COVID-19 Contagion Risk Estimation Model for Indoor Environments.

COVID-19 is an infectious disease mainly transmitted through aerosol particles. Physical distancing can significantly reduce airborne transmission at a short range, but it is not a sufficient measure to avoid contagion. In recent months, health authorities have identified indoor spaces as possible sources of infection, mainly due to poor ventilation, making it necessary to take measures to improve indoor air quality. In this work, an accurate model for COVID-19 contagion risk estimation based on the Wells-Riley probabilistic approach for indoor environments is proposed and implemented as an Android mobile App. The implemented algorithm takes into account all relevant parameters, such as environmental conditions, age, kind of activities, and ventilation conditions, influencing the risk of contagion to provide the real-time probability of contagion with respect to the permanence time, the maximum allowed number of people for the specified area, the expected number of COVID-19 cases, and the required number of Air Changes per Hour. Alerts are provided to the user in the case of a high probability of contagion and CO2 concentration. Additionally, the app exploits a Bluetooth signal to estimate the distance to other devices, allowing the regulation of social distance between people. The results from the application of the model are provided and discussed for different scenarios, such as offices, restaurants, classrooms, and libraries, thus proving the effectiveness of the proposed tool, helping to reduce the spread of the virus still affecting the world population.

A Non-Contact Integrated Body-Ambient Temperature Sensors Platform to Contrast COVID-19

An integrated sensors platform for non-contact temperature monitoring is proposed in this work. The adopted solution, based on the combined integration of an infrared thermometer and a capacitive humidity sensor, is able to provide a fast and accurate tool for remotely sensing both ambient and body temperature in the framework of pandemic situations, such as COVID-19, thus avoiding any direct contact with people. The information relative to the ambient temperature is successfully exploited to derive a correction formula for the accurate extraction of body temperature from the measurement provided by the standard infrared sensor. Full details on the design of the proposed platform are provided in the work, by reporting relevant simulation results on the variations of ambient temperature, relative humidity, and body temperature. Experimental validations are also discussed to provide a full assessment of the proposed approach.