Predicting and Modelling the Spread of COVID-19 Variants in the USA: A Time-Varying SEIRVHD Approach

This study presents a comprehensive analysis and prediction of the COVID-19 variants within the USA using a time-dependent advanced SEIR model. It features dynamic and practical parameter rates including the transmission, development, recovery, mortality, hospitalization, vaccination, and waning immunity rates. The model is applied to five primary COVID-19 variants: Alpha, Beta, Gamma, Delta, and Omicron. These parameters are based on discovered epidemic behaviour and values rooted in real-world occurrences. Experimental simulations are evaluated in opposition to current differential equation (DE) models to assess prediction accuracy and model robustness. The consequences highlight significant factors driving the course of the pandemic and provide guidance for mitigating future outbreaks. The model's predictions emphasize the importance of well-timed vaccination and other non-pharmaceutical interventions in controlling the COVID-19 variants.

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Publication Details

Published
2024-11-22
DOI
https://doi.org/10.1109/icaccm61117.2024.11059192
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
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article

Predicting and Modelling the Spread of COVID-19 Variants in the USA: A Time-Varying SEIRVHD Approach

Anju Sood, Amarpreet Kaur Sabherwal
COVID-19 epidemiological studies
article

Predicting and Modelling the Spread of COVID-19 Variants in the USA: A Time-Varying SEIRVHD Approach

Anju Sood, Amarpreet Kaur Sabherwal
article en

Abstract

This study presents a comprehensive analysis and prediction of the COVID-19 variants within the USA using a time-dependent advanced SEIR model. It features dynamic and practical parameter rates including the transmission, development, recovery, mortality, hospitalization, vaccination, and waning immunity rates. The model is applied to five primary COVID-19 variants: Alpha, Beta, Gamma, Delta, and Omicron. These parameters are based on discovered epidemic behaviour and values rooted in real-world occurrences. Experimental simulations are evaluated in opposition to current differential equation (DE) models to assess prediction accuracy and model robustness. The consequences highlight significant factors driving the course of the pandemic and provide guidance for mitigating future outbreaks. The model's predictions emphasize the importance of well-timed vaccination and other non-pharmaceutical interventions in controlling the COVID-19 variants.

Openalex Percentile: Top 71%
COVID-19 epidemiological studies
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