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Quantifying competitive advantages of mutant strains in a population involving importation and mass vaccination rollout

Quantifying competitive advantages of mutant strains in a population involving importation and mass vaccination rollout

Xia, Fan, Yang, Xinpei, Cheke, Robert A. ORCID: 0000-0002-7437-1934 and Xiao, Yanni (2021) Quantifying competitive advantages of mutant strains in a population involving importation and mass vaccination rollout. Infectious Disease Modelling, 6. pp. 988-996. ISSN 2468-0427 (doi:https://doi.org/10.1016/j.idm.2021.08.001)

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Abstract

The SARS-CoV-2 Delta variant, known as B.1.617.2 and first identified in India, is becoming a dominant strain in many countries due to its extreme infectiousness. Assessing and quantifying the transmissibility and competitive advantage of the Delta variant is of major significance for countries around the world so that appropriate measures to mitigate and even eliminate the epidemic can be introduced. Aiming at such problems, we proposed a method to model the invasion process of a novel strain and estimate the competitive advantage of the invading strain over the local strain. We applied this method to study the invasion and spread of the Delta variant into England. We have estimated the basic reproduction number of the Delta variant as being 49% higher than that of the Alpha variant (CI: 45–52%), assuming a mean generation interval of 5 days with a standard deviation of 3 days. In the period 11 April to 17 May 2021, the effective reproduction number of the Delta variant was 65% higher than that of the Alpha variant in England (CI: 61–70%). Our results show that the Delta variant has a significantly higher transmission capacity than other strains, which explains the rebound of the epidemic in many countries, even in those with relatively high vaccination coverages.

Item Type: Article
Uncontrolled Keywords: COVID-19, SARS-CoV-2 Delta variant, competitive advantage, reproduction number
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QR Microbiology > QR180 Immunology
Q Science > QR Microbiology > QR355 Virology
Faculty / School / Research Centre / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Natural Resources Institute
Faculty of Engineering & Science > Natural Resources Institute > Agriculture, Health & Environment Department
Last Modified: 22 Sep 2021 15:12
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/33591

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