How can process safety and a risk management approach guide pandemic risk management?

Area of Science:

• Epidemiology
• Process Safety Engineering
• Computational Modeling

Background:

• The COVID-19 pandemic necessitated global risk management strategies.
• Existing prediction models require enhancement for dynamic risk assessment.

Purpose of the Study:

• To propose an advanced mechanistic model framework for COVID-19 risk management.
• To develop and compare parameter learning models for effective dynamic risk forecasting.
• To analyze the impact of non-pharmaceutical interventions on pandemic risk.

Main Methods:

• Utilized an advanced mechanistic model integrated with process safety tools.
• Developed parameter tweaking and artificial neural network-based parameter learning models.
• Employed Monte Carlo simulation for parameter randomness and the SEIQRD model for comparative analysis across four locations.
• Applied Layer of Protection Analysis to assess non-pharmaceutical interventions.

Main Results:

• The artificial neural network-based learning approach demonstrated superior performance in risk forecasting compared to other tested models.
• Stage-wise release scenarios were identified as the most effective strategy for minimizing pandemic resurgence.
• Layer of Protection Analysis effectively evaluated the impact of non-pharmaceutical interventions.

Conclusions:

• The proposed framework offers valuable insights for risk assessment and management in both health and process industries.
• Mathematical modeling and safety tools can be synergistically applied across sectors for crisis management.
• Cross-sectoral learning from crises enhances preparedness and response strategies.