Targeted pandemic containment through identifying local contact network bottlenecks

Area of Science:

• Network science
• Computational epidemiology
• Mathematical modeling

Background:

• Simulation modeling is crucial for pandemic mitigation strategies.
• Disease transmission models utilize contact networks between individuals and populations.
• Real-world networks possess complex structures influencing transmission dynamics.

Purpose of the Study:

• To propose a novel flow-based edge-betweenness centrality method for detecting bottleneck edges in contact networks.
• To enhance the identification of crucial connections for effective intervention strategies.
• To improve the speed and accuracy of network analysis for epidemiological modeling.

Main Methods:

• Development of a flow-based edge-betweenness centrality method utilizing convex optimization and p-norm network flow.
• Application of the method to real-world network data at individual and county levels.
• Simulation of COVID-19 transmission dynamics on these networks.

Main Results:

• The proposed method identifies bottleneck edges more effectively than state-of-the-art techniques.
• Targeting identified bottleneck edges reduced COVID-19 cases by up to 10% more than existing methods.
• The new method is significantly faster, operating orders of magnitude quicker than current approaches.

Conclusions:

• The proposed flow-based centrality method offers a superior approach for identifying critical network connections in epidemiological studies.
• This method provides a computationally efficient and effective tool for pandemic mitigation planning.
• The findings have implications for optimizing public health interventions by targeting key network structures.