Digital twin based systems and methods for business continuity plan and safe return to workplace

Abstract

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.