The outbreak of COVID-19 constitutes an unprecedented disruption globally, in which risk management framework is on top priority in many countries. Travel restriction and home/office quarantine are some frequently utilized non-pharmaceutical interventions, which bring the worst crisis of airline industry compared with other transport modes. Therefore, the post-recovery of global air transport is extremely important, which is full of uncertainty but rare to be studied. The explicit/implicit interacted factors generate difficulties in drawing insights into the complicated relationship and policy intervention assessment. In this paper, a Causal Bayesian Network (CBN) is utilized for the modelling of the post-recovery behaviour, in which parameters are synthesized from expert knowledge, open-source information and interviews from travellers. The tendency of public policy in reaction to COVID-19 is analyzed, whilst sensitivity analysis and forward/backward belief propagation analysis are conducted. Results show the feasibility and scalability of this model. On condition that no effective health intervention method (vaccine, medicine) will be available soon, it is predicted that nearly 120 days from May 22, 2020, would be spent for the number of commercial flights to recover back to 58.52%–60.39% on different interventions. This intervention analysis framework is of high potential in the decision making of recovery preparedness and risk management for building the new normal of global air transport.

COVID-19 的爆发在全球范围内造成了前所未有的破坏，其中风险管理框架在许多国家/地区是重中之重。旅行限制和家庭/办公室隔离是一些经常使用的非药物干预措施，与其他运输方式相比，这给航空业带来了最严重的危机。因此，全球航空运输复苏后的情况极为重要，充满不确定性，却鲜有研究。显性/隐性相互作用的因素导致难以深入了解复杂的关系和政策干预评估。在本文中，因果贝叶斯网络 (CBN) 用于对恢复后行为进行建模，其中参数是根据专家知识、开源信息和旅行者的访谈合成的。分析了公共政策对 COVID-19 的反应趋势，同时进行了敏感性分析和前向/后向信念传播分析。结果表明该模型的可行性和可扩展性。在短期内尚无有效的健康干预手段（疫苗、药物）问世的情况下，预计从2020年5月22日起，将用近120天的时间，商业航班数量恢复至58.52%~60.39%不同的干预措施。该干预分析框架在建立全球航空运输新常态的恢复准备和风险管理决策中具有很高的潜力。结果表明该模型的可行性和可扩展性。在短期内尚无有效的健康干预手段（疫苗、药物）问世的情况下，预计从2020年5月22日起，将用近120天的时间，商业航班数量恢复至58.52%~60.39%不同的干预措施。该干预分析框架在建立全球航空运输新常态的恢复准备和风险管理决策中具有很高的潜力。结果表明该模型的可行性和可扩展性。在短期内尚无有效的健康干预手段（疫苗、药物）问世的情况下，预计从2020年5月22日起，将用近120天的时间，商业航班数量恢复至58.52%~60.39%不同的干预措施。该干预分析框架在建立全球航空运输新常态的恢复准备和风险管理决策中具有很高的潜力。