Capturing COVID-19 spread and interplay with multi-hop contact tracing intervention.

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• COVID-19 transmission necessitates effective contact tracing strategies.
• Traditional contact tracing focuses on direct contacts, potentially missing wider spread.
• Multi-hop contact tracing extends to secondary and tertiary contacts for enhanced containment.

Purpose of the Study:

• To develop a mathematical methodology for evaluating preemptive multi-hop contact tracing.
• To compare the cost-benefit tradeoff of multi-hop versus traditional contact tracing.
• To elucidate the mechanisms underlying multi-hop tracing effectiveness.

Main Methods:

• Development of a mathematical framework combining contact tracing dynamics and virus transmission using microscopic Markov Chain approach (MMCA).
• Validation of the MMCA model through Monte Carlo (MC) simulations.
• Application of the framework to human contact networks derived from real-world data.

Main Results:

• The proposed MMCA model accurately reflects MC simulation outcomes.
• Multi-hop contact tracing significantly enhances the cost-benefit tradeoff compared to traditional methods.
• The framework demonstrates computational efficiency for analyzing large contact networks.

Conclusions:

• The mathematical framework provides an effective tool for evaluating multi-hop contact tracing strategies.
• Multi-hop tracing offers a more cost-effective approach to managing infectious disease spread.
• The computational efficiency of the framework facilitates analysis of real-world, large-scale contact networks.