An Internet-of-Disease System for COVID-19 Testing Using Saliva by an AI-Controlled Microfluidic ELISA Device.

Area of Science:

• Biomedical Engineering
• Public Health Informatics
• Infectious Disease Surveillance

Background:

• Effective monitoring of infectious diseases like COVID-19 is crucial for public health guidance.
• Integrating information and communication technologies (ICTs) with point-of-care (POC) devices is vital for real-time health monitoring.

Purpose of the Study:

• To develop an Internet of Diseases (IoD) platform for continuous, real-time monitoring of infectious diseases.
• To create a system for diagnosing SARS-CoV-2 using a lab-on-a-chip (LOC) device and visualizing infection rates.

Main Methods:

• Developed an IoD platform integrating a lab-on-a-chip (LOC) device for SARS-CoV-2 diagnosis from saliva samples.
• Uploaded diagnostic data to a cloud system connected with disease control centers.
• Created a choropleth IoD map for visualizing provincial infection rates.

Main Results:

• The platform successfully quantified SARS-CoV-2 N-protein antigen with a limit of detection (LOD) as low as 0.013 ng mL⁻¹.
• Provincial infection rates were accurately projected using the developed IoD map.
• The system demonstrated real-time monitoring capabilities for public health.

Conclusions:

• The proposed IoD system serves as a valuable tool for disease control and prevention centers.
• It aids in restraining COVID-19 outbreaks by identifying high-severity regions.
• The system supports informed public health decision-making during outbreaks.