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Predictive algorithm for COVID-19 infection risk in indoor environments.

Chiara Rucco1, Prisco Piscitelli2, Antonella Longo3,4

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Summary
This summary is machine-generated.

Indoor air quality significantly impacts airborne disease spread. This study introduces an Algorithm for the Prediction of Risk of Infections (APRI) using IoT sensors to forecast COVID-19 transmission risk based on environmental factors.

Keywords:
Air pollutionAir quality monitoringAlgorithmEnvironmental pollutionGlobal pandemic (COVID-19)Particulate matterPredictive

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Area of Science:

  • Environmental Health
  • Epidemiology
  • Sensor Technology

Background:

  • The COVID-19 pandemic highlighted the critical link between indoor environments and airborne disease transmission.
  • Poor indoor air quality is a significant factor in the spread of infectious diseases, necessitating research into mitigation strategies.
  • Internet of Things (IoT) devices offer potential for comprehensive indoor environmental monitoring.

Purpose of the Study:

  • To develop a predictive model for indoor airborne infectious disease risk, including COVID-19.
  • To utilize IoT devices for real-time monitoring of indoor environmental parameters.
  • To create an algorithm for forecasting infection transmission dynamics in indoor spaces.

Main Methods:

  • Collected data on indoor environmental factors including temperature, humidity, CO2, PM10, and PM2.5 concentrations.
  • Developed a predictive algorithm, the Algorithm for the Prediction of Risk of Infections (APRI), integrating these parameters.
  • Established risk thresholds based on combinations of environmental factors.

Main Results:

  • Significant associations were found between environmental factors (temperature, humidity, CO2, PM) and airborne disease transmission risk.
  • Particulate Matter (PM10 and PM2.5) concentrations played a pivotal role; low levels correlated with minimal risk.
  • Elevated PM levels, combined with variations in temperature, humidity, and CO2, indicated an increased risk of infection.

Conclusions:

  • The APRI model effectively predicts airborne infectious disease risk in indoor environments.
  • Environmental monitoring using IoT devices is crucial for understanding and mitigating disease spread.
  • This research contributes to pandemic preparedness by providing a tool for assessing indoor infection risk.