Published at : 25 Oct 2018
Volume : IJtech Vol 9, No 5 (2018)
DOI : https://doi.org/10.14716/ijtech.v9i5.2106
|Yandi Andri Yatmo||Department of Architecture, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia|
|Nandy Putra||Department of Mechanical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia|
|M. M. Y. Harahap||Department of Architecture, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia|
|Diandra Pandu Saginatari||Department of Architecture, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia|
This paper addresses the issue of airborne transmission of diseases in relation to spatial layout in health care facilities. In particular, this study analyzes the occurrence of airborne transmission in the waiting areas of puskesmas, a form of primary health care facilities that are distributed in many cities and villages in Indonesia. The study uses computational fluid dynamics (CFD) analysis as a design tool to examine the potential for airborne infection by analyzing the simulated droplet movement in a waiting area layout. The findings of the study identify the distribution of areas where the droplets are likely and unlikely to spread and use this to suggest seating layouts in waiting areas to spatially reduce the potential for airborne infection.
Airborne infection; CFD simulation; Droplet movement; Puskesmas; Spatial layout
1. HEALTH CARE FACILITY AND AIRBORNE INFECTION CONTROL
Health care facilities have been directly contributing both to the process of healing and transmitting diseases. The performance of health care facilities is closely related to their design (Johanes et al., 2015). Facility design, which includes spatial layout, material, and overall environment, plays a vital role in determining the performance of health care facilities, which is always aimed at the prevention of disease transmission and promoting a healing environment.
This paper addresses the contribution of the spatial layout of health care facilities to the transmission or control of health care-associated airborne infections. Many diseases are airborne-transmitted. Patients with a specific condition, such as infectious pulmonary disease, could become potential sources of infection by producing infectious droplets through coughing, talking, or sneezing (Memarzadeh et al., 2000; Gupta et al., 2009; VanSciver et al., 2011). As a result of a complicated process of interaction between pathogens and the environment (Jacob et al., 2013), an airborne transmitted pathogen, as indicated by several epidemiological and simulation studies, has the ability to spread in various distances and settings within health care facilities (Wong et al., 2004; Tang et al., 2006; Blachere et al., 2009).
Certain spaces in a hospital, such as patient and procedural rooms, are pressurized to control the airflow, thus limiting the potential for airborne infection (Ninomura & Bartley, 2001). Meanwhile, other spaces are overlooked, including spaces that are used publicly such as waiting rooms, where sick and healthy people gather, stay, and make contact both directly and indirectly. This paper discusses how airflow performs in waiting areas in a type of health care facility in Indonesia, pusat kesehatan masyarakat (puskesmas). The Ministry of Health of the Republic of Indonesia recorded that as of 2016 there were 9,767 puskesmas — 3,411 of them units with inpatient facilities — in Indonesia (Kurniawan et al., 2017). Puskesmas cater for large numbers of patients and non-patients. The interactions among users may potentially cause the spread of diseases. Therefore, puskesmas spaces might cause healthy people to become infected and sick people to become even sicker. A study to understand the potential for airborne infection in puskesmas is needed to identify preventive actions to reduce the potential for infection, especially through spatial design.
Many studies regarding health care facility-associated airborne infections have suggested that airborne infection could be influenced by many factors, such as spatial arrangement of the building, ventilation system, airflow conditions, air temperature and humidity, material, human body movement, and the movement of architectural features such as doors (Memarzadeh & Xu, 2011; Wang & Chow, 2015; Mousavi & Grosskopf, 2016). These studies, however, were unable to explain how airborne pathogens are actually transmitted. A pathogen’s range of travel or how airflow-related factors influence the movement of a pathogen in the air does not necessarily inform us of the pattern of airborne transmission in space. Some simulation studies used computational fluid dynamics (CFD) (Tung & Hu, 2008) to analyze airborne transmission in several health care settings. The studies explain how the airflow occurs but not how the pathogens actually travel.
The infection potential and the spreading movements of pathogens within health care settings are the basis for the present study. As health care facility design should be based on evidence (Johanes & Atmodiwirjo, 2015), this study aims to collect evidence by using CFD simulation to trace the distribution of pathogens and their movement patterns. Consequently, the result of the simulation study and analysis have the potential to contribute to design considerations. In particular, this study can be used as a reference for redesigning the seating arrangements in the waiting areas of the observed puskesmas, hence contributing to the prevention of health care facility-associated airborne infections.
2. THE USE OF CFD: FROM SIMULATING PATHOGEN MOVEMENT TO EVALUATING SPATIAL LAYOUT
The use of computation in architecture presents the possibility for simulations that could resemble complex environments such as health care facilities (Johanes et al., 2015) and analyze their performance. The use of CFD for architecture especially presents a novel and promising opportunity to discover the conditions of our surroundings (Addington, 2017). In general, CFD simulation can analyze the nature of airflow in certain contexts (Ramdlan et al., 2016). It could generate an accurate prediction of airflow patterns (Yu et al., 2004), and thus becomes an appropriate tool for simulating the pattern of air movement in certain spaces. CFD airflow modeling has been used to perform analyses of airflow patterns and substantial dispersion (Memarzadeh et al., 2000), to predict the diffusion of an airborne contaminant in a space (Xing et al., 2001), and to model a hospital ventilation system (Qian et al., 2010; Colquhoun & Partridge, 2003). The above range of CFD utilization has been made possible because CFD has the capacity to produce various forms of simulation representation.
CFD simulation has the capability to visually present the simulation result by illustrating the details of distribution in the space and the effects of each parameter involved in the simulation (Memarzadeh wt al., 2000). In this study, the simulation representation of particle movement in the air could be useful for studying the potential for airborne infection. If the simulated particle is assumed to be an infectious droplet, the simulation result, through further analysis, would benefit us by providing the potential indication of infection in space.
Furthermore, this indication is closely related to the environmental and engineering control necessary for preventing airborne infection (Atkinson et al., 2009). Environmental and engineering control strategies deal primarily with the physical aspects of a health care building that relate to the health care activities inside the building. These strategies include designing a layout and using technology that could support infection control inside the building. While infection control could be achieved primarily through the use of technology, such as by increasing air changes per hour or employing a specific ventilation system (Fernstrom & Goldblatt, 2013), thoroughly considered building layouts could also contribute substantially to the control of airborne infection. Retrospectively, this is the area in which the indication of the potential for infection that resulted from the CFD’s droplet movement simulation could contribute to airborne infection control. Through droplet movement simulation, one would be able to suggest a building layout that could prevent the occurrence of airborne infection within the building.
The analysis of airborne infection using CFD analysis was performed by simulating the movement of droplets in the air and by calculating the traces of droplets at heights relevant to the human body while standing and sitting. From the tracing of the droplet movement, it is revealed that there is a significant influence of airflow-related spatial objects, such as internal fan, air conditioner, and apertures, on the droplet movement pattern. Moreover, further analysis of droplet movement simulation reveals the potential for each sample location to become the origin point from which a droplet of infectious disease might spread to other parts of the space and results in the zoning of the airborne infection contribution from each part of the space. These findings could be considered as an evaluation of the spatial performance of the simulated puskesmas waiting areas in relation to airborne infection control. The overall findings of this study can form the basis for a reconfiguration of the seating arrangements in the puskesmas waiting areas that promotes the prevention of health care facility-related airborne infection.
There are, however, some limitations in this study. The study only simulates the path of droplet movement, without considering the detailed characteristics of the particle, such as the size of the particle, the duration that particles can remain airborne, the distance that particles can travel, etc. These aerobiological factors should be considered in further research, thus obtaining more detailed information on the occurrence of airborne infection. Further research should also consider the possibility of a different kind of spatial intervention besides the arrangement of seats and should include other variables related to the architectural elements, such as the position of the internal fan and the exhaust fan, the arrangement of window openings, etc. By discovering how these elements contribute to the movement of particles in the air, more information will be gained as a basis for designing a layout for waiting areas that could minimize the occurrence of airborne infection.
This study is part of the research conducted in the Hospital and Healthcare Design and Engineering Research Cluster at the Department of Architecture, Universitas Indonesia and is funded by Ministry of Research, Technology and Higher Education of the Republic of Indonesia under a Multidisciplinary Research Grant 2015.
Addington, M., 2017. The Unbounded Boundary. In: Thermodynamic Interactions: An Exploration into Physiological, Material and Territorial Atmospheres, García-Germán, J. (Ed.), Actar Publishers, New York, pp. 79–87
Atkinson, J., Chartier, Y., Pessoa-Silva C.L., Jensen, P., Li, Y., Seto, W.H. (Eds.), 2009. Natural Ventilation for Infection Control in Health-Care Settings. WHO Press, Switzerland
Blachere, F.M., Lindsley, W.G., Pearce T.A., Anderson, S.E., Fisher, M., Khakoo, R., Meade, B.J., Lander, O., Davis, S., Thewlis, R.E., Celik, I., Chen, B.T., Beezhold, D.H., 2009. Measurement of Airborne Influenza Virus in a Hospital Emergency Department. Clinical Infectious Diseases, Volume 48(4), pp. 438–440
Colquhoun, J., Partridge, L., 2003. Computational Fluid Dynamics Applications in Hospital Ventilation Design. Indoor and Built Environment, Volume 23, pp. 81–88
Fernstrom, A., Goldblatt, M., 2013. Aerobiology and Its Role in the Transmission of Infectious Disease. Journal of Pathogens, Volume 2013(493960), pp. 1–13
Gupta, J.K., Lin, C.H., Chen, Q., 2009. Flow Dynamics and Characterization of a Cough. Indoor Air, Volume 19, pp. 517–525
Jacob, J.T., Kasali, A., Steinberg, J.P., Zimring, C., Denham, M.E., 2013. The Role of the Hospital Environment in Preventing Healthcare-associated Infections Caused by Pathogens Transmitted through Air. Health Environments Research & Design Journal, Volume 7(Suppl), pp. 74–98
Johanes, M., Atmodiwirjo, P., 2015. Visibility Analysis of Hospital Inpatient Ward. International Journal of Technology, Volume 6(3), pp. 400–409
Johanes, M., Yatmo, Y.A., Atmodiwirjo, P., 2015. The Use of Computational Medium for Visualization and Simulation in Healthcare Architectural Design. In: Proceedings of the 3rd International Conference on New Media (CONMEDIA), Universitas Multimedia Nusantara, Tangerang, Indonesia, pp. 182–187
Memarzadeh, F., Elsworth, P., Jiang, J.Y., 2000. Methodology for Minimizing Risk from Airborne Organisms in Hospital Isolation Rooms. Symposium, ASHRAE-Transactions, Volume 106(2), pp. 731–747
Memarzadeh, F., Xu, W., 2011. Role of Air Changes per Hour (ACH) in Possible Transmissions of Airborne Infections. Building Simulation, Volume 5(1), pp. 15–28
Mousavi, E.S., Grosskopf, K.R., 2016. Airflow Patterns due to Door Motion and Pressurization in Hospital Isolation Rooms. Science and Technology for the Built Environment, Volume 22(4), pp. 379–384
Ninomura, P.E., Bartley, J., 2001. New Ventilation Guidelines for Health-care Facilities. ASHRAE Journal, Volume 43(6), pp. 29–33
Kurniawan, R., Yudianto, Hardhana, B., Soenardi, T. A. (Eds.), 2017. Profil Kesehatan Indonesia Tahun 2016 (Indonesia Health Profile 2016). Kementerian Kesehatan Republik Indonesia, Jakarta
Qian, H., Li, Y., Seto, W.H., Ching, P., Ching, W.H., Sun, H.Q., 2010. Natural Ventilation for Reducing Airborne Infection in Hospitals. Building and Environment, Volume 45, pp. 559–565
Ramdlan, G.G., Siswantara, A.I., Budiarso, B., Daryus, A., Pujowidodo, H., 2016. Turbulence Model and Validation of Air Flow in Wind Tunnel. International Journal of Technology, Volume 7(8), pp. 1362–1372
Tang, J.W., Li, Y., Eames, I., Chan, P.K.S., Ridgway, G.L., 2006. Factors Involved in the Aerosol Transmission of Infection and Control of Ventilation in Healthcare Premises. Journal of Hospital Infection, Volume 64, pp. 100–114
Tung, Y.C., Hu, S.C., 2008. Infection Risk of Indoor Airborne Transmission of Diseases in Multiple Spaces. Architectural Science Review, Volume 51(1), pp. 14–20
VanSciver, M., Miller, S., Hertzberg, J., 2011. Particle Image Velocimetry of Human Cough. Aerosol Science and Technology, Volume 45, pp. 415–422
Wang, J., Chow, T., 2015. Influence of Human Movement on the Transport of Airborne Infectious Particles in Hospital. Journal of Building Performance Simulation, Volume 8(4), pp. 205–215
Wong, T.W., Lee, C.K., Tam, W., Lau, J.T.F., Yu, T.S., Lui, S.F., Chan, P.K.S., Li, Y., Bresee, J.S., Sung, J.J.Y., Parashar, U.D., 2004. Cluster of SARS Among Medical Students Exposed to Single Patient, Hong Kong. Emerging Infectious Diseases, Volume 10(2), pp. 269–276
Yu, I.T.S., Li, Y., Wong, T.W., Tam, W., Chan, A.T., Lee, J.H.W., Leung, D.Y.C., Ho, T., 2004. Evidence of Airborne Transmission of the Severe Acute Respiratory Syndrome Virus. New England Journal of Medicine, Volume 350(17), pp. 31–39