SEI3R2D2V: Pandemic Modeling and Analysis of Its Latent Factors: A Case Study of COVID-19 in India

ABSTRACT

For densely populated developing countries, such as India, where due to a lack of general and public awareness, limited data collection and compilation facilities, and inherent limitations of the available diagnostic test, accurate modeling of the pandemic is more challenging. Thus, a realistic model for predictions is required in order to formulate more effective strategic policies to control the COVID-19 pandemic using limited available resources. In this article, we propose a time-varying epidemiological model with two classes of compartments, reported and unreported, and consider influential latent factors, for example, undetectable infections, the false-negative rate of testing, testing hesitancy, vaccination efficacy, dual contact dynamics, and the possibility of reinfection in recovered as well as vaccinated individuals. For simulation purposes, we consider the COVID-19 data of India from March 13, 2020, to January 20, 2022. Furthermore, we provide a sensitivity analysis of various latent factors and predictions for the third wave in India. Simulated results suggest that India is able to control COVID-19 for the first time after the second wave, as observed from the trajectory of effective reproduction number. Moreover, for unseen or coming variants of virus for which vaccine efficacy is low, the available vaccine requires a high vaccination rate to control future waves.