Analysing the Effects of Lockdown & Vaccination to Inhibit the Spread of COVID-19 in India with a Networked SUVIRD Model

In this paper, a novel deterministic discrete-time networked SUVIRD (Susceptible-Undetected positive-Vaccinated-Infected-Recovered-Deceased) model has been pro-posed to capture the spreading dynamics of COVID-19 in fifteen most affected states of India. Using the proposed model, inves-tigation has been carried out to analyse the role of lockdown as well as vaccination to minimize the spread of the disease during the second wave in India. In this connection, to capture the stringency of lockdown a time varying intervention term
$(\eta[t])$
has been introduced and a distance-based adjacency matrix has been constructed to demonstrate the interconnection between states. Basic reproduction number
$(\mathcal{R}_{0})$
has been computed for each state using the estimated rate parameters. Eventually, an analogy has been drawn between the original cumulative caseload and the simulated cumulative infection using estimated parameters.