  In this paper, we develop an extension of standard epidemiological models,
suitable for COVID-19. This extension incorporates the transmission due to
pre-symptomatic or asymptomatic carriers of the virus. Furthermore, this model
also captures the spread of the disease due to the movement of people to/from
different administrative boundaries within a country. The model describes the
probabilistic rise in the number of confirmed cases due to the concomitant
effects of (incipient) human transmission and multiple compartments. The
associated parameters in the model can help architect the public health policy
and operational management of the pandemic. For instance, this model
demonstrates that increasing the testing for symptomatic patients does not have
any major effect on the progression of the pandemic, but testing rate of the
asymptomatic population has an extremely crucial role to play. The model is
executed using the data obtained for the state of Chhattisgarh in the Republic
of India. The model is shown to have significantly better predictive capability
than the other epidemiological models. This model can be readily applied to any
administrative boundary (state or country). Moreover, this model can be applied
for any other epidemic as well.
