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Personnel Scheduling during the COVID-19 Pandemic: A Probabilistic Graph-Based Approach.

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Optimizing staff scheduling and testing plans can significantly reduce infection risks by 25-60%. Planned testing strategies are most effective for minimizing workplace virus transmission.

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Area of Science:

  • Operations Research
  • Epidemiology
  • Public Health

Background:

  • Effective personnel scheduling is vital for organizations, but vulnerable to disruptions like pandemics.
  • Infectious disease outbreaks necessitate strategies balancing operational needs with employee safety.
  • Workplace safety during outbreaks requires managing on-site density and implementing testing protocols.

Purpose of the Study:

  • To develop and evaluate mathematical models for optimal staff scheduling and testing strategies.
  • To minimize infection risk among employees while meeting organizational demands.
  • To compare the effectiveness of planned versus random testing strategies.

Main Methods:

  • Formulation of two Mixed Integer Non-linear Programming (MINLP) models.
  • Development of a canonical genetic algorithm for problem-solving.
  • Utilizing real and synthetic employee contact network data for analysis.

Main Results:

  • Recommended occupancy and testing strategies reduced infection risk by 25-60% across various scenarios.
  • Planned testing strategies were found to be superior to random testing for minimizing infection risk.
  • Employee vaccination status and interaction rates are key factors influencing infection risk.

Conclusions:

  • Optimized scheduling and testing plans are crucial for mitigating workplace infection spread.
  • Proactive, planned testing significantly enhances the effectiveness of infection control strategies.
  • Integrating individual factors like vaccination and interaction rates improves scheduling model efficacy.