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Research Article | Open Access

Modeling variants of the COVID-19 virus in Hawai‘i and the responses to forecasting

Richard Carney1Monique Chyba1( )Victoria Y. Fan2Prateek Kunwar1Thomas Lee2,3Ionica Macadangdang1Yuriy Mileyko1
Department of Mathematics, University of Hawai‘i at Mānoa, 2565 McCarthy Mall Honolulu, Hawai‘i 96822
Thompson School of Social Work & Public Health, University of Hawai‘i at Mānoa, Honolulu, HI 96822, USA
Hawaii Data Collaborative, Honolulu, HI 96813, USA
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Abstract

In this paper we introduce a model for the spread of COVID-19 which takes into account competing SARS-CoV-2 mutations as well as the possibility of reinfection due to fading of vaccine protection. Our primary focus is to describe the impact of the B.1.617.2 (Delta) and B.1.1.529 (Omicron) variants on the state of Hawai‘i and to illustrate how the model performed during the pandemic, both in terms of accuracy, and as a resource for the government and media. Studying the effect of the pandemic on the Hawaiian archipelago is of notable interest because, as an isolated environment, its unique geography affords partially controlled travel to and from the state. We highlight the modeling efforts of the Hawai‘i Pandemic Applied Modeling Work Group (HiPAM) which used the model presented here, and we detail the model fitting and forecasting for the periods from July 2021 to October 2021 (Delta surge) and from November 2021 to April 2022 (Omicron surge). Our results illustrate that the model was both accurate when the forecasts were built on assumptions that held true, and was inaccurate when the public response to the forecasts was to enforce safety measures that invalidated the assumptions in the model.

CLC number: 39Axx, 92D25, 92D30, 93-10

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AIMS Mathematics
Pages 4487-4523

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Cite this article:
Carney R, Chyba M, Fan VY, et al. Modeling variants of the COVID-19 virus in Hawai‘i and the responses to forecasting. AIMS Mathematics, 2023, 8(2): 4487-4523. https://doi.org/10.3934/math.2023223

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Received: 09 September 2022
Revised: 11 November 2022
Accepted: 17 November 2022
Published: 15 February 2023
©2023 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)