The COVID-19 pandemic demonstrated that the process of global vaccination against a novel virus can be a prolonged one. Social distancing measures, that are initially adopted to control the pandemic, are gradually relaxed as vaccination progresses and population immunity increases. The result is a prolonged period of high disease prevalence combined with a fitness advantage for vaccine-resistant variants, which together lead to a considerably increased probability for vaccine escape. A spatial vaccination strategy is proposed that has the potential to dramatically reduce this risk. Rather than dispersing the vaccination effort evenly throughout a country, distinct geographic regions of the country are sequentially vaccinated, quickly bringing each to effective herd immunity. Regions with high vaccination rates will then have low infection rates and vice versa. Since people primarily interact within their own region, spatial vaccination reduces the number of encounters between infected individuals (the source of mutations) and vaccinated individuals (who facilitate the spread of vaccine-resistant strains). Thus, spatial vaccination may help mitigate the global risk of vaccine-resistant variants.
|Journal||PLoS Computational Biology|
|State||Published - Aug 2022|
Bibliographical noteFunding Information:
X.Z. was supported by the NNSF of China under Grant No. 12105117, the Fundamental Research Funds for the Central Universities (Grant No. 21621007) and Guangdong Basic and Applied Basic Research Foundation (Grant No. 2022A1515010523). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank Jonathan Gershoni and Ron Milo for helpful comments and discussions.
Funding:X.Z.wassupportedbytheNNSFofChina underGrantNo.12105117,theFundamental ResearchFundsfortheCentralUniversities(Grant No.21621007)andGuangdongBasicandApplied BasicResearchFoundation(GrantNo. 2022A1515010523).Thefundershadnorolein
© 2022 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.