Pooled testing of traced contacts under superspreading dynamics.

Area of Science:

• Epidemiology
• Infectious Disease Modeling
• Public Health

Background:

• Contact tracing is crucial for managing infectious diseases like COVID-19.
• Current pooled testing strategies often do not leverage contact tracing information.
• Optimizing testing efficiency is vital during pandemics.

Purpose of the Study:

• To develop a novel pooled testing strategy that integrates contact tracing data.
• To improve the efficiency of COVID-19 testing by reducing the total number of tests required.
• To compare the performance of the new method against existing pooled testing approaches.

Main Methods:

• Developed a dynamic programming-based pooled testing algorithm.
• Incorporated contact tracing information into the pool design.
• Simulated testing scenarios using various epidemiological parameters (e.g., reproduction numbers, dispersion levels).
• Evaluated performance based on the total number of tests needed.

Main Results:

• The proposed method significantly reduces the number of tests compared to Dorfman's method.
• The greatest advantage is observed with a small number of contacts or high overdispersion of secondary infections.
• The method remains effective even with imperfect contact tracing and dilution.

Conclusions:

• A dynamic programming approach utilizing contact tracing data offers a more efficient pooled testing strategy.
• This method is particularly beneficial in specific epidemiological contexts, enhancing pandemic response.
• The strategy demonstrates robustness against common real-world challenges in contact tracing and testing.