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The mathematical interpretation of interventions for the mitigation of epidemics and pandemics in the literature often involves finding the optimal time to initiate an intervention and/or the use of infections to manage impact. Whilst these methods may work in theory, in order to implement they may require information which is likely not available whilst one is in the midst of an epidemic, or they may require impeccable data about infection levels in the community. In practice, testing and cases data is only as good as the policy of implementation and the compliance of the individuals, which means that understanding the levels of infections becomes difficult or complicated from the data that is provided. In this paper, we aim to develop a different approach to the mathematical modelling of interventions, not based on optimality, but based on demand and capacity of local authorities who have to deal with the epidemic on a day to day basis. In particular, we use data-driven modelling to calibrate an Susceptible Exposed Infectious Recovered-Died (SEIR-D) model to infer parameters that depict the dynamics of the epidemic in a region of the UK. We use the calibrated parameters for forecasting scenarios and understand, given a maximum capacity of hospital healthcare services, how the timing of interventions, severity of interventions, and conditions for the releasing of interventions affect the overall epidemic-picture.