In September 2019, I reviewed a great air quality monitor called Djinn. One of the unique features of this device is the algorithms that are running in its core. The team of Djinn was able to move even further their technology by designing a respiratory virus risk among others analytics on their dashboard, which is very handy during the covid-19 pandemic.

The team took part in the “A Call for Action” towards building the data infrastructure and ecosystem we need to tackle pandemics and other dynamic societal and environmental threats.

A lot of great companies took part in the initiate including Apple Inc, Vodafone, Mozilla, and many more, the list is long.

What the team has designed is the ability to evaluate the risks individuals and employees may face, taking into account all the necessary factors for the analysis. The data come from the device which has an array of pollution and environmental sensors such as, CO2, PM10/2.5/1.0, Formaldehyde, light, temperature, humidity, air pressure, and noise.

However, the company has unleashed the same power for other air quality monitors such as the Netatmo and Xiaomi thanks to a Cloud API.

My personal Djinn dashboard is publicly available, just click here and you will be able to have an idea of the risks that may or may not face. So far my respiratory virus risk is on safe levels and I work hard to keep it that way.

There are other useful analytics in my dashboard like Brain health, Cardiovascular health, Respiratory health, Mental health, and Sensorial health.

Respiratory Virus Risk

What parameters affect Respiratory Virus Activity.

• Absolute humidity (or water vapour pressure): Higher moisture values reduce the contagious characteristics of the virus
• Temperature: An increase in temperature reduces risks, a decrease – increases
• CO2 level: An increase in CO2 indicates insufficient air exchange and possibly a “high density of people” in the room.
• The amount of dust in the air. An increase in the level above certain values indicates that viruses that have settled on the surface, probably due to the electrical charge of the virus molecules, are likely to rise into the air, along with dust. (Netatmo has no sensor that measures the level of dust in the air. )

Their mathematical model calculates the combined risks (those determined by the indoor environment). Results can be seen here.

They also did a compare dashboard and Respiratory Virus Activity index from Djinn Sensor and NetAtmo located in adjacent rooms in the office of the company in Minsk, Belarus with close parameters of the internal environment.

The value of Respiratory Virus Activity the level shown on the basis of the Djinn sensor data is slightly higher than that shown on the basis of the Netatmo sensor data since Netatmo’s absolute humidity (Vapor Pressure) readings are slightly higher.

The temperature and carbon dioxide levels differ slightly, the dust level is low and does not affect the index in this case. We can observe a decrease in the level at about 2 PM when the CO2 level decreases, the rooms were probably aired.