  We present "interoperability" as a guiding framework for statistical
modelling to assist policy makers asking multiple questions using diverse
datasets in the face of an evolving pandemic response. Interoperability
provides an important set of principles for future pandemic preparedness,
through the joint design and deployment of adaptable systems of statistical
models for disease surveillance using probabilistic reasoning. We illustrate
this through case studies for inferring spatial-temporal coronavirus disease
2019 (COVID-19) prevalence and reproduction numbers in England.
