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

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.