Dynamics of the COVID-19 pandemic in India

TL;DR

This study models COVID-19 spread in India using an SEIR framework, incorporating contact tracing and hospitalization data, revealing potential oscillatory and seasonal patterns, and analyzing media influence on pandemic dynamics.

Contribution

Introduces a refined SEIR model with Indian data, performs sensitivity analysis, and explores the impact of media on COVID-19 dynamics in India.

Findings

Increasing short-term COVID-19 cases in India and provinces

Potential oscillatory and seasonal behavior of the pandemic

Media influence affects pandemic dynamics

Abstract

Understanding the dynamics of the COVID-19 pandemic is crucial for improved control and social distancing strategies. To that effect, we have employed the susceptible-exposed-infectious-recovered model, refined by contact tracing and hospitalization data from Indian provinces Kerala, Delhi, Maharashtra, and West Bengal, as well as from overall India. We have performed a sensitivity analysis to identify the most crucial input parameters, and we have calibrated the model to describe the data as best as possible. Short-term predictions reveal an increasing and worrying trend of COVID-19 cases for all four provinces and India as a whole, while long-term predictions also reveal the possibility of oscillatory dynamics. Our research thus leaves the option open that COVID-19 might become a seasonal occurrence. We also simulate and discuss the impact of media on the dynamics of the COVID-19 pandemic.