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Plausible explanation for the third COVID-19 wave in India and its implications

Recently some of us used a random-walk Monte Carlo simulation approach to study the spread of COVID-19. The calculations were reasonably successful in describing secondary and tertiary waves of infection, in countries such as the USA, India, South Africa and Serbia. However, they failed to predict t...

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Autores principales: Triambak, S., Mahapatra, D.P., Barik, N., Chutjian, A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: KeAi Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824946/
https://www.ncbi.nlm.nih.gov/pubmed/36643865
http://dx.doi.org/10.1016/j.idm.2023.01.001
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author Triambak, S.
Mahapatra, D.P.
Barik, N.
Chutjian, A.
author_facet Triambak, S.
Mahapatra, D.P.
Barik, N.
Chutjian, A.
author_sort Triambak, S.
collection PubMed
description Recently some of us used a random-walk Monte Carlo simulation approach to study the spread of COVID-19. The calculations were reasonably successful in describing secondary and tertiary waves of infection, in countries such as the USA, India, South Africa and Serbia. However, they failed to predict the observed third wave for India. In this work we present a more complete set of simulations for India, that take into consideration two aspects that were not incorporated previously. These include the stochastic movement of an erstwhile protected fraction of the population, and the reinfection of some recovered individuals because of their exposure to a new variant of the SARS-CoV-2 virus. The extended simulations now show the third COVID-19 wave for India that was missing in the earlier calculations. They also suggest an additional fourth wave, which was indeed observed during approximately the same time period as the model prediction.
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spelling pubmed-98249462023-01-09 Plausible explanation for the third COVID-19 wave in India and its implications Triambak, S. Mahapatra, D.P. Barik, N. Chutjian, A. Infect Dis Model Article Recently some of us used a random-walk Monte Carlo simulation approach to study the spread of COVID-19. The calculations were reasonably successful in describing secondary and tertiary waves of infection, in countries such as the USA, India, South Africa and Serbia. However, they failed to predict the observed third wave for India. In this work we present a more complete set of simulations for India, that take into consideration two aspects that were not incorporated previously. These include the stochastic movement of an erstwhile protected fraction of the population, and the reinfection of some recovered individuals because of their exposure to a new variant of the SARS-CoV-2 virus. The extended simulations now show the third COVID-19 wave for India that was missing in the earlier calculations. They also suggest an additional fourth wave, which was indeed observed during approximately the same time period as the model prediction. KeAi Publishing 2023-01-07 /pmc/articles/PMC9824946/ /pubmed/36643865 http://dx.doi.org/10.1016/j.idm.2023.01.001 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Triambak, S.
Mahapatra, D.P.
Barik, N.
Chutjian, A.
Plausible explanation for the third COVID-19 wave in India and its implications
title Plausible explanation for the third COVID-19 wave in India and its implications
title_full Plausible explanation for the third COVID-19 wave in India and its implications
title_fullStr Plausible explanation for the third COVID-19 wave in India and its implications
title_full_unstemmed Plausible explanation for the third COVID-19 wave in India and its implications
title_short Plausible explanation for the third COVID-19 wave in India and its implications
title_sort plausible explanation for the third covid-19 wave in india and its implications
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824946/
https://www.ncbi.nlm.nih.gov/pubmed/36643865
http://dx.doi.org/10.1016/j.idm.2023.01.001
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