  Phenomenological and deterministic models are often used for the estimation
of transmission parameters in an epidemic and for the prediction of its growth
trajectory. Such analyses are usually based on single peak outbreak dynamics.
In light of the present COVID-19 pandemic, there is a pressing need to better
understand observed epidemic growth with multiple peak structures, preferably
using first-principles methods. Along the lines of our previous work [Physica A
574, 126014 (2021)], here we apply 2D random-walk Monte Carlo calculations to
better understand COVID-19 spread through contact interactions. Lockdown
scenarios and all other control interventions are imposed through mobility
restrictions and a regulation of the infection rate within the stochastically
interacting population. The susceptible, infected and recovered populations are
tracked over time, with daily infection rates obtained without recourse to the
solution of differential equations.
  The simulations were carried out for population densities corresponding to
four countries, India, Serbia, South Africa and USA. In all cases our results
capture the observed infection growth rates. More importantly, the simulation
model is shown to predict secondary and tertiary waves of infections with
reasonable accuracy. This predictive nature of multiple wave structures
provides a simple and effective tool that may be useful in planning mitigation
strategies during the present pandemic.
