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Predicting elimination of evolving virus variants.
Hughes, Elliott; Binny, Rachelle; Hendy, Shaun; James, Alex.
  • Hughes E; School of Mathematics and Statistics, University of Canterbury, Christchurch 8140, New Zealand.
  • Binny R; Te Punaha Matatini: the Centre for Complex Systems and Networks, Auckland 1010, New Zealand.
  • Hendy S; Manaaki Whenua, Lincoln 7640, New Zealand.
  • James A; Te Punaha Matatini: the Centre for Complex Systems and Networks, Auckland 1010, New Zealand.
Math Med Biol ; 39(4): 410-424, 2022 Dec 02.
Article in English | MEDLINE | ID: covidwho-1992197
ABSTRACT
As the SARS-CoV-2 virus spreads around the world new variants are appearing regularly. Although some countries have achieved very swift and successful vaccination campaigns, on a global scale the vast majority of the population is unvaccinated and new variants are proving more resistant to the current set of vaccines. We present a simple model of disease spread, which includes the evolution of new variants of a novel virus and varying vaccine effectiveness to these new strains. We show that rapid vaccine updates to target new strains are more effective than slow updates and containing spread through non-pharmaceutical interventions is vital while these vaccines are delivered. Finally, when measuring the key model inputs, e.g. the rate at which new mutations and variants of concern emerge, is difficult we show how an observable model output, the number of new variants that have been seen, is strongly correlated with the probability the virus is eliminated.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Prognostic study Topics: Vaccines / Variants Limits: Humans Language: English Journal: Math Med Biol Journal subject: Biology / Medicine Year: 2022 Document Type: Article Affiliation country: Imammb

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Prognostic study Topics: Vaccines / Variants Limits: Humans Language: English Journal: Math Med Biol Journal subject: Biology / Medicine Year: 2022 Document Type: Article Affiliation country: Imammb