Quantifying the effect of government interventions and virus mutations on transmission advantage during COVID-19 pandemic

Jingzhi Lou; Hong Zheng; Shi Zhao; Lirong Cao; Eliza Ly Wong; Zigui Chen; Renee Wy Chan; Marc Kc Chong; Benny Cy Zee; Paul Ks Chan; Eng-Kiong Yeoh; Maggie H Wang

doi:10.1016/j.jiph.2022.01.020

Quantifying the effect of government interventions and virus mutations on transmission advantage during COVID-19 pandemic

J Infect Public Health. 2022 Mar;15(3):338-342. doi: 10.1016/j.jiph.2022.01.020. Epub 2022 Feb 4.

Authors

Jingzhi Lou¹, Hong Zheng², Shi Zhao³, Lirong Cao⁴, Eliza Ly Wong⁵, Zigui Chen⁶, Renee Wy Chan⁷, Marc Kc Chong⁸, Benny Cy Zee⁹, Paul Ks Chan¹⁰, Eng-Kiong Yeoh¹¹, Maggie H Wang¹²

Affiliations

¹ JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Hong Kong, China. Electronic address: jzlou@qq.com.
² JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Hong Kong, China. Electronic address: hongzheng@cuhk.edu.hk.
³ JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Hong Kong, China; CUHK Shenzhen Research Institute, Shenzhen, China. Electronic address: zhaoshi.cmsa@gmail.com.
⁴ JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Hong Kong, China. Electronic address: caolr@link.cuhk.edu.hk.
⁵ JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Hong Kong, China. Electronic address: lywong@cuhk.edu.hk.
⁶ Department of Microbiology, the Chinese University of Hong Kong, Hong Kong, China. Electronic address: zigui.chen@cuhk.edu.hk.
⁷ Department of Paediatric, the Chinese University of Hong Kong, Hong Kong, China. Electronic address: reneewy@cuhk.edu.hk.
⁸ JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Hong Kong, China; CUHK Shenzhen Research Institute, Shenzhen, China. Electronic address: marc@cuhk.edu.hk.
⁹ JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Hong Kong, China; CUHK Shenzhen Research Institute, Shenzhen, China. Electronic address: bzee@cuhk.edu.hk.
¹⁰ Department of Microbiology, the Chinese University of Hong Kong, Hong Kong, China. Electronic address: paulkschan@cuhk.edu.hk.
¹¹ JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Hong Kong, China. Electronic address: yeoh_ek@cuhk.edu.hk.
¹² JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Hong Kong, China; CUHK Shenzhen Research Institute, Shenzhen, China. Electronic address: maggiew@cuhk.edu.hk.

Abstract

Background: The coronavirus disease 2019 (COVID-19) pandemic has become a major public health threat. This study aims to evaluate the effect of virus mutation activities and policy interventions on COVID-19 transmissibility in Hong Kong.

Methods: In this study, we integrated the genetic activities of multiple proteins, and quantified the effect of government interventions and mutation activities against the time-varying effective reproduction number R_t.

Findings: We found a significantly positive relationship between R_t and mutation activities and a significantly negative relationship between R_t and government interventions. The results showed that the mutations that contributed most to the increase of R_t were from the spike, nucleocapsid and ORF1b genes. Policy of prohibition on group gathering was estimated to have the largest impact on mitigating virus transmissibility. The model explained 63.2% of the R_t variability with the R².

Conclusion: Our study provided a convenient framework to estimate the effect of genetic contribution and government interventions on pathogen transmissibility. We showed that the S, N and ORF1b protein had significant contribution to the increase of transmissibility of SARS-CoV-2 in Hong Kong, while restrictions of public gathering and suspension of face-to-face class are the most effective government interventions strategies.

Keywords: G-measure; Government interventions; SARS-CoV-2; Virus activities.

MeSH terms

COVID-19*
Government
Humans
Mutation
Pandemics* / prevention & control
SARS-CoV-2 / genetics