Pandemic viruses have historically caused tremendous damage to lives and livelihoods The coronavirus, COVID-19, has proven to be a significant issue around the world In this paper it is argued that systems of controlling similar types of disasters need to be improved through learning from past experience and from others, as well as through improved modelling for better decision making In doing so, the focus will be on resilience modelling and learning from incidents Therefore, in this paper, first the introduction deals with hybrid approaches in operational research highlighting the differences between hybrid modelling and hybrid models Second, an introduction to mathematical modelling of epidemics is provided and how such modelling leads to certain types of public health modelling is demonstrated Third, resilience modelling will be discussed as a complimentary type of modelling, where concepts related to robustness, redundancy, resourcefulness, and rapidity are introduced Fourth, resilience modelling will be extended to new principles taking COVID-19 as an example for the analysis Fifth, the analysis will then be used to compare degrees of resilience for different countries Finally, other modelling approaches for managing – and learning from – pandemics, in terms of root cause analysis, bowtie modelling and safety barriers, are proposed
Towards a new approach for managing pandemics: Hybrid resilience and bowtie modelling