Mitigate SIR epidemic spreading via contact blocking in temporal networks

Area of Science:

• Epidemiology
• Network Science
• Computational Biology

Background:

• Temporal networks model dynamic human interactions crucial for disease transmission.
• Existing strategies often focus on node or pair removal, which can be disruptive.
• Mitigating epidemics on evolving contact networks requires targeted intervention strategies.

Purpose of the Study:

• To develop and evaluate novel contact blocking strategies for temporal networks.
• To identify optimal methods for removing a fraction of contacts to suppress epidemic spread.
• To understand the relationship between link centrality, contact removal, and epidemic mitigation.

Main Methods:

• Developed contact removal strategies based on link centrality metrics in aggregated temporal networks.
• Defined removal probability as a function of link centrality and contact time.
• Evaluated 12 centrality-based strategies and a random removal baseline on empirical networks.
• Measured epidemic mitigation using average prevalence, peak prevalence, and time to peak.

Main Results:

• The most effective strategy involved removing contacts between node pairs with fewer and earlier interactions.
• Strategies performing better exhibited less variation in the average number of contacts removed per node pair.
• The optimal contact removal strategy resulted in a pruned network with a large largest eigenvalue and high modularity.

Conclusions:

• Targeted contact removal, prioritizing low-frequency and early interactions, is a viable method for epidemic suppression.
• Network properties of the pruned network, such as spectral properties and community structure, correlate with effective mitigation.
• This approach offers a nuanced alternative to complete node or pair blocking for managing infectious disease spread.