COVID-19 Ventilation Risk Meter

COVID-19 like many other infections are now proven to be airborne i.e spread via fine droplets suspended in the air. To reduce the risks of airborne transmission of the disease it quite important to have adequate ventilation in proportion to the number of people and type of activity in the space. We have developed a free to use, simple and accessible interactive tool to asses this risk using models proven in peer reviewed scientific literature. Disclaimer: please note this tool is based on historic knowledge and models of airborne transmission which is continuously evolving for COVID19 and this tool is to be used as only a guidance to operate your property. The actual risk may vary depending on many other factors, measures implements and this tool does not claim to reduce the risk.

Your current CO2 level

500 pm

Critical CO2 level

1000 ppm

One superspreader person, after spending 2 hours with 1 healthy people in a room, where CO2 level is at 500 ppm, will (on average) infect 30 person(s).

Available evidence suggests that SARS-CoV-2 (the virus which causes the COVID-19 disease) is airborne, meaning that it can spread via aerosols (fine droplets) produced while coughing, talking, or breathing (Correia et al., 2020; Li et al., 2020; Liu et al., 2020; Tang et al., 2020), which can stay suspended in the air for up to several hours (Van Doremalen et al., 2020). In practice, it means that staying in closed, poorly-ventilated room can pose significant infection risk, if an infected person (possibly asymptomatic or pre-symptomatic) is also present, or stayed in the same room shortly before. This tool assumes that there is one infected person always present in the room and tries to asses the risk of other people getting infected due to aerosol dispersion alone. (Not counting direct contact and close proximity droplet transmission). This model does not need to know the dimensions of the space as CO2 levels are used as a proxy for rate of ventilation as outside fresh air is assumed to be around 400ppm (Rudnick & Milton, 2003).

In general to prevent this kind of infection spread, it is therefore necessary to wear face masks, and ensure good ventilation, whenever spending time in closed spaces with other people is not possible to avoid.

Depending on the virus' properties, the infected person, and different environmental factors, a single (primary) infector may spread the disease to a various number of other susceptible individuals. The expected number of secondary cases resulting directly from a single primary case, given certain circumstances, is called basic reproduction number (R₀).

If R₀ is between 0 and 1 , each primary case will result in less than one secondary case, on average, which means the epidemic will slowly die out. On the other hand, if an average primary case generates more than one secondary case, the number of infections will grow exponentially.

Here, the level of CO₂ at which R₀ crosses 1 is called critical value, and is marked with a red vertical line on the graph.

Keep in mind that even at R₀ is less than 1 there is still a non-zero risk of getting infected - especially if the infector happens to be a superspreader.

This section will help you understand the parameters that you can manipulate in this tool. To learn more about how they are used, see the next section (The risk model).

Current CO₂ level

What is the approximate level of CO₂ in the room?

Normally, the level of CO₂ outdoors remains more or less constant, at approximately 410ppm. Even a single person present in an unventilated room can cause the indoor CO₂ to go up to 600ppm or more. In a poorly ventilated classroom or a meeting room, it is not uncommon to see values of 2000-3000ppm.

Occupancy

How many people are there in the room?

The more people are present, the higher the chance that some of them will get infected. In many instances e.g. classrooms the number of people can be assumed to be fixed e.g. 20 students and 1 teacher. But in many other instances it’s important to actually count the number of people.

Time of exposure

There is no lower limit to how much time it takes to get infected. If you enter a room where the air is contaminated with virus, there is a chance that it will happen the first time you breathe in. It is very unlikely; however, the longer you remain in such space, the more virus you are exposed to, and the higher is the risk of infection. It’s useful to know the length of exposure if possible.

For example, imagine a colleague from your office, after getting infected, but before developing symptoms, attended the office for two working days. It could have meant that you were exposed to the airborne virus for around 16 hours.

Expiratory activity type

Airborne transmission is possible because the virus, which accumulates in an infected person's mouth and throat, is released into the air during talking, coughing, or breathing. Those various expiratory activities will produce droplets and aerosols in different numbers and sizes. Thus the total number of viral particles floating in the air is also a factor of the type of respiratory activity occurring in a confined space.

The four options presented here, along with their underlying values, come from the experimental results obtained by Morawska et al. (2009).

Physical activity type

Depending on the physical activity they are performing, the breathing rate of an infected person will change, resulting in different amounts of virus released. It’s usually hard to generalise the physical activity in mixed use spaces but for known contexts it could be set as a static parameter while in other cases, more dynamic calculations need to be implemented.

The options presented here, and their corresponding breathing rates, were obtained by Adams (1993).

Infectivity

This parameter refers to the concentration of virus that can be found in person's mouth - the viral load. The higher the viral load, the larger the amount of virus emitted into the air, and the higher the risk of somebody getting infected. Extremely high viral load can result in the emergence of so-called super spreaders - people who infect many other, even in a relatively short time.

The viral load can vary a lot between patients. There are, however, some regularities. There is evidence, for example, that viral load changes over the course of the disease, and that it is usually highest around the time of the onset of symptoms (To et al., 2020; Walsh et al., 2020; Woelfel et al., 2020). That means that people infected with SARS-CoV-2 can spread the infection a high rate even before they know they are sick.

Additionally, available evidence suggests that people who are asymptomatic (do not develop symptoms at all) can still be infectious, and tend to spread the virus as a similar rate as symptomatic patients (Rothe et al., 2020; Walsh et al., 2020; Zou et al., 2020).

As it is not possible to predict who might be a super spreader, the visualization takes the numbers reported in the literature as a reference to approximate the median value of viral load, while allowing the user to adjust it, and explore different possible scenarios.

This tool, and the underlying mathematical model, can help you estimate and visualize the scale of risk of airborne infection with SARS-CoV-2 virus, and understand better how different factors affect the epidemiological safety of spaces that you live in, work in, or manage. It comes with the following major limitations:

It cannot tell you if you will get sick. This depends on many factors that the model does not know about, including your personal health and behaviour. Even if the predicted R0 score seems low, you should still take appropriate precautions, and follow the advice of relevant health authorities.
It only takes airborne transmission into account. Airborne infection appears to be a major or main way many diseases spread, and there is evidence suggesting that COVID-19 is one of them. There are, however, other possible infection vectors, including large droplets (which are emitted while coughing or sneezing, but do not travel long distances, and quickly fall to the ground); contaminated surfaces; or direct contact with an infected person. Those risks are not included in this model, and ventilation has limited potential to mitigate them.
It is based on our current understanding of the properties of SARS-CoV-2, which can still be subject to change. While doctors and scientists work tirelessly to understand how the novel coronavirus spreads, our current knowledge about it is still incomplete, and constantly evolving. Some of the parameters used by this model are based on estimates that had to rely on a limited body of evidence.

Some parameters used in these calculations are specific to the SARS-CoV-2 virus. However, the model itself can, and has been successfully used to estimate the risk of other diseases that transmit via the airborne route (see e.g. Sun et al., 2011). These include common cold, measles, smallpox, influenza, and possibly SARS (Booth et al., 2005; Olsen et al., 2003; Yu et al., 2004; Yu et al., 2005) and MERS (Bin et al., 2016; Kim et al., 2016), and others.

In general, higher risk of contracting COVID-19, as predicted by the Rudnick-Milton model, will also mean higher risk for any other airborne disease.

Adams, W. C. (1993). Measurement of Breathing Rate and Volume in Routinely Performed Daily Activities. Final Report. Prepared for the California Air Resources Board, Contract No. A033-205, April 1993.

Bin, S. Y., Heo, J. Y., Song, M. S., Lee, J., Kim, E. H., Park, S. J., ... & Lee, I. W. (2016). Environmental contamination and viral shedding in MERS patients during MERS-CoV outbreak in South Korea. Clinical Infectious Diseases, 62(6), 755-760.

Booth, T. F., Kournikakis, B., Bastien, N., Ho, J., Kobasa, D., Stadnyk, L., ... & Mederski, B. (2005). Detection of airborne severe acute respiratory syndrome (SARS) coronavirus and environmental contamination in SARS outbreak units. The Journal of infectious diseases, 191(9), 1472-1477.

Buonanno, G., Stabile, L., & Morawska, L. (2020). Estimation of airborne viral emission: quanta emission rate of SARS-CoV-2 for infection risk assessment. Environment International, 105794.

Correia, G., Rodrigues, L., Silva, M. G., & Gonçalves, T. (2020). Airborne route and bad use of ventilation systems as non-negligible factors in SARS-CoV-2 transmission. Medical Hypotheses, 109781.

Kim, S. H., Chang, S. Y., Sung, M., Park, J. H., Bin Kim, H., Lee, H., ... & Min, J. Y. (2016). Extensive viable Middle East respiratory syndrome (MERS) coronavirus contamination in air and surrounding environment in MERS isolation wards. Reviews of Infectious Diseases, 63(3), 363-369.

Li, Y., Qian, H., Hang, J., Chen, X., Hong, L., Liang, P., ... & Kang, M. (2020). Evidence for probable aerosol transmission of SARS-CoV-2 in a poorly ventilated restaurant. medRxiv.

Liu, Y., Ning, Z., Chen, Y., Guo, M., Liu, Y., Gali, N. K., ... & Liu, X. (2020). Aerodynamic analysis of SARS-CoV-2 in two Wuhan hospitals. Nature, 582(7813), 557-560.

Morawska, L. J. G. R., Johnson, G. R., Ristovski, Z. D., Hargreaves, M., Mengersen, K., Corbett, S., ... & Katoshevski, D. (2009). Size distribution and sites of origin of droplets expelled from the human respiratory tract during expiratory activities. Journal of Aerosol Science, 40(3), 256-269.

Olsen, S. J., Chang, H. L., Cheung, T. Y. Y., Tang, A. F. Y., Fisk, T. L., Ooi, S. P. L., ... & Hsu, K. H. (2003). Transmission of the severe acute respiratory syndrome on aircraft. New England Journal of Medicine, 349(25), 2416-2422.

Pan, Y., Zhang, D., Yang, P., Poon, L. L., & Wang, Q. (2020). Viral load of SARS-CoV-2 in clinical samples. The Lancet Infectious Diseases, 20(4), 411-412.

Rothe, C., Schunk, M., Sothmann, P., Bretzel, G., Froeschl, G., Wallrauch, C., ... & Seilmaier, M. (2020). Transmission of 2019-nCoV infection from an asymptomatic contact in Germany. New England Journal of Medicine, 382(10), 970-971.

Rudnick, S. N., & Milton, D. K. (2003). Risk of indoor airborne infection transmission estimated from carbon dioxide concentration. Indoor air, 13(3), 237-245.

Sun, Y., Wang, Z., Zhang, Y., & Sundell, J. (2011). In China, students in crowded dormitories with a low ventilation rate have more common colds: evidence for airborne transmission. PloS one, 6(11), e27140.

Tang, S., Mao, Y., Jones, R. M., Tan, Q., Ji, J. S., Li, N., ... & Wang, X. (2020). Aerosol Transmission of SARS-CoV-2? Evidence, Prevention and Control. Environment International, 106039.

To, K. K. W., Tsang, O. T. Y., Leung, W. S., Tam, A. R., Wu, T. C., Lung, D. C., ... & Lau, D. P. L. (2020). Temporal profiles of viral load in posterior oropharyngeal saliva samples and serum antibody responses during infection by SARS-CoV-2: an observational cohort study. The Lancet Infectious Diseases.
van Doremalen, N., Bushmaker, T., Morris, D. H., Holbrook, M. G., Gamble, A., Williamson, B. N., ... & Lloyd-Smith, J. O. (2020). Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1. New England Journal of Medicine, 382(16), 1564-1567.

Walsh, K. A., Jordan, K., Clyne, B., Rohde, D., Drummond, L., Byrne, P., ... & O'Neill, M. (2020). SARS-CoV-2 detection, viral load and infectivity over the course of an infection: SARS-CoV-2 detection, viral load and infectivity. Journal of Infection.
Watanabe, T., Bartrand, T. A., Weir, M. H., Omura, T., & Haas, C. N. (2010). Development of a dose‐response model for SARS coronavirus. Risk Analysis: An International Journal, 30(7), 1129-1138.

Woelfel, R., Corman, V. M., Guggemos, W., Seilmaier, M., Zange, S., Mueller, M. A., ... & Bleicker, T. (2020). Clinical presentation and virological assessment of hospitalized cases of coronavirus disease 2019 in a travel-associated transmission cluster. medRxiv.
Yu, I. T., Li, Y., Wong, T. W., Tam, W., Chan, A. T., Lee, J. H., ... & Ho, T. (2004). Evidence of airborne transmission of the severe acute respiratory syndrome virus. New England Journal of Medicine, 350(17), 1731-1739.

Yu, I. T., Wong, T. W., Chiu, Y. L., Lee, N., & Li, Y. (2005). Temporal-spatial analysis of severe acute respiratory syndrome among hospital inpatients. Clinical Infectious Diseases, 40(9), 1237-1243.

Zou, L., Ruan, F., Huang, M., Liang, L., Huang, H., Hong, Z., ... & Guo, Q. (2020). SARS-CoV-2 viral load in upper respiratory specimens of infected patients. New England Journal of Medicine, 382(12), 1177-1179.

Are my people at risk?

The risk profile of a space and its occupants keeps changing over time based on all the above factors lists and it important to keep track of it as facilities are reopened for use to avoid becoming a high risk zone.

For professionals operating buildings we have tools that can assess the risk on a continuous basis with a combination of sensor fusion from our IoT sensors (CO₂ levels, people count, aerosol counting etc.), data science models and multi-channel notification system via email/SMS or in-app notifications. Please contact us if you would like to learn more or see a live demo.

Get In Touch

If you would like to use our free API, integrated into your technical systems or implement this as a real-time service in your facility.

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COVID-19 Ventilation Risk Meter

Current CO₂ Level

410

500 ppm

2000

Occupancy

100

Time Of Exposure (Hours)

2 Hours

Face Mask

Expiratory Activity Level

Physical Activity Level

Infectivity Scenario

This section will help you understand the parameters that you can manipulate in this tool. To learn more about how they are used, see the next section (The risk model).

Current CO₂ level

Occupancy

Time of exposure

Expiratory activity type

Physical activity type

Infectivity

It cannot tell you if you will get sick. This depends on many factors that the model does not know about, including your personal health and behaviour. Even if the predicted R0 score seems low, you should still take appropriate precautions, and follow the advice of relevant health authorities.
It only takes airborne transmission into account. Airborne infection appears to be a major or main way many diseases spread, and there is evidence suggesting that COVID-19 is one of them. There are, however, other possible infection vectors, including large droplets (which are emitted while coughing or sneezing, but do not travel long distances, and quickly fall to the ground); contaminated surfaces; or direct contact with an infected person. Those risks are not included in this model, and ventilation has limited potential to mitigate them.
It is based on our current understanding of the properties of SARS-CoV-2, which can still be subject to change. While doctors and scientists work tirelessly to understand how the novel coronavirus spreads, our current knowledge about it is still incomplete, and constantly evolving. Some of the parameters used by this model are based on estimates that had to rely on a limited body of evidence.

Adams, W. C. (1993). Measurement of Breathing Rate and Volume in Routinely Performed Daily Activities. Final Report. Prepared for the California Air Resources Board, Contract No. A033-205, April 1993.

Bin, S. Y., Heo, J. Y., Song, M. S., Lee, J., Kim, E. H., Park, S. J., ... & Lee, I. W. (2016). Environmental contamination and viral shedding in MERS patients during MERS-CoV outbreak in South Korea. Clinical Infectious Diseases, 62(6), 755-760.

Booth, T. F., Kournikakis, B., Bastien, N., Ho, J., Kobasa, D., Stadnyk, L., ... & Mederski, B. (2005). Detection of airborne severe acute respiratory syndrome (SARS) coronavirus and environmental contamination in SARS outbreak units. The Journal of infectious diseases, 191(9), 1472-1477.

Buonanno, G., Stabile, L., & Morawska, L. (2020). Estimation of airborne viral emission: quanta emission rate of SARS-CoV-2 for infection risk assessment. Environment International, 105794.

Correia, G., Rodrigues, L., Silva, M. G., & Gonçalves, T. (2020). Airborne route and bad use of ventilation systems as non-negligible factors in SARS-CoV-2 transmission. Medical Hypotheses, 109781.

Kim, S. H., Chang, S. Y., Sung, M., Park, J. H., Bin Kim, H., Lee, H., ... & Min, J. Y. (2016). Extensive viable Middle East respiratory syndrome (MERS) coronavirus contamination in air and surrounding environment in MERS isolation wards. Reviews of Infectious Diseases, 63(3), 363-369.

Li, Y., Qian, H., Hang, J., Chen, X., Hong, L., Liang, P., ... & Kang, M. (2020). Evidence for probable aerosol transmission of SARS-CoV-2 in a poorly ventilated restaurant. medRxiv.

Liu, Y., Ning, Z., Chen, Y., Guo, M., Liu, Y., Gali, N. K., ... & Liu, X. (2020). Aerodynamic analysis of SARS-CoV-2 in two Wuhan hospitals. Nature, 582(7813), 557-560.

Morawska, L. J. G. R., Johnson, G. R., Ristovski, Z. D., Hargreaves, M., Mengersen, K., Corbett, S., ... & Katoshevski, D. (2009). Size distribution and sites of origin of droplets expelled from the human respiratory tract during expiratory activities. Journal of Aerosol Science, 40(3), 256-269.

Olsen, S. J., Chang, H. L., Cheung, T. Y. Y., Tang, A. F. Y., Fisk, T. L., Ooi, S. P. L., ... & Hsu, K. H. (2003). Transmission of the severe acute respiratory syndrome on aircraft. New England Journal of Medicine, 349(25), 2416-2422.

Pan, Y., Zhang, D., Yang, P., Poon, L. L., & Wang, Q. (2020). Viral load of SARS-CoV-2 in clinical samples. The Lancet Infectious Diseases, 20(4), 411-412.

Rothe, C., Schunk, M., Sothmann, P., Bretzel, G., Froeschl, G., Wallrauch, C., ... & Seilmaier, M. (2020). Transmission of 2019-nCoV infection from an asymptomatic contact in Germany. New England Journal of Medicine, 382(10), 970-971.

Rudnick, S. N., & Milton, D. K. (2003). Risk of indoor airborne infection transmission estimated from carbon dioxide concentration. Indoor air, 13(3), 237-245.

Sun, Y., Wang, Z., Zhang, Y., & Sundell, J. (2011). In China, students in crowded dormitories with a low ventilation rate have more common colds: evidence for airborne transmission. PloS one, 6(11), e27140.

Tang, S., Mao, Y., Jones, R. M., Tan, Q., Ji, J. S., Li, N., ... & Wang, X. (2020). Aerosol Transmission of SARS-CoV-2? Evidence, Prevention and Control. Environment International, 106039.

To, K. K. W., Tsang, O. T. Y., Leung, W. S., Tam, A. R., Wu, T. C., Lung, D. C., ... & Lau, D. P. L. (2020). Temporal profiles of viral load in posterior oropharyngeal saliva samples and serum antibody responses during infection by SARS-CoV-2: an observational cohort study. The Lancet Infectious Diseases.
van Doremalen, N., Bushmaker, T., Morris, D. H., Holbrook, M. G., Gamble, A., Williamson, B. N., ... & Lloyd-Smith, J. O. (2020). Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1. New England Journal of Medicine, 382(16), 1564-1567.

Walsh, K. A., Jordan, K., Clyne, B., Rohde, D., Drummond, L., Byrne, P., ... & O'Neill, M. (2020). SARS-CoV-2 detection, viral load and infectivity over the course of an infection: SARS-CoV-2 detection, viral load and infectivity. Journal of Infection.
Watanabe, T., Bartrand, T. A., Weir, M. H., Omura, T., & Haas, C. N. (2010). Development of a dose‐response model for SARS coronavirus. Risk Analysis: An International Journal, 30(7), 1129-1138.

Woelfel, R., Corman, V. M., Guggemos, W., Seilmaier, M., Zange, S., Mueller, M. A., ... & Bleicker, T. (2020). Clinical presentation and virological assessment of hospitalized cases of coronavirus disease 2019 in a travel-associated transmission cluster. medRxiv.
Yu, I. T., Li, Y., Wong, T. W., Tam, W., Chan, A. T., Lee, J. H., ... & Ho, T. (2004). Evidence of airborne transmission of the severe acute respiratory syndrome virus. New England Journal of Medicine, 350(17), 1731-1739.

Yu, I. T., Wong, T. W., Chiu, Y. L., Lee, N., & Li, Y. (2005). Temporal-spatial analysis of severe acute respiratory syndrome among hospital inpatients. Clinical Infectious Diseases, 40(9), 1237-1243.

Zou, L., Ruan, F., Huang, M., Liang, L., Huang, H., Hong, Z., ... & Guo, Q. (2020). SARS-CoV-2 viral load in upper respiratory specimens of infected patients. New England Journal of Medicine, 382(12), 1177-1179.

Are my people at risk?

Your current CO2 level

500 pm

Critical CO2 level

1000 ppm

One superspreader person, after spending 2 hours with 1 healthy people in a room, where CO2 level is at 500 ppm, will (on average) infect 30 person(s).

Get In Touch

Name *Email *I am a

I am interested in