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Modeling the XBB strain of SARS-CoV-2: Competition between variants and impact of reinfection.
Cheng, Ziqiang; Lai, Yinglei; Jin, Kui; Zhang, Mengping; Wang, Jin.
Afiliación
  • Cheng Z; School of Mathematics, Hefei University of Technology, Hefei, Anhui 230009, China.
  • Lai Y; School of Mathematical Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China.
  • Jin K; Department of Emergency Medicine, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China.
  • Zhang M; School of Mathematical Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China. Electronic address: mpzhang@ustc.edu.cn.
  • Wang J; Department of Mathematics, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA. Electronic address: jin-wang02@utc.edu.
J Theor Biol ; 574: 111611, 2023 Oct 07.
Article en En | MEDLINE | ID: mdl-37640233
ABSTRACT
XBB, an Omicron subvariant of SARS-CoV-2 that began to circulate in late 2022, has been dominant in the US since early 2023. To quantify the impact of XBB on the progression of COVID-19, we propose a new mathematical model which describes the interplay between XBB and other SARS-CoV-2 variants at the population level and which incorporates the effects of reinfection. We apply the model to COVID-19 data in the US that include surveillance data on the cases and variant proportions from the New York City, the State of New York, and the State of Washington. Our fitting and simulation results show that the transmission rate of XBB is significantly higher than that of other variants and the reinfection from XBB may play an important role in shaping the pandemic/epidemic pattern in the US.
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Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: J Theor Biol Año: 2023 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: J Theor Biol Año: 2023 Tipo del documento: Article