  Undetected infectious populations have played a major role in the COVID-19
outbreak across the globe and estimation of this undetected class is a major
concern in understanding the actual size of the COVID-19 infections. Due to the
asymptomatic nature of some infections, many cases have gone undetected. Also,
despite carrying COVID-19 symptoms, most of the infected population kept the
infections hidden and stayed unreported, especially in a country like India.
Based on these factors, we have added an undetected compartment to the already
developed SEIR model [48] to estimate these uncounted infections. In this
article, we have applied Physics Informed Neural Network (PINN) to estimate the
undetected infectious populations in the 20 worst-affected Indian states as
well as India as a whole. The analysis has been carried out for the first as
well as second surge of COVID-19 infections in India. A ratio of the active
undetected infectious to the active detected infectious population is
calculated through the PINN analysis which gives a picture of the real size of
the pandemic in India. The rate at which symptomatic infectious population goes
undetected and are never reported is also estimated using the PINN method.
Toward the end, an artificial neural network (ANN) based forecasting scenario
of the pandemic in India is presented. The prediction is found to be reliable
as the training of the neural network has been carried out using the unique
features, obtained from the state-wide analysis of the newly proposed model as
well as from the PINN analysis.
