the potential for vaccination induced herd immunity against the sars cov 2 b 1 1 7 CORD-Papers-2022-06-02 (Version 1)

Title: The potential for vaccination-induced herd immunity against the SARS-CoV-2 B.1.1.7 variant
Abstract: We assess the feasibility of reaching the herd immunity threshold against SARS-CoV-2 through vaccination considering vaccine effectiveness (VE) transmissibility of the virus and the level of pre-existing immunity in populations as well as their age structure. If highly transmissible variants of concern become dominant in areas with low levels of naturally-acquired immunity and/or in populations with large proportions of < 15 year-olds control of infection without non-pharmaceutical interventions may only be possible with a VE 80% and coverage extended to children.
Published: 2021-05-20
Journal: Euro Surveill
DOI: 10.2807/1560-7917.es.2021.26.20.2100428
DOI_URL: http://doi.org/10.2807/1560-7917.es.2021.26.20.2100428
Author Name: Hodgson David
Author link: https://covid19-data.nist.gov/pid/rest/local/author/hodgson_david
Author Name: Flasche Stefan
Author link: https://covid19-data.nist.gov/pid/rest/local/author/flasche_stefan
Author Name: Jit Mark
Author link: https://covid19-data.nist.gov/pid/rest/local/author/jit_mark
Author Name: Kucharski Adam J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/kucharski_adam_j
Author Name: Abbott Sam
Author link: https://covid19-data.nist.gov/pid/rest/local/author/abbott_sam
Author Name: Edmunds W John
Author link: https://covid19-data.nist.gov/pid/rest/local/author/edmunds_w_john
Author Name: Davies Nicholas G
Author link: https://covid19-data.nist.gov/pid/rest/local/author/davies_nicholas_g
Author Name: Eggo Rosalind M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/eggo_rosalind_m
Author Name: Medley Graham
Author link: https://covid19-data.nist.gov/pid/rest/local/author/medley_graham
Author Name: Lei Jiayao
Author link: https://covid19-data.nist.gov/pid/rest/local/author/lei_jiayao
Author Name: Liu Yang
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_yang
Author Name: Tully Damien C
Author link: https://covid19-data.nist.gov/pid/rest/local/author/tully_damien_c
Author Name: McCarthy Ciara V
Author link: https://covid19-data.nist.gov/pid/rest/local/author/mccarthy_ciara_v
Author Name: Mee Paul
Author link: https://covid19-data.nist.gov/pid/rest/local/author/mee_paul
Author Name: Endo Akira
Author link: https://covid19-data.nist.gov/pid/rest/local/author/endo_akira
Author Name: Hellewell Joel
Author link: https://covid19-data.nist.gov/pid/rest/local/author/hellewell_joel
Author Name: Funk Sebastian
Author link: https://covid19-data.nist.gov/pid/rest/local/author/funk_sebastian
Author Name: Jombart Thibaut
Author link: https://covid19-data.nist.gov/pid/rest/local/author/jombart_thibaut
Author Name: Jafari Yalda
Author link: https://covid19-data.nist.gov/pid/rest/local/author/jafari_yalda
Author Name: Brady Oliver
Author link: https://covid19-data.nist.gov/pid/rest/local/author/brady_oliver
Author Name: Prem Kiesha
Author link: https://covid19-data.nist.gov/pid/rest/local/author/prem_kiesha
Author Name: Krauer Fabienne
Author link: https://covid19-data.nist.gov/pid/rest/local/author/krauer_fabienne
Author Name: Koltai Mihaly
Author link: https://covid19-data.nist.gov/pid/rest/local/author/koltai_mihaly
Author Name: Waterlow Naomi R
Author link: https://covid19-data.nist.gov/pid/rest/local/author/waterlow_naomi_r
Author Name: Russell Timothy W
Author link: https://covid19-data.nist.gov/pid/rest/local/author/russell_timothy_w
Author Name: Meakin Sophie R
Author link: https://covid19-data.nist.gov/pid/rest/local/author/meakin_sophie_r
Author Name: O aposReilly Kathleen
Author link: https://covid19-data.nist.gov/pid/rest/local/author/o_aposreilly_kathleen
Author Name: Bosse Nikos I
Author link: https://covid19-data.nist.gov/pid/rest/local/author/bosse_nikos_i
Author Name: Waites William
Author link: https://covid19-data.nist.gov/pid/rest/local/author/waites_william
Author Name: Nightingale Emily S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/nightingale_emily_s
Author Name: Lowe Rachel
Author link: https://covid19-data.nist.gov/pid/rest/local/author/lowe_rachel
Author Name: Chan Yung Wai Desmond
Author link: https://covid19-data.nist.gov/pid/rest/local/author/chan_yung_wai_desmond
Author Name: Atkins Katherine E
Author link: https://covid19-data.nist.gov/pid/rest/local/author/atkins_katherine_e
Author Name: Quilty Billy J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/quilty_billy_j
Author Name: Sandmann Frank G
Author link: https://covid19-data.nist.gov/pid/rest/local/author/sandmann_frank_g
Author Name: van Zandvoort Kevin
Author link: https://covid19-data.nist.gov/pid/rest/local/author/van_zandvoort_kevin
Author Name: Villabona Arenas C Julian
Author link: https://covid19-data.nist.gov/pid/rest/local/author/villabona_arenas_c_julian
Author Name: Gibbs Hamish P
Author link: https://covid19-data.nist.gov/pid/rest/local/author/gibbs_hamish_p
Author Name: Munday James D
Author link: https://covid19-data.nist.gov/pid/rest/local/author/munday_james_d
Author Name: Foss Anna M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/foss_anna_m
Author Name: Gimma Amy
Author link: https://covid19-data.nist.gov/pid/rest/local/author/gimma_amy
Author Name: Pearson Carl A B
Author link: https://covid19-data.nist.gov/pid/rest/local/author/pearson_carl_a_b
Author Name: Barnard Rosanna C
Author link: https://covid19-data.nist.gov/pid/rest/local/author/barnard_rosanna_c
Author Name: Quaife Matthew
Author link: https://covid19-data.nist.gov/pid/rest/local/author/quaife_matthew
Author Name: Sun Fiona Yueqian
Author link: https://covid19-data.nist.gov/pid/rest/local/author/sun_fiona_yueqian
Author Name: Rosello Alicia
Author link: https://covid19-data.nist.gov/pid/rest/local/author/rosello_alicia
Author Name: Pung Rachael
Author link: https://covid19-data.nist.gov/pid/rest/local/author/pung_rachael
Author Name: Jarvis Christopher I
Author link: https://covid19-data.nist.gov/pid/rest/local/author/jarvis_christopher_i
Author Name: Finch Emilie
Author link: https://covid19-data.nist.gov/pid/rest/local/author/finch_emilie
Author Name: Abbas Kaja
Author link: https://covid19-data.nist.gov/pid/rest/local/author/abbas_kaja
Author Name: Clifford Samuel
Author link: https://covid19-data.nist.gov/pid/rest/local/author/clifford_samuel
Author Name: Knight Gwenan M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/knight_gwenan_m
Author Name: Procter Simon R
Author link: https://covid19-data.nist.gov/pid/rest/local/author/procter_simon_r
sha: c75f8c8f0e9ae64ab66e25462196b450fd9d7568
license: cc-by
license_url: https://creativecommons.org/licenses/by/4.0/
source_x: Medline; PMC; WHO
source_x_url: https://www.medline.com/https://www.ncbi.nlm.nih.gov/pubmed/https://www.who.int/
pubmed_id: 34018481
pubmed_id_url: https://www.ncbi.nlm.nih.gov/pubmed/34018481
pmcid: PMC8138959
pmcid_url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8138959
url: https://www.ncbi.nlm.nih.gov/pubmed/34018481/ https://doi.org/10.2807/1560-7917.es.2021.26.20.2100428
has_full_text: TRUE
Keywords Extracted from Text Content: V e SARS-24 CoV-2 2 post-2009 herd 1-1/R mumps 1 varicella 1 A/H1N1 SeroTracker A/H3N2 BNT162b2 4 rubella 1 SARS-CoV-2 B 3 UK P(transmit | ChAdOx1 nCoV-19 vaccination 7 measles 1 nCoV-19 matrix P(transmit | 33
Extracted Text Content in Record: First 5000 Characters:Formula for required vaccine coverage 10 11 We defined the required coverage for herd immunity, C, as follows: where R 0 is for SARS-CoV-2, p is the proportional reduction in transmission (due to previous 16 infection), and V e is the vaccine effectiveness. If R 0 is calculated using an age-structured 17 next generation matrix and vaccination scales susceptibility across this matrix, the herd 18 immunity threshold will still be 1-1/R 0 . 19 20 Source of vaccine effectiveness estimates 21 We obtained published estimates for the average and 95% upper and lower confidence 23 intervals for vaccine effectiveness against measles 1 , mumps 1 , rubella 1 , varicella 1 , SARS-24 CoV-2 2 , influenza A/H1N1 (post-2009), A/H3N2, and B 3 . For SARS-CoV-2, we used data 25 from a study estimating vaccine effectiveness in reducing infection among antibody negative 26 healthcare workers who received two doses of BNT162b2 4 (effectiveness was estimated at 27 86% (95% CI: 76-97%). This compares with an estimate of 83% (76-87%) lower risk of 28 reinfection among healthcare workers following prior infection 5 . A recent analysis of UK 29 community infection data 6 also estimated a 64% (95% CI: 55-70%) reduction in risk of 30 infection following one dose of ChAdOx1 nCoV-19, and a 45% (33-54%) reduction in risk of 31 transmission if infected after ChAdOx1 nCoV-19 vaccination 7 , which would imply a potential 32 reduction in transmission of around 70-85% post-vaccination, because P(transmit | 33 exposed) = P(transmit | infected) x P(infected | exposed). In order to reflect uncertainty in Seroprevalence studies were obtained from SeroTracker, a dashboard that synthesises 55 findings from hundreds of global SARS-CoV-2 serological studies 15 . We aimed to estimate 56 the seroprevalence in the general population for each study region. Therefore, in an attempt 57 to reduce selection bias, we only considered prospective households/community studies. 58 Each seroprevalence study provides information on study site country, sample size, 59 geographical scope (national, region, local), and the time frame in which samples were 60 collected. If multiple studies exist for a country within a geographical scope, we consider only 61 the most recent estimate.
Keywords Extracted from PMC Text: matrix 1.5–3.8 Comirnaty Comirnaty vaccine [1] – COVID-19 vaccines Brazil [14 human coronaviruses persons ≥ rubella viruses [4 B.1.1.7 people SARS-CoV-2 vaccines — children vaccinees 76–97 's herd Germany/New pre-B.1.1.7 persons vaccine-rollout − pre-B.1.1.7 SARS-CoV-2 COVID-19 ≥ SARS-CoV-2 measles BioNTech/Pfizer BNT162b2
Extracted PMC Text Content in Record: First 5000 Characters:The feasibility of attaining vaccination-induced herd immunity depends on (i) vaccine effectiveness in reducing transmission, (ii) the transmissibility of the target pathogen and (iii) the vaccine coverage that is achievable in a population. In a scenario where vaccines are distributed randomly across a population, the herd immunity threshold (HIT) for an immunisation programme is defined as 1 − 1/R0, where R0 is the basic reproduction number [3]. Note that if R0 is calculated using an age- or risk-structured next generation matrix then this equation will still hold. If vaccine effectiveness is below the HIT, then even vaccination of the entire population would, on its own, be insufficient to ensure control (i.e. the effective reproduction number, accounting for immunity, would remain above 1). Comparing this theoretical HIT with estimated values of R0 and vaccine effectiveness for a range of vaccine-preventable diseases (Figure 1), we see that for infections caused by viruses with little antigenic variation, vaccine effectiveness is sufficiently high to control transmission if high vaccine coverage is achieved. This is why, in many countries, childhood immunisation programmes have led to elimination of viruses with little antigenic variation and long-lasting sterilizing immunity, such as measles and rubella viruses [4]. In contrast, viruses that undergo frequent antigenic turnover, such as influenza viruses necessitate regular vaccine updates and re-vaccination [5]. Seasonal influenza vaccine effectiveness is influenced by antigenic evolution of influenza viruses, with similar rates of evolution to that observed for seasonal human coronaviruses. Moreover, the effectiveness depends on whether the influenza vaccine strains are or not the same as the circulating ones. In the event of well-matched influenza vaccines, the effectiveness may nevertheless still be below the HIT. For influenza A(H3N2) virus, for example, the estimated effectiveness of an antigenically-matched vaccine (33%; 95% confidence interval (CI): 22–43) [6] implies that control of this subtype in the absence of natural immunity is unlikely, even in theory; we estimate a very small probability (defined as number of Monte Carlo samples) in an unexposed population with 100% vaccination coverage of being above the HIT. For SARS-CoV-2, we consider two SARS-CoV-2 variants for which we assume vaccination provides equal protection: pre-B.1.1.7 variants with an R0 of 2.7 (95% CI: 1.5–3.8) and the B.1.1.7 variant with an R0 of 4.5 (95% CI: 2.5–6.4) [7]. Assuming 86% (95% CI: 76–97) vaccine effectiveness against infectiousness, based on early estimates of protection against infection following two doses of Comirnaty (BNT162b2, BioNTech/Pfizer, Mainz, Germany/New York, United States) [1], we estimate, in the case of a pre-B.1.1.7 variant, a 99% probability of being above the HIT with whole-population coverage and a 94% probability if B.1.1.7 is circulating exclusively. Ethical approval was not necessary for this modelling study as the analysis uses only aggregated secondary data from published articles. The estimated 94% and 99% probabilities to be above the HIT, for B.1.1.7 and pre-B.1.1.7 SARS-CoV-2 respectively, are based on the assumption that the whole population is vaccinated. However, whole-population vaccination would require SARS-CoV-2 vaccines — which, as at mid-May 2021, are only approved for adults in most countries — to also be used at high coverage in children, for whom there is currently limited evidence from trials only, on safety or effectiveness. Assuming a vaccination campaign aimed at all individuals ≥ 15 years old, the proportion of the population currently eligible for vaccination (i.e. comprising people aged ≥ 15 years) varies between countries, with the proportion of children within a country's population decreasing along increasing income brackets (low, lower-middle, upper-middle, upper income). Given this trend, we use income level as a proxy for the total proportion of the population aged 15 years and over, which is eligible for vaccination. We estimated that for pre-B.1.1.7 SARS-CoV-2 variants, an immunisation programme targeting all persons aged ≥ 15 years (as would be the case for a vaccine not approved for use in younger groups), would have generated herd immunity against homotypic viruses in most higher income countries, regardless of the level of natural immunity, if vaccine effectiveness is at least 70% (or at least 90%, with a high degree of confidence). However, the high proportion of children in many lower income countries means the HIT cannot be reached with a ≥ 15-years-old vaccination programme alone and would require higher levels of immunity among children, acquired either through vaccination or infection, to be reached (Figure 2A). For B.1.1.7, or similarly transmissible variants, we would expect ongoing transmission until a sufficient level of natural immunity has been accrued, even in countries with an older a
PDF JSON Files: document_parses/pdf_json/c75f8c8f0e9ae64ab66e25462196b450fd9d7568.json
PMC JSON Files: document_parses/pmc_json/PMC8138959.xml.json
G_ID: the_potential_for_vaccination_induced_herd_immunity_against_the_sars_cov_2_b_1_1_7