Predictive algorithm for COVID-19 infection risk in indoor environments

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 d...

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Vydáno v:Scientific reports Ročník 15; číslo 1; s. 27789 - 11
Hlavní autoři: Rucco, Chiara, Piscitelli, Prisco, Longo, Antonella, Ardebili, Ali Aghazadeh, Miani, Alessandro, Greco, Enrico
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 30.07.2025
Nature Publishing Group
Nature Portfolio
Témata:
631/114/1314
631/114/2164
692/308/174
Air pollution
Air Pollution, Indoor - analysis
Air quality
Air quality measurements
Air quality monitoring
Algorithm
Algorithms
Carbon dioxide
COVID-19
COVID-19 - epidemiology
COVID-19 - transmission
Disease spread
Disease transmission
Environmental factors
Environmental monitoring
Environmental Monitoring - methods
Environmental pollution
Global pandemic (COVID-19)
Health risks
Humanities and Social Sciences
Humans
Humidity
Indoor air quality
Indoor environments
Infectious diseases
Internet of Things
multidisciplinary
Outdoor air quality
Pandemics
Particulate matter
Particulate Matter - analysis
Prediction Algorithms
Prediction models
Predictive
Risk Assessment
SARS-CoV-2 - isolation & purification
Science
Science (multidisciplinary)
Sensors
Temperature
Italy
Global pandemic (COVID-19)
Environmental pollution
Particulate matter
Predictive
Air pollution
Air quality monitoring
Algorithm
ISSN:2045-2322, 2045-2322
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Shrnutí: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-12626-y