This app protects you and your loved ones from airborne infectious agents such as influenza, SARS-CoV-2 etc. It also assists you in living in an infection-free environment.

Description of stop-infection

After the emergence of SARS-CoV-2 in 2019 and the subsequent COVID-19 pandemic, carbon dioxide monitors are now more commonly used to measure indoor air quality than they were before the pandemic. However, it is challenging to determine the likelihood of infection based solely on the carbon dioxide concentration level, as infection depends on other variables, such as the number of people infected in the room. The "Infection risk assessment" in this app provides insight into the likelihood of infection in the room where you are based on the following values: 1) carbon dioxide concentration in ppm units, 2) the number of people infected, 3) the number of people not infected, 4) the average body weight in kg of the people in the room, 5) the size of the room in cubic meters, and 6) the duration of time spent in the room.

   Additionally, the "Clean Air Evaluation" in this app gives you clean air conditions you can set in your room to maintain your infection risk at your desired level. The values you need to enter include your desired level of infection risk and values 2) to 6) mentioned above. You can always assess the risk of infection in the room and ensure that your room remains safe according to the infection risk you are comfortable with.

This app provides valuable information for everyone who spends time with their family, friends, colleagues in various settings: home, school, nursery, offices, restaurants, bars, clubs, gyms, theatres, cinemas, religious institutions, public transports, hotels, libraries, museums and more. It offers essential insights to reduce the risk of infection and enhance safety in diverse gathering places.

   As a rule of thumb, the average outdoor level of carbon dioxide is 400 ppm. In occupied spaces with good air exchange, the typical level ranges from 400 to 1000 ppm. Levels associated with complaints of drowsiness and poor air quality are in the range of 1000 to 2000 ppm. Furthermore, levels ranging from 2000 to 5000 ppm are associated with symptoms such as headaches, sleepiness, a sense of stagnant and stale air, poor concentration, loss of attention, increased heart rate, and slight nausea. As long as you do not have any of those symptoms, the carbon dioxide level in the room is considered below 2000 ppm.

Theory behind stop-infection

The infected individuals exhale "quanta" (plural of quantum), which is a unit of infectious agent causing infection. A "quantum" is defined as the number of aerosols causing infection with a probability of 0.63 once uninfected and infectious-agent-susceptible individuals inhale a "quantum".  The Wells-Riley model (reference 1 and 2) assumes that the "quanta"  are distributed uniformly in the room, people inhale air with uniformly distributed "quanta” and the number of inhaled “quanta” follows a homogeneous Poisson distribution.  Additionally, It is modelled that the carbon dioxide molecules and “quanta” within the room exhibits similar behaviour, and a reciprocal correlation between the two within the given spatial context. How many “quanta" are produced by infected people? This is dictated by the quantum generation rate (QGR), an infectious-agent-specific parameter calculated retrospectively from the basic reproduction number. As the basic reproduction number varies widely due to its dependence on the physical activity of people, age, infection stage and environmental conditions etc, the QGR fluctuates across a wide magnitude range. We employed a geometric mean between the lowest and the highest QGR reported by Dai and Zhao (reference 3 and 4) in this app.  Once the carbon dioxide concentration is known, the required air change rate in the room is estimated, quantifying the amount of "quanta" in the room and leading to the assessment of the risk of infection, specifically the probability of inhaling at least one quantum. Please note that the process of estimating air change based on CO2 levels involves approximating an inverse function of CO2 concentration. It's important to understand that this inverse function does not have a closed-form solution, meaning air exchange rate cannot be accurately expressed as a function of CO2. Instead, it requires approximations to determine air exchange rate from CO2 levels. This causes a paradox in a small number of occupants, namely when the CO2 level is low, yet the infection risk is higher compared to environments with more occupants experiencing the same CO2 level. In StoPify, this problem is completely resolved without the need for any approximation, as both CO2 levels and infection risk are precisely calculated based on the air exchange rate, ensuring accurate and reliable solutions for maintaining clean and safe indoor environments. We can reverse the calculation from the risk of infection to deduce “quanta”, the air change rate, and subsequently, the carbon dioxide concentration. This reverse calculation allows us to set the clean air conditions that fulfil the risk requirements provided by the user.

Reference

1. Wells, H.F. Airborne Contagion and Air Hygiene: an Ecological Study of Droplet Infection, Cambridge, MA, Harvard University Press (1955)

2. Riley, E.C., Murphy, G. and Riley, R.L. Airborne spread of measles in a suburban elementary school, Am. J. Epidemiol., (1978) 107: 421-432

3. Dai, H., Zhao, B. Association of the infection probability of COVID-19 with ventilation rates in confined spaces, Build. Simul. (2020) 13: 1321-1327

4. Dai, H., Zhao, B. Association between the infection probability of COVID-19 and ventilation rates: An update for SARS-CoV-2 variants, Build. Simul. (2023) 16: 3-12

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