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 positiveVaccinated-Infected-Recovered-Deceased) model has been proposed to capture the spreading dynamics of COVID-19 in fifteen most affected states of India. Using the proposed model, investigation 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 (.[t]) has been introduced and a distance-based adjacency matrix has been constructed to demonstrate the interconnection between states. Basic reproduction number (R0) 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.

Prediction of COVID-19 Infected Population for Indian States through a State Interaction Network-based SEIR Epidemic Model

Objective of this present study is to predict the COVID-19 trajectories in terms of infected population of Indian states. In this work, a state interaction network of sixteen Indian states with highest number of infected caseload is considered, based on networked SusceptibleExposed-Infected-Recovered (SEIR) epidemic model. An intervention term has been introduced in order to capture the effect of lockdown with different stringencies at different periods of time. The model has been fitted using least absolute shrinkage and selection operator (LASSO). Machine learning methods have been used to train the parameters of the model, cross-validate the data, and predict the parameters. The predictions of infected population for each of the sixteen states have been shown using data considered from January 1, 2021 till writing this manuscript on June 25, 2021. Finally, the effectiveness of the model is manifested by the calculated mean error and confidence interval. Copyright (C) 2022 The Authors.

Analysis of Imposition and Periodic Relaxation of Lockdown on the Spread of COVID-19 in India through Networked SEIR Model

In this paper, an investigation is carried out to analyse how periodic lockdown and unlocking have helped India to combat the first wave of COVID-19. To that end, a networked SEIR model is considered that captures the spreading dynamics of the disease in sixteen of the worst affected states of India. In this regard, a distance based contact matrix is constructed to reflect the connectivity between states. Various rate parameters of the model are estimated as well as the basic reproduction number (\mathscr{R}_{0}) of each of the sixteen states for each phase of lockdown is found out. Finally, a comparison is drawn between the simulated results of cumulative infected caseload using the estimated parameters and that with the real COVID-19 data of India till December 31, 2020, which establishes the effectiveness of the method. © 2021 IEEE.