Artificial intelligence-inspired comprehensive framework for Covid-19 outbreak control

Area of Science:

• Medical Informatics
• Epidemiology
• Internet of Things (IoT)

Background:

• COVID-19 poses a significant global health threat requiring continuous patient identification and remote monitoring.
• Internet of Things (IoT) technology, integrated with fog-cloud computing, offers an efficient solution for real-time remote patient surveillance.

Purpose of the Study:

• To propose a comprehensive framework for COVID-19 patient identification and management using IoT, RFID, and wearable sensors.
• To leverage fog-cloud architecture for efficient and time-sensitive remote medical service delivery.
• To assess the framework's performance in classifying infection severity and tracking disease spread.

Main Methods:

• A fog-cloud framework integrating Radio Frequency Identification Device (RFID) and body-wearable sensors for patient monitoring.
• Utilizing the J48 decision tree algorithm to assess infection degree based on user symptoms.
• Employing RFID for detecting Temporal Proximity Interactions (TPI) and Temporal Network Analysis for tracking COVID-19 spread.

Main Results:

• The proposed framework achieved high classification accuracy (96.68%) and sensitivity (94.65%) in statistical assessments.
• Significant improvements were observed in Temporal Delay efficacy, Precision, and F-measure for the fog-cloud-based data analysis.
• The system demonstrated effective performance in identifying infected individuals and analyzing disease transmission dynamics.

Conclusions:

• The developed fog-cloud-based IoT framework provides an effective and accurate solution for remote COVID-19 patient surveillance and management.
• The integration of RFID and wearable sensors, coupled with advanced analytical techniques, enhances the ability to monitor and control infectious disease outbreaks.
• This approach offers a promising direction for improving public health responses to pandemics through technological innovation.