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Author: search.filters.author.Suebyat, KewaleeStart date: 2020

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    Three-dimensional numerical modeling for assessing airborne infection risk in hospital waiting rooms with various ventilation approaches
    (2026-06-01)
    Suebyat, Kewalee
    ;
    Pochai, Nopparat
    ;
    Sooknum, Jenjira
    ;
    Oyjinda, Pravitra
    Airborne infectious diseases, such as COVID-19, TB, MERS, and SARS, constitute a profound threat to public health and quality of life. These pathogens are transmitted primarily via atmospheric particles, especially within clinical environments, where they often circulate. Effective ventilation controls to mitigate pathogens and air pollution are thus essential for reducing hospital-based transmission of airborne infections. The purpose of this research is to assess the risk of airborne infectious diseases within a hospital in Thailand using a mathematical model. Specifically, the finite difference technique is employed to estimate carbon dioxide (CO<inf>2</inf>) concentration as a proxy for indoor air quality to indicate and assess the risk of airborne infectious diseases. The hospital layout is categorized into waiting areas and circulation areas with disparate occupant densities. Three simulation scenarios are conducted, accounting for variations in ventilation rates and architectural structure of hospitals. The results of this research demonstrate that CO<inf>2</inf> concentration can be effectively quantified as a proxy for indoor air quality within hospital environments. These calculated CO<inf>2</inf> levels are subsequently used to model the risk of airborne infection at a hospital, providing a robust framework for assessing this risk. Crucially, by integrating ventilation dynamics that reflect the physical constraints and structure of the hospital, this research enables precise evaluation of infection risks. The findings indicate that ventilation control can reduce the incidence of airborne infection, with significant practical utility in real-world clinical settings.
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    A mathematical model for the risk analysis of airborne infectious disease in an outpatient room with personal classification factor
    (2020-12-01)
    Suebyat, Kewalee
    ;
    Oyjinda, Pravitra
    ;
    Konglok, Sureerat A.
    ;
    Pochai, Nopparat
    Every day, a large number of people will use a hospital, creating a main air quality problem which may mean the risk of airborne infectious disease contamination in outpatient rooms, and affects human health. TB, COVID-19, MERS, and SARS are a hazardous communicable disease which are spread from person to person through the air or the aerosol in different ways, such as through coughing, spitting, sneezing, speaking, or through wounds. US scientists in the laboratory have shown that the virus can live in an aerosol and remain infectious for at least 3 hours. A new human coronavirus now known as the serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (formerly known as HCoV-19) emerged in late 2019 in Wuhan, China, and is now triggering a pandemic. COVID-19, TB, MERS and SARS-threats and opportunities progress against deadly infection make more people sick in the hospital. Therefore, we should be aware of the care and control of these diseases. Consequently, good air quality management is required to control and reduce possible infected air, such as carbon dioxide (CO2) concentration. In this research, a mathematical model for the risk analysis of airborne infectious disease in an outpatient room is proposed. Not only considering one type of person but also in this research, people are considered according to personal classifications. There are 4 types-patient, relative, worker, and outsider, staying in an outpatient room, which is in accordance with the real world. Air quality control manipulations are simulated using the inlet and outlet ventilation rates adjustment under the condition of a number of surrounding people with a personal classified factor. The fourth-order Runge-Kutta (RK4) is used to approximate the model solution. The proposed numerical model can be used to describe the dynamical dispersion of airborne infectious disease in an outpatient room. The results of the model are satisfactory, and it will be able to control airborne disease in more complicated structures.