Untangling the changing impact of non-pharmaceutical interventions and vaccination on European COVID-19 trajectories

Yong Ge; Wen-Bin Zhang; Xilin Wu; Corrine W Ruktanonchai; Haiyan Liu; Jianghao Wang; Yongze Song; Mengxiao Liu; Wei Yan; Juan Yang; Eimear Cleary; Sarchil H Qader; Fatumah Atuhaire; Nick W Ruktanonchai; Andrew J Tatem; Shengjie Lai

doi:10.1038/s41467-022-30897-1

Untangling the changing impact of non-pharmaceutical interventions and vaccination on European COVID-19 trajectories

Nat Commun. 2022 Jun 3;13(1):3106. doi: 10.1038/s41467-022-30897-1.

Authors

Yong Ge^#^{1

2}, Wen-Bin Zhang^#^{3

4

5}, Xilin Wu^#^{3

4}, Corrine W Ruktanonchai^#⁶, Haiyan Liu⁷, Jianghao Wang^{3

4}, Yongze Song⁸, Mengxiao Liu^{3

4}, Wei Yan^#⁹, Juan Yang^{10

11}, Eimear Cleary¹², Sarchil H Qader^{12

13}, Fatumah Atuhaire^{12

14}, Nick W Ruktanonchai⁶, Andrew J Tatem¹⁵, Shengjie Lai^{16

17

18}

Affiliations

¹ State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. gey@lreis.ac.cn.
² College of Resources and Environment, University of Academy of Sciences, Beijing, China. gey@lreis.ac.cn.
³ State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
⁴ College of Resources and Environment, University of Academy of Sciences, Beijing, China.
⁵ Lancaster Environment Center, Faculty of Science and Technology, Lancaster University, Lancaster, UK.
⁶ Population Health Sciences, Virginia Tech, Blacksburg, VA, USA.
⁷ Marine Data Center, South Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China.
⁸ School of Design and the Built Environment, Curtin University, Perth, Australia.
⁹ Respiratory Medicine Department, Peking University Third Hospital, Beijing, China.
¹⁰ School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.
¹¹ Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China.
¹² WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK.
¹³ Natural Resources Department, College of Agricultural Engineering Sciences, University of Sulaimani; Sulaimani 334, Kurdistan Region, Sulaymaniyah, Iraq.
¹⁴ School of Mathematical Sciences, University of Southampton, Southampton, UK.
¹⁵ WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK. A.J.Tatem@soton.ac.uk.
¹⁶ Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China. shengjie.lai@soton.ac.uk.
¹⁷ WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK. shengjie.lai@soton.ac.uk.
¹⁸ Institute for Life Sciences, University of Southampton, Southampton, UK. shengjie.lai@soton.ac.uk.

^# Contributed equally.

Abstract

Non-pharmaceutical interventions (NPIs) and vaccination are two fundamental approaches for mitigating the coronavirus disease 2019 (COVID-19) pandemic. However, the real-world impact of NPIs versus vaccination, or a combination of both, on COVID-19 remains uncertain. To address this, we built a Bayesian inference model to assess the changing effect of NPIs and vaccination on reducing COVID-19 transmission, based on a large-scale dataset including epidemiological parameters, virus variants, vaccines, and climate factors in Europe from August 2020 to October 2021. We found that (1) the combined effect of NPIs and vaccination resulted in a 53% (95% confidence interval: 42-62%) reduction in reproduction number by October 2021, whereas NPIs and vaccination reduced the transmission by 35% and 38%, respectively; (2) compared with vaccination, the change of NPI effect was less sensitive to emerging variants; (3) the relative effect of NPIs declined 12% from May 2021 due to a lower stringency and the introduction of vaccination strategies. Our results demonstrate that NPIs were complementary to vaccination in an effort to reduce COVID-19 transmission, and the relaxation of NPIs might depend on vaccination rates, control targets, and vaccine effectiveness concerning extant and emerging variants.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Bayes Theorem
COVID-19* / epidemiology
COVID-19* / prevention & control
Humans
Pandemics / prevention & control
SARS-CoV-2
Vaccination

Abstract

Publication types

MeSH terms

Grants and funding