A novel approach of adapting social distancing consideration into a SEIR model is presented, where susceptible, exposed and unidentified compartments are collated under the umbrella of the social-distanced compartment. Another key characteristic of the model is the inclusion of the nature of social distancing to be contingent on the rate of change of the active cases. The methodology and the results exhibiting an excellent fit to the data (upto 3rd March 2021) are presented, in case of the COVID-19 outbreak in India. The model attributed the apparently extensive social distancing, to the socio-geographical factors, unique to India. Also the data exhibited greater rate of infection from a diagnosed case as compared to undetected infection. Finally, it is demonstrated that a very conservative estimate of undiagnosed cases is at least $75%$ of the total number of cases.
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