Organizations are struggling to ensure
business continuity without compromising on delivery excellence in the face of
pandemic related uncertainties which exists along multiple dimensions effected by authorities. This uncertainty plays out in a non-uniform manner thus leading to highly heterogeneous evolution of
pandemic. Present disclosure provides digital twin based systems and methods for
business continuity plan and safe return to workplace wherein a
simulation-based data-driven evidence-backed approach is implemented that captures details pertaining to
virus, individualistic characteristics of employees and their dependents, offices, locations of the employees and offices, and various
pandemic control measures that are in effect and need to be explored using a
hybrid modelling and
simulation approach that combines fine-grained actor / agent model and coarse-grained stock-and-flow model. The present disclosure further leverages past
macro-
level data pertaining to pandemic evolution of relevant cities, states, and countries to make available information amenable for collective analysis and infection prediction.