Rucco Chiara, Piscitelli Prisco, Longo Antonella, Ardebili Ali Aghazadeh, Miani Alessandro, Greco Enrico
Department of Innovation Engineering, University of Salento, Lecce, Italy.
Department of Wellbeing, Nutrition and Sport, Pegaso University, Naples, Italy.
Sci Rep. 2025 Jul 30;15(1):27789. doi: 10.1038/s41598-025-12626-y.
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, 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 levels. The model showed significant associations between environmental factors, such as temperature, CO 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 levels, thus corresponding to an elevated risk of infection, particularly in the frame of highly diffusive diseases like COVID-19.
在全球新冠疫情爆发后，环境因素与空气传播传染病（包括新冠病毒）传播之间的深层联系已成为相关科学和社会关注的领域。室内环境是我们日常生活中花费大量时间的地方，在塑造疾病传播动态方面起着至关重要的作用。与室内空气质量差相关的感染风险缓解及其与空气传播疾病传播的联系已成为研究和干预策略的焦点。本文介绍了该领域一个特定合作项目的成果，重点是利用物联网（IoT）设备进行全面的室内环境监测和感染风险预测。在制定针对新冠病毒的有效应对措施和未来大流行防范的框架内，我们的主要目标是开发一种室内环境感染风险预测模型。已对湿度、温度、一氧化碳和颗粒物浓度（即PM10和PM2.5）等参数进行评估并建模，作为室内空气质量指标，这些测量值被组合起来生成一种预测算法，专门用于提供有关新冠病毒和空气传播传染病在室内空间传播动态的信息。这种新开发的感染风险预测算法（APRI）依赖于严格的分析，并根据温度、湿度和一氧化碳水平确定了不同的风险阈值。该模型显示环境因素之间存在显著关联，如温度、一氧化碳水平、湿度和颗粒物浓度。已强调了PM10和PM2.5在塑造室内环境空气质量方面的关键作用，因为在我们的预测模型中，低颗粒物浓度对应空气传播传染病的最低风险，而中等或高颗粒物水平与温度、湿度和一氧化碳水平的变化相关，因此对应感染风险升高，特别是在像新冠病毒这样高度传播性疾病的背景下。
