Network-Informed Constrained Divisive Pooled Testing Assignments.

Area of Science:

• Epidemiology
• Network Science
• Computational Biology

Background:

• Universal testing is effective for infectious disease outbreak prevention but can be costly in large populations.
• Group testing (pooled testing) reduces costs by testing groups, but existing methods often ignore individual contact patterns.

Purpose of the Study:

• To develop a cost-effective group testing strategy that incorporates individual contact patterns for infectious disease surveillance.
• To improve upon existing group testing methods by accounting for the underlying transmission network.

Main Methods:

• Developed a constrained divisive hierarchical clustering algorithm to assign individuals to pools based on contact patterns.
• Utilized simulation studies based on real-world contact networks to evaluate the algorithm's performance.

Main Results:

• The proposed approach demonstrated significant benefits compared to random pool assignments.
• The method remains effective even when the contact network data is imperfectly measured or has high missingness.

Conclusions:

• Incorporating contact network structures into group testing significantly enhances cost-effectiveness for infectious disease surveillance.
• This network-informed pooling strategy offers a robust and efficient solution for public health resource allocation.