Optimization of testing protocols to screen for COVID-19: a multi-objective model

Area of Science:

• Epidemiology and Public Health
• Computational Modeling
• Operations Research

Background:

• The COVID-19 pandemic highlighted the need for effective infectious disease screening strategies.
• Optimizing testing protocols involves balancing multiple competing objectives such as cost and disease transmission.
• Existing methods may not adequately address the complexities of real-world screening scenarios in congregate settings.

Purpose of the Study:

• To develop and present a novel multi-objective simulated annealing (MOSA) algorithm for optimizing infectious disease testing protocols.
• To apply this algorithm to the context of COVID-19 screening within K-12 school districts.
• To provide a scalable and adaptable tool for designing effective testing strategies in various congregate settings.

Main Methods:

• Development of a multi-objective simulated annealing (MOSA) algorithm.
• Integration of a susceptible-exposed-infected-recovered (SEIR) epidemiological model as the computational engine.
• Optimization focused on minimizing test material costs, total infections, and false negatives over a defined testing horizon.

Main Results:

• The MOSA algorithm successfully generated optimal testing protocols for infectious diseases.
• Demonstrated application in recommending screening strategies for North Carolina K-12 school districts.
• The approach is scalable and can be adapted for diverse congregate settings like schools, businesses, and nursing homes.

Conclusions:

• The developed MOSA algorithm offers a robust framework for optimizing infectious disease testing protocols.
• Findings provide valuable insights for policy decisions regarding COVID-19 and future pandemic disease control.
• The tool can generate location-specific or common protocols across multiple testing sites.