Adaptive group testing strategy for infectious diseases using social contact graph partitions

Area of Science:

• Epidemiology and Public Health
• Computational Biology and Network Science
• Infectious Disease Modeling

Background:

• Mass testing is critical for epidemic control, but testing capacity is often limited.
• Traditional group testing methods like Dorfman's do not account for disease dynamics or social correlations.
• Existing methods fail to optimize testing strategies in real-world epidemiological scenarios.

Purpose of the Study:

• To develop an adaptive group testing (AGT) strategy to improve epidemic testing efficiency.
• To address limitations of existing group testing methods by incorporating network structure and disease dynamics.
• To maximize infected individual detection while minimizing test usage and disease spread.

Main Methods:

• Developed an adaptive group testing (AGT) strategy based on graph partitioning of social contact networks.
• Implemented an enhanced infectious disease transmission model to simulate pathogen spread dynamics.
• Evaluated AGT performance across 13 diverse social contact networks.

Main Results:

• AGT demonstrated significant performance improvements over Dorfman's method and its variations.
• The AGT strategy required fewer tests overall and effectively reduced disease spread.
• AGT showed robustness across varying group sizes, testing capacities, and network parameters.

Conclusions:

• Adaptive group testing based on graph partitioning offers a more efficient approach to epidemic testing.
• AGT enhances disease detection and mitigation by leveraging social contact network information.
• This strategy provides valuable guidance for public health policy in balancing disease control and societal activity.