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Optimizing Large-Scale COVID-19 Nucleic Acid Testing with a Dynamic Testing Site Deployment Strategy.

Xiaozhou He1,2, Li Luo1, Xuefeng Tang3

  • 1Business School, Sichuan University, Chengdu 610065, China.

Healthcare (Basel, Switzerland)
|February 11, 2023
PubMed
Summary
This summary is machine-generated.

Optimizing large-scale COVID-19 nucleic acid testing site deployment using a dynamic strategy significantly reduces costs. This approach considers population distribution and resource availability, outperforming static plans.

Keywords:
COVID-19 controldynamic site deploymentlarge-scale testingspatial–temporal demands

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Area of Science:

  • Epidemiology
  • Operations Research
  • Public Health

Background:

  • The COVID-19 pandemic necessitated large-scale nucleic acid testing to control transmission.
  • Effective deployment of testing sites is crucial but challenging due to population dynamics and resource constraints.

Purpose of the Study:

  • To optimize large-scale COVID-19 nucleic acid testing site deployment.
  • To develop a dynamic testing site strategy considering spatial-temporal population distribution and resource availability.

Main Methods:

  • Proposed a multiperiod location-allocation model for dynamic testing site deployment.
  • Developed comparison models with static site deployment strategies.
  • Validated the model with a real-world case study in Chengdu, China.

Main Results:

  • The dynamic strategy achieved a 15% cost reduction compared to a practical plan and a 2% reduction compared to other static strategies.
  • Sensitivity analysis provided insights for practical testing site deployment.
  • The dynamic model demonstrated improved cost-effectiveness and robustness.

Conclusions:

  • Dynamic deployment of COVID-19 testing sites, based on population distribution, is crucial for cost reduction.
  • This strategy enhances the robustness of testing plans amidst limited medical resources.
  • The proposed model offers valuable insights for public health resource allocation during epidemics.