IoT-based cloud framework to control Ebola virus outbreak.

Area of Science:

• Public Health
• Epidemiology
• Health Informatics

Background:

• Ebola virus disease (EVD) poses a significant public health threat due to rapid human-to-human transmission.
• Continuous patient monitoring and early detection are crucial for controlling EVD outbreaks.
• Internet of Things (IoT) and cloud computing offer promising solutions for remote patient monitoring.

Purpose of the Study:

• To propose a novel architecture for early detection and remote monitoring of Ebola infected patients.
• To prevent the spread of EVD during the initial stages of an outbreak.
• To leverage emerging technologies for proactive healthcare services.

Main Methods:

• A system integrating Radio Frequency Identification Device (RFID), wearable sensors, and cloud computing infrastructure was developed.
• The J48 decision tree algorithm was employed for symptom-based infection level evaluation.
• Temporal Network Analysis (TNA) was utilized to monitor outbreak dynamics using proximity interaction data.

Main Results:

• The proposed model demonstrated 94% accuracy in classifying infection levels.
• Resource utilization efficiency reached 92% during performance evaluation.
• The system effectively utilized synthetic data from two million users on Amazon EC2 cloud.

Conclusions:

• The novel architecture provides an effective solution for early EVD detection and patient monitoring.
• The integration of RFID, sensors, and cloud computing enhances proactive public health interventions.
• The system shows high accuracy and efficiency in managing potential EVD outbreaks.