Epidemics with asymptomatic transmission: Subcritical phase from recursive contact tracing

Area of Science:

• Epidemiology
• Network Science
• Infectious Disease Modeling

Background:

• COVID-19 highlighted the need for new infectious disease control methods.
• Asymptomatic transmission in SARS-CoV-2 complicates traditional contact tracing and quarantine strategies.
• The impact of asymptomatic transmission on epidemic thresholds in network models is understudied.

Purpose of the Study:

• To investigate the effectiveness of recursive contact tracing for epidemic control.
• To analyze the epidemic threshold in network models with asymptomatic transmission.
• To determine the network participation required for recursive contact tracing success.

Main Methods:

• Modeling epidemic spreading on networks using a simple epidemic network model.
• Incorporating a recursive contact tracing algorithm for instant quarantining.
• Analyzing critical percolation transitions for diseases with non-zero asymptomatic rates.

Main Results:

• Standard contact tracing becomes ineffective at suppressing epidemics above a certain asymptomatic transmission fraction.
• Recursive contact tracing demonstrates potential for containing epidemics with significant asymptomatic or presymptomatic spread.
• Calculated the necessary network participation for recursive contact tracing across various network structures and recursion depths.

Conclusions:

• Recursive contact tracing offers a viable strategy for managing infectious diseases with high asymptomatic transmission rates.
• Digital, app-based tools leveraging recursive contact tracing can enhance epidemic preparedness.
• The study provides a framework for understanding and optimizing contact tracing for future public health challenges.