COVID-19 appeared abruptly in early 2020, requiring a rapid response amid a context of great uncertainty. Good quality data and knowledge was initially lacking, and many early models had to be developed with causal assumptions and estimations built in to supplement limited data, often with no reliable approach for identifying, validating and documenting these causal assumptions. Our team embarked on a knowledge engineering process to develop a causal knowledge base consisting of several causal BNs for diverse aspects of COVID-19. The unique challenges of the setting lead to experiments with the elicitation approach, and what emerged was a knowledge engineering method we call Causal Knowledge Engineering (CKE). The CKE provides a structured approach for building a causal knowledge base that can support the development of a variety of application-specific models. Here we describe the CKE method, and use our COVID-19 work as a case study to provide a detailed discussion and analysis of the method.

As has been argued in [Rod91] and [FOP92b], there is a growing demand for information systems, not only allowing for changes of their information base, but also for modifications in their underlying structure (conceptual schema and specification of dynamic aspects). In case of snapshot databases, structure modifications will lead to costly data conversions and reprogramming. The intention of an evolving information system ([FOP92a], [OPF94]) is to be able to handle updates of all components of the so-called application model, containing the information structure, the constraints on this structure, the population conforming to this structure and the possible operations. The theory of such systems should, however, be independent of whatever modelling technique is used to describe the application model. In this paper, we discuss a general theory for the evolution of application models. However, only conceptual aspects are considered, focus is on what evolution is, rather than on how to implement evolution in a database manegement system. In [PW93], an informal introduction to this theory is provided.