The COVID-19 herd immunity threshold is not low: A re-analysis of European data from spring of 2020
Fox, S. J.; Potu, P.; Srinivasan, R.; Lachmann, M.; Meyers, L. A.
Show abstract
The recent publication of the Great Barrington Declaration (GBD), which calls for relaxing all public health interventions on young, healthy individuals, has brought the question of herd immunity to the forefront of COVID-19 policy discussions, and is partially based on unpublished research that suggests low herd immunity thresholds (HITs) of 10-20%. We re-evaluate these findings and correct a flawed assumption leading to COVID-19 HIT estimates of 60-80%. If policymakers were to adopt a herd immunity strategy, in which the virus is allowed to spread relatively unimpeded, we project that cumulative COVID-19 deaths would be five times higher than the initial estimates suggest. Our re-estimates of the COVID-19 HIT corroborate strong signals in the data and compelling arguments that most of the globe remains far from herd immunity, and suggest that abandoning community mitigation efforts would jeopardize the welfare of communities and integrity of healthcare systems.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Behaviour, booster vaccines and waning immunity: modelling the medium-term dynamics of SARS-CoV-2 transmission in England in the Omicron era 95%
- Impact of unequal testing on vaccine effectiveness estimates across two study designs: a simulation study 95%
- Estimating the potential impact and diagnostic requirements for SARS-CoV-2 test-and-treat programs 94%
Similar papers in this journal
- Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2 94%
- SARS-CoV-2 transmission dynamics in South Africa and epidemiological characteristics of the Omicron variant 94%
- Mechanistic theory predicts the effects of temperature and humidity on inactivation of SARS-CoV-2 and other enveloped viruses 93%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.