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A Mathematical Model for Evaluating the Risk of Airborne Infection Among Bus Passengers Using Ventilation Systems
(2024-01-01)
Sooknum, Jenjira
;
Pochai, Nopparat
Carbon dioxide from human breath contributes significantly to airborne diseases. Breathing can expose us to usually dangerous airborne infections, which rapidly spread. By using a bus, there is a chance of contracting an infection. This study takes into account a mathematical model of airborne infection caused by human breath. The purpose of this research is to evaluate the probability that passengers in a bus with ventilation systems may well get an airborne infection. The model can be divided into five submodels, such as an exhaled air concentration measurement model for a bus with a variable number of passengers, the volume fraction of exhaled air model, the concentration of airborne infectious particles model, the number of airborne infectious particles model, and the risk of airborne infection model. The model’s solution might be used to determine the probability that susceptible people will get an airborne infection. An explicit forward-time centered-space finite difference method is used to approximate the solution. In order to reduce the risk of airborne infection and improve ventilation, the provided mathematical models were used to assess the risk of airborne infection among bus passengers using ventilation systems. Better air quality control that balances the number of passengers allowed to travel on a bus will be among the ventilation’s main advantages.
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A Mathematical Model for the Evaluation of Airborne Infection Risk for Bus Passengers
(2023-03-01)
Sooknum, Jenjira
;
Pochai, Nopparat
Human breath emits a lot of carbon dioxide, which contributes a lot to airborne infections. Airborne infections spread speedily, and breathing can expose us to life-threatening airborne infections. There is a risk of infection if people are traveling by bus. A mathematical model of carbon dioxide concentration measurement due to human breath is proposed in this research. The focus of this research is to determine the amount of carbon dioxide produced by bus passengers. The model's solution is approximated using an explicit finite difference technique. The model solution can be used to determine how much time passengers are willing to spend on the bus while carbon dioxide levels are kept under control. Furthermore, mathematical models were utilized to quantify the risk of air infection among bus passengers with ventilation systems, in order to reduce the risk of air infection and increase ventilation. The proposed air quality model was found to be in good agreement in that it allows us to know the balance between the number of passengers allowed to sit on the bus while managing the risk of airborne infection, carbon dioxide concentration, and ventilation system potential. A significant advantage of the ventilation will be better air quality control that balances the number of passengers permitted to ride on a bus
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A Mathematical Model of the Risk Assessment for Airborne Transmission in a Classroom with a Ventilation System and Nine Distinct Face Mask Efficiency
(2023-03-01)
Janmanit, Benjawan
;
Pochai, Nopparat
TB, COVID-19, MERS, and SARS are all serious infectious diseases that are transmitted by the air or aerosol via coughing, spitting, sneezing, speaking, or wounds. When restaurants and bars reopen and continue operations in some parts of the United States, the Centers for Disease Control and Prevention (CDC) gives the following suggestions for how operators can reduce risk for employees, customers, and communities while also restricting the spread of COVID-19. The more and longer a person interacts with others, the greater the risk of COVID-19 spreading. Therefore, we need to be informed of its management and treatment. As a result, for the control and reduction of potentially polluted air, such as CO2 levels, good air quality management is required. They investigated the protective effectiveness of face masks against airborne transmission of infectious SARS-CoV-2 droplets and aerosols in response to the World Health Organization's recommendation to wear face masks to prevent the spread of COVID-19. Using nine different forms of mask efficiency, this research provides a mathematical model for calculating the chance of airborne transmission in a classroom. The fourth-order Runge-Kutta approach is used to approximate the model solution. The proposed strategy strikes a balance between the number of students allowed to stay in the classroom and the effectiveness of nine different masks. We can see how utilizing nine different masks and a well-ventilated system in the classroom can help to reduce the risk of airborne infection.
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A Risk Assessment Model for Airborne Infection in a Ventilated Room using the Adaptive Runge-Kutta Method with Cubic Spline Interpolation
(2022-01-01)
Timpitak, Wasu
;
Pochai, Nopparat
Bacteria or viruses that are spread by tiny respiratory droplets are known as airborne infections. These infectious vehicles can move along air currents, stay in the air, or stick to surfaces before being inhaled by another person. Airborne transmission may happen across long ranges and time periods. Increased infection rates or clusters of airborne infections are linked to a lack of ventilation or low ventilation rates. While normal people remain in the same room as infectors, this research will utilize a mathematical model for estimating the concentration of exhaled air in a space with an outlet ventilation system, as well as the risk of infection. As a result, the exhaled air concentration and infection risk are affected by the actual concentration level, the number of users, and the rate of ventilation. The adaptive Runge-Kutta technique and the standard fourth-order Runge-Kutta technique are used to estimate the model solution. Because the number of individuals who stay in the space varies over time, the Lagrange interpolating polynomial and cubic splines interpolation are employed to represent the number of individuals in the space. A good agreement solution is obtained using the adaptive Runge-Kutta method with cubic spline interpolation. The proposed strategy represents the balance in the air quality management process between the number of individuals allowed to stay in the space and the performance of the air ventilation system. For the optimal outcomes, the proposed technique was capable of converting field data from a the number of individuals using cubic splines and adaptive RK methods. The model can also be utilized as a part of an internet of things (IoT) system to develop new approaches to controlling infection-free zones. We demonstrate that the proposed strategy works in real-world scenarios.
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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
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Oyjinda, Pravitra
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Konglok, Sureerat A.
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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.

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