The containment of infectious diseases is challenging due to complex transmutation in the biological system, intricate global interactions, intense mobility, and multiple transmission modes. An emergent disease has the potential to turn into a pandemic impacting millions of people with loss of life, mental health, and severe economic impairment. Multifarious approaches to risk management have been explored for combating an epidemic spread. This work presents the implementation of engineering safety principles to pandemic risk management. We have assessed the pandemic risk using Paté-Cornell's six levels of uncertainty. The susceptible, exposed, infected, quarantined, recovered, deceased (SEIQRD), an advanced mechanistic model, along with the Monte Carlo simulation, has been used to estimate the fatality risk. The risk minimization strategies have been categorized into hierarchical safety measures. We have developed an event tree model of pandemic risk management for distinct risk-reducing strategies realized due to natural evolution, government interventions, societal responses, and individual practices. The roles of distinct interventions have also been investigated for an infected individual's survivability with the existing healthcare facilities. We have studied the Corona Virus Disease of 2019 (COVID-19) for pandemic risk management using the proposed framework. The results highlight effectiveness of the proposed strategies in containing a pandemic.

由于生物系统中复杂的嬗变、复杂的全球相互作用、强烈的流动性和多种传播模式，传染病的遏制具有挑战性。一种新出现的疾病有可能演变成一场大流行，影响数百万人，造成生命损失、精神健康和严重的经济损害。为了对抗流行病的蔓延，人们探索了多种风险管理方法。这项工作介绍了工程安全原则在流行病风险管理中的实施。我们使用帕特-康奈尔大学的六个不确定性级别评估了大流行风险。易感者、暴露者、感染者、隔离者、康复者、死亡者（SEIQRD）是一种先进的机制模型，结合蒙特卡罗模拟，已被用来估计死亡风险。风险最小化策略已分为分层安全措施。我们开发了流行病风险管理的事件树模型，用于因自然演变、政府干预、社会反应和个人实践而实现的不同风险降低策略。还研究了不同干预措施对于感染者在现有医疗设施中的生存能力的作用。我们使用拟议的框架研究了 2019 年冠状病毒病 (COVID-19)，以进行大流行风险管理。结果凸显了所提出的遏制大流行策略的有效性。