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Modeling and Predicting the COVID-19 Trajectory in India
7th IEEE International conference for Convergence in Technology, I2CT 2022 ; 2022.
Article in English | Scopus | ID: covidwho-1992610
ABSTRACT
The SARS-CoV-2 has a confirmed case count of about 11.3 million and a death count of about 158,000 in India as of March 13th, 2021. Despite the early social distancing and lockdown measures imposed by the government, these counts have continued to rise. Mathematical models prove extremely useful to predict the course of the pandemic and for the government to strategize accordingly. Over due course several models have emerged to predict the number of COVID-19 cases, but a thorough comparison among them is lacking. In this paper, we propose three novel Hybrid Models based on the compartment-based modeling over data from January 22nd, 2020 to December 3rd 2020 and then make comparisons among them and show through experiments that each performs a better fitting and prediction on the Johns Hopkins COVID-19 dataset pertaining to India than all other benchmark models discussed. Comparison of our proposed Hybrid models with the existing compartment models like SIR, SIRD and SEIRD demonstrates that our proposed Hybrid models not only overcome the performance inefficiencies related to the existing compartmental models but also achieve a better fitting on the Johns Hopkins COVID-19 dataset. © 2022 IEEE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study Language: English Journal: 7th IEEE International conference for Convergence in Technology, I2CT 2022 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study Language: English Journal: 7th IEEE International conference for Convergence in Technology, I2CT 2022 Year: 2022 Document Type: Article