Pandemic risk management using engineering safety principles.

Area of Science:

• Public Health and Epidemiology
• Risk Management and Engineering Safety
• Mathematical Modeling and Simulation

Background:

• Infectious disease containment is complex due to biological, global, mobility, and transmission factors.
• Emergent diseases pose significant risks, including loss of life, mental health impacts, and economic disruption.
• Existing epidemic risk management approaches require robust frameworks for effective control.

Purpose of the Study:

• To implement engineering safety principles for enhanced pandemic risk management.
• To assess pandemic risk using established uncertainty frameworks and advanced modeling.
• To evaluate the effectiveness of hierarchical safety measures and interventions in containing pandemics.

Main Methods:

• Utilized Paté-Cornell's six levels of uncertainty for risk assessment.
• Employed the susceptible, exposed, infected, quarantined, recovered, deceased (SEIQRD) model with Monte Carlo simulation to estimate fatality risk.
• Developed an event tree model for pandemic risk management, incorporating natural evolution, interventions, societal, and individual responses.

Main Results:

• The SEIQRD model and Monte Carlo simulation provided quantitative fatality risk estimations.
• Hierarchical safety measures were categorized, and their effectiveness in risk reduction was analyzed.
• The framework demonstrated the significant role of various interventions in improving infected individual survivability and containing disease spread, as shown in the COVID-19 case study.

Conclusions:

• The proposed engineering safety framework offers a structured approach to pandemic risk management.
• The integrated modeling and risk assessment strategies are effective in identifying and mitigating pandemic threats.
• The study validates the proposed strategies' potential for containing pandemics, exemplified by the COVID-19 analysis.