  Coronavirus COVID-19 spreads through the population mostly based on social
contact. To gauge the potential for widespread contagion, to cope with
associated uncertainty and to inform its mitigation, more accurate and robust
modelling is centrally important for policy making. We provide a flexible
modelling approach that increases the accuracy with which insights can be made.
We use this to analyse different scenarios relevant to the COVID-19 situation
in the UK. We present a stochastic model that captures the inherently
probabilistic nature of contagion between population members. The computational
nature of our model means that spatial constraints (e.g., communities and
regions), the susceptibility of different age groups and other factors such as
medical pre-histories can be incorporated with ease. We analyse different
possible scenarios of the COVID-19 situation in the UK. Our model is robust to
small changes in the parameters and is flexible in being able to deal with
different scenarios.
  This approach goes beyond the convention of representing the spread of an
epidemic through a fixed cycle of susceptibility, infection and recovery (SIR).
It is important to emphasise that standard SIR-type models, unlike our model,
are not flexible enough and are also not stochastic and hence should be used
with extreme caution. Our model allows both heterogeneity and inherent
uncertainty to be incorporated. Due to the scarcity of verified data, we draw
insights by calibrating our model using parameters from other relevant sources,
including agreement on average (mean field) with parameters in SIR-based
models.
