Minimizing outbreak through targeted blocking for disease control: a community-based approach using super-spreader...

Area of Science:

• Epidemiology
• Network Science
• Computational Biology

Background:

• The COVID-19 pandemic highlighted the need for effective disease control strategies beyond vaccination.
• Limited vaccine supply necessitates targeted interventions to manage infectious disease outbreaks.
• Identifying key individuals for intervention is crucial for early-stage disease containment.

Purpose of the Study:

• To propose a novel network-based method for identifying critical nodes to block disease transmission.
• To minimize the set of individuals needing intervention while preventing large-scale outbreaks.
• To evaluate the proposed method's effectiveness against existing strategies.

Main Methods:

• The study frames disease spread control as a network problem, similar to identifying super-spreader nodes.
• A community detection approach is used to partition the network and identify critical connecting nodes.
• The proposed method aims to find the minimum set of nodes whose removal disconnects the network.

Main Results:

• The proposed method demonstrated superior performance in controlling disease spread compared to other approaches.
• Evaluations were conducted on both real-world and synthetically generated networks (LFR algorithm).
• Blocking identified connecting nodes effectively limited disease transmission between communities.

Conclusions:

• The developed network-based strategy offers an effective approach for targeted disease control.
• Community partitioning and blocking connecting nodes are key to limiting epidemic spread.
• This method provides a valuable tool for public health interventions, especially with resource constraints.