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Using simulation modelling and systems science to help contain COVID-19: A systematic review.

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|October 17, 2022
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Summary

This review analyzes simulation models like agent-based modeling (ABM) and system dynamics (SDM) in COVID-19 research. Hybrid models are crucial for evaluating complex interventions and societal impacts.

Keywords:
COVID‐19 pandemicagent‐based modeldiscrete event simulationsystem dynamics modelsystematic review

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Area of Science:

  • Public Health
  • Computational Epidemiology
  • Health Systems Research

Background:

  • COVID-19 research extensively utilized simulation modeling.
  • Understanding pandemic dynamics and intervention effectiveness is critical.

Purpose of the Study:

  • To systematically review simulation approaches (SDM, ABM, DES, hybrids) in COVID-19 research.
  • To identify theoretical and application innovations in public health.

Main Methods:

  • Systematic literature review of 372 eligible papers.
  • Categorization of studies by research focus (transmission, interventions, prediction, impacts) and simulation methodology.

Main Results:

  • Agent-based modeling (ABM) was the most prevalent approach (275 papers).
  • Intervention evaluation and design was the most common research area (55%).
  • Hybrid models are increasingly needed for complex socio-economic impact assessments.

Conclusions:

  • Simulation models, particularly ABM, are vital tools in COVID-19 research.
  • Hybrid simulation models offer advanced capabilities for evaluating multifaceted public health interventions.
  • Further research should focus on developing and applying hybrid models for comprehensive impact analysis.