Digital Contact Tracing Based on a Graph Database Algorithm for Emergency Management During the COVID-19 Epidemic:...

Area of Science:

• Public Health
• Epidemiology
• Data Science

Background:

• The COVID-19 pandemic necessitates effective contact tracing, but traditional methods face significant limitations.
• Digital technology offers a promising avenue for comprehensive, efficient, and precise contact tracing during public health emergencies.

Purpose of the Study:

• To introduce novel solutions for overcoming traditional contact tracing limitations.
• To detail the organizational and technical processes of digital contact tracing in Hainan Province.

Main Methods:

• Application of a graph database algorithm on a governmental big data platform for analyzing multisource COVID-19 data.
• Building relational networks among infected individuals, the general population, vehicles, and public places.
• Summarizing organizational and technical processes through interviews and data analysis.

Main Results:

• Established an integrated emergency management command system and multi-agency coordination.
• Utilized a centralized big data platform for multisource epidemic data management.
• Successfully identified and traced 10,871 contacts, including 378 close contacts and high-risk public places, using the graph database algorithm.

Conclusions:

• Hainan Province's digital contact tracing model, using a graph database algorithm, enables rapid and accurate identification of infected individuals and high-risk locations.
• This approach supports evidence-based emergency management and enhances public health emergency response systems.
• Future optimization should focus on data security, tracing accuracy, intelligent data collection, and improved data-sharing mechanisms.