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

Hadi Moheb-Alizadeh1,2, Donald P Warsing3, Richard E Kouri4

  • 1Graduate Program in Operations Research, North Carolina State University, Raleigh, NC, 27695, USA.

Health Care Management Science
|October 11, 2024
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Summary
This summary is machine-generated.

This study introduces a multi-objective simulated annealing (MOSA) algorithm to optimize infectious disease testing protocols, balancing cost, infection spread, and false negatives for COVID-19 control in schools.

Keywords:
COVID-19Multiple objective programmingOR in health servicesSEIR modelSimulated annealing

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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.