The disease-induced herd immunity level for Covid-19 is substantially lower than the classical herd immunity level

Author:

Britton TomORCID,Ball FrankORCID,Trapman PieterORCID

Abstract

AbstractMost countries are suffering severely from the ongoing covid-19 pandemic despite various levels of preventive measures. A common question is if and when a country or region will reach herd immunity h. The classical herd immunity level hC is defined as hC =1−1/R0, where R0 is the basic reproduction number, for covid-19 estimated to lie somewhere in the range 2.2-3.5 depending on country and region. It is shown here that the disease-induced herd immunity level hD, after an outbreak has taken place in a country/region with a set of preventive measures put in place, is actually substantially smaller than hC. As an illustration we show that if R0 =2.5 in an age-structured community with mixing rates fitted to social activity studies, and also categorizing individuals into three categories: low active, average active and high active, and where preventive measures affect all mixing rates proportionally, then the disease-induced herd immunity level is hD = 43% rather than hC =1−1/2.5 = 60%. Consequently, a lower fraction infected is required for herd immunity to appear. The underlying reason is that when immunity is induced by disease spreading, the proportion infected in groups with high contact rates is greater than that in groups with low contact rates. Consequently, disease-induced immunity is stronger than when immunity is uniformly distributed in the community as in the classical herd immunity level.

Publisher

Cold Spring Harbor Laboratory

Reference15 articles.

1. S. Flaxman , S. Mishra , A. Gandy , H. Unwin , H. Coupland , T. Mellan , H. Zhu , T. Berah , J. Eaton , P. Perez Guzman , et al., “Report 13: Estimating the number of infections and the impact of non-pharmaceutical interventions on covid-19 in 11 european countries,” 2020.

2. Public Health Institute of Sweden, “Estimates of the peak-day and the number of infected individuals during the covid-19 outbreak in the stockholm region, sweden february—april 2020,” 2020.

3. N. Ferguson , D. Laydon , G. Nedjati Gilani, N. Imai , K. Ainslie , M. Baguelin , S. Bhatia , A. Boonyasiri , Z. Cucunuba Perez , G. Cuomo-Dannenburg , et al., “Report 9: Impact of non-pharmaceutical interventions (npis) to reduce covid19 mortality and healthcare demand,” 2020.

4. W. Bock , B. Adamik , M. Bawiec , V. Bezborodov , M. Bodych , J. P. Burgard , T. Goetz , T. Krueger , A. Migalska , B. Pabjan , et al., “Mitigation and herd immunity strategy for covid-19 is likely to fail,” medRxiv, 2020.

5. O. Diekmann , H. Heesterbeek , and T. Britton , Mathematical tools for understanding infectious disease dynamics, vol. 7. Princeton University Press, 2013.

Cited by 25 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3