  The worldwide spread of COVID-19 has called for fast advancement of new
modelling strategies to estimate its unprecedented spread. Here, we introduce a
model based on the fundamental SIR equations with a stochastic disorder by a
random exchange of infected populations between cities to study dynamics in an
interacting network of epicentres in a model state. Although each stochastic
exchange conserves populations pair-wise, the disorder drives the global system
towards newer routes to dynamic equilibrium. Upon controlling the range of the
exchange fraction, we show that it is possible to control the heterogeneity in
the spread and the co-operativity among the interacting hotspots. Data of
collective temporal evolution of the infected populations in federal states of
Germany validate the qualitative features of the model.
