COVID-19 Contagion Risk Estimation Model for Indoor Environments.

Area of Science:

• Environmental Health
• Infectious Disease Epidemiology
• Mobile Health Technology

Background:

• COVID-19 transmission is primarily through aerosols, with indoor environments posing significant risks due to poor ventilation.
• Physical distancing alone is insufficient to prevent airborne transmission in enclosed spaces.
• Improving indoor air quality and monitoring contagion risk are crucial public health measures.

Purpose of the Study:

• To develop and implement an accurate model for COVID-19 contagion risk estimation in indoor environments.
• To create a user-friendly Android mobile application integrating this risk assessment model.
• To provide real-time alerts and social distancing support to mitigate virus transmission.

Main Methods:

• Utilized the Wells-Riley probabilistic approach for airborne contagion modeling.
• Developed an Android application incorporating environmental, demographic, and activity-based risk factors.
• Integrated Bluetooth technology for real-time social distance monitoring and alerts.
• Calculated contagion probability based on air changes per hour (ACH), occupancy, and local COVID-19 prevalence.

Main Results:

• The model accurately estimates COVID-19 contagion risk across various indoor settings (offices, restaurants, classrooms, libraries).
• The mobile app provides real-time alerts for high contagion probability and elevated CO2 concentrations.
• Bluetooth-enabled distance monitoring aids in maintaining effective social distancing.

Conclusions:

• The proposed mobile application is an effective tool for reducing COVID-19 spread in indoor public spaces.
• Real-time risk assessment and alerts empower individuals and organizations to manage transmission risks.
• The integration of ventilation data, occupancy limits, and social distancing features enhances indoor safety.