Predictive algorithm for COVID-19 infection risk in indoor environments - Rucco, Chiara; Piscitelli, Prisco; Longo, Antonella; Ardebili, Ali Aghazadeh; Miani, Alessandro; Greco, Enrico (Scientific reports, 2025)
After the onset of the global COVID-19 pandemic, the deep connections between environmental factors and the transmission of airborne infectious diseases (including COVID-19) has become an area of relevant scientific and social interest. Indoor environments, where we spend a significant part of our daily lives, play a crucial role in shaping the dynamics of disease spread. The mitigation of infection risk related to poor indoor air quality and its link with the transmission of airborne diseases has emerged as a focal point for research and intervention strategies. This paper presents the results of a specific collaborative project in this field, focused on the utilization of Internet of Things (IoT) devices for comprehensive indoor environmental monitoring and infectious risk forecasting. In the frame of developing effective countermeasures for COVID-19 and future pandemic preparedness, our primary goal was to develop a predictive model for infection risk in indoor environments. Parameters such as humidity, temperature, CO 2 , and particulate matter concentrations (namely PM10 and PM2.5), have been assessed and modelled as indicators of indoor air quality, with these measures having been combined to generate a predictive algorithm specifically able to provide information about the transmission dynamics of COVID-19 and airborne infectious diseases within indoor spaces. This newly-developed Algorithm for the Prediction of Risk of Infections (APRI) relies on rigorous analyses and established different risk thresholds based on temperature, humidity, and CO 2 levels. The model showed significant associations between environmental factors, such as temperature, CO 2 levels, humidity, and particulate matter concentrations. A pivotal role of PM10 and PM2.5 in shaping air quality in indoor environments has been highlighted, as low PM concentrations corresponded in our predictive model to a minimal risk of airborne infectious diseases, while medium or high PM levels were associated with variations in temperature, humidity, and CO 2 levels, thus corresponding to an elevated risk of infection, particularly in the frame of highly diffusive diseases like COVID-19.