The selected rooms varied in three sizes considered small, medium and large. Figures 1-6 show the differences of air age in the different patient rooms. Overall, the size did not have a large impact on velocity and air age, rather design had an impact on the local mean age of air at different areas within the room Layout Inboard Patient Room The inboard CFD model shows that the air is circulated 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. Longer local mean age of air is located in the bathroom if the door remains open, resulting in air age of 14.5 minutes. This on average would approximately result in 6ACH per hour, each air change taking on average 10 minutes Outboard Patient Room The outboard patient room is quite different when compared to the inboard patient room s local mean age of air in the space. The CFD model shows that the back zone in the rooms seen in figures 4.1, 4.3, and 4.5 have older air parcels exemplifying bad circulation. This area s air age is on average 15 minutes old, which would be the equivalent of 4 ACH. The area around the patient bed has an average air age of 10.5 minutes which again would approximately result in the required 6ACH. This air change discrepancy is significant: the risk of transmission will significantly increase in the areas of poor circulation. The following mathematical risk assessments show the increased risk associated with the poor circulation. 33

42 Figure 4.1: Local mean age of air in small outboard patient rooms. (Left to right: 0'-6" AFF, 3'-6" AFF, 6'-0" AFF) Figure 4.2: Local mean age of air in small inboard patient rooms. (Left to right: 0'-6" AFF, 3'-6" AFF, 6'-0" AFF) Figure 4.3: Local mean age of air in medium outboard patient rooms. (Left to right: 0'-6" AFF, 3'-6" AFF, 6'-0" AFF) 34

43 Figure 4.4: Local mean age of air in medium inboard patient rooms. (Left to right: 0'-6" AFF, 3'-6" AFF, 6'-0" AFF) Figure 4.5: Local mean age of air in large outboard patient rooms. (Left to right: 0'-6" AFF, 3'-6" AFF, 6'-0" AFF) 35

44 Figure 4.6: Local mean age of air in large inboard patient rooms. (Left to right: 0'-6" AFF, 3'-6" AFF, 6'-0" AFF) Figure 4.7: Air Velocity in small outboard patient rooms. (Left to right: 0'-6" AFF, 3'-6" AFF, 6'-0" AFF) 36

45 Figure 4.8: Air Velocity in small inboard patient rooms. (Left to right: 0'-6" AFF, 3'-6" AFF, 6'-0" AFF) Figure 4.9: Air Velocity in medium outboard patient rooms. (Left to right: 0'-6" AFF, 3'-6" AFF, 6'-0" AFF) Figure 4.10: Air Velocity in medium inboard patient rooms. (Left to right: 0'-6" AFF, 3'-6" AFF, 6'-0" AFF) 37

46 Figure 4.11: Air Velocity in large outboard patient rooms. (Left to right: 0'-6" AFF, 3'-6" AFF, 6'-0" AFF) Figure 4.12: Air Velocity in large inboard patient rooms. (Left to right: 0'-6" AFF, 3'-6" AFF, 6'-0" AFF) 38