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This work presents a simple and realistic approach to handle the availabledata of COVID-19 patients in India and to forecast the scenario. The modelproposed is based on the available facts like the onset of lockdown (asannounced by the Government on 25th day, {\tau}0 and the recovery patterndictated by a mean life recovery time of {\tau}1 ( normally said to be around14 days). The data of infected COVID-19 patients from March 2, to April 16,2020 has been used to fit the evolution of infected, recovery and death counts.A slow rising exponential growth, with R0 close to 1/6, is found to representthe infected counts indicating almost a linear rise. The rest of growth,saturation and decay of data is comprehensibly modelled by incorporatinglockdown time controlled R0, having a normal error function like behaviourdecaying to zero in some time frame of {\tau}2 . The recovery mean life time{\tau}1 dictates the peak and decay. The results predicted for coming days areinteresting and optimistic. The introduced time constants based on experimentaldata for both the recovery rate as well as for determining the time span ofactivity of R0 after the lockdown are subject of debate and provide possibilityto introduce trigger factors to alter these to be more suited to the model. Themodel can be extended to other communities with their own R0 and recovery timeparameters.

COVID 19, a realistic model for saturation, growth and decay of the India specific disease