Quantifying the effect of government interventions and virus mutations on transmission advantage during COVID-19 pandemic
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. In this study, we integrated the genetic activities of multiple proteins,...
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| Published in: | Journal of infection and public health Vol. 15; no. 3; pp. 338 - 342 |
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| Language: | English |
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01-03-2022
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| Abstract | 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.
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 Rt.
We found a significantly positive relationship between Rt and mutation activities and a significantly negative relationship between Rt and government interventions. The results showed that the mutations that contributed most to the increase of Rt 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 Rt variability with the R2.
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. |
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| AbstractList | BACKGROUNDThe 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. METHODSIn 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 Rt. FINDINGSWe found a significantly positive relationship between Rt and mutation activities and a significantly negative relationship between Rt and government interventions. The results showed that the mutations that contributed most to the increase of Rt 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 Rt variability with the R2. CONCLUSIONOur 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. 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 Rt. Findings: We found a significantly positive relationship between Rt and mutation activities and a significantly negative relationship between Rt and government interventions. The results showed that the mutations that contributed most to the increase of Rt 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 Rt 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. 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. 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 Rt. We found a significantly positive relationship between Rt and mutation activities and a significantly negative relationship between Rt and government interventions. The results showed that the mutations that contributed most to the increase of Rt 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 Rt variability with the R2. 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. 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. 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 . We found a significantly positive relationship between R and mutation activities and a significantly negative relationship between R and government interventions. The results showed that the mutations that contributed most to the increase of R 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 variability with the R . 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. |
| Author | Wong, Eliza LY Zee, Benny CY Lou, Jingzhi Chan, Paul KS Yeoh, Eng-kiong Cao, Lirong Chan, Renee WY Chong, Marc KC Wang, Maggie H Zheng, Hong Chen, Zigui Zhao, Shi |
| Author_xml | – sequence: 1 givenname: Jingzhi surname: Lou fullname: Lou, Jingzhi email: jzlou@qq.com organization: JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Hong Kong, China – sequence: 2 givenname: Hong surname: Zheng fullname: Zheng, Hong email: hongzheng@cuhk.edu.hk organization: JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Hong Kong, China – sequence: 3 givenname: Shi surname: Zhao fullname: Zhao, Shi email: zhaoshi.cmsa@gmail.com organization: JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Hong Kong, China – sequence: 4 givenname: Lirong surname: Cao fullname: Cao, Lirong email: caolr@link.cuhk.edu.hk organization: JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Hong Kong, China – sequence: 5 givenname: Eliza LY surname: Wong fullname: Wong, Eliza LY email: lywong@cuhk.edu.hk organization: JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Hong Kong, China – sequence: 6 givenname: Zigui surname: Chen fullname: Chen, Zigui email: zigui.chen@cuhk.edu.hk organization: Department of Microbiology, the Chinese University of Hong Kong, Hong Kong, China – sequence: 7 givenname: Renee WY surname: Chan fullname: Chan, Renee WY email: reneewy@cuhk.edu.hk organization: Department of Paediatric, the Chinese University of Hong Kong, Hong Kong, China – sequence: 8 givenname: Marc KC surname: Chong fullname: Chong, Marc KC email: marc@cuhk.edu.hk organization: JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Hong Kong, China – sequence: 9 givenname: Benny CY surname: Zee fullname: Zee, Benny CY email: bzee@cuhk.edu.hk organization: JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Hong Kong, China – sequence: 10 givenname: Paul KS surname: Chan fullname: Chan, Paul KS email: paulkschan@cuhk.edu.hk organization: Department of Microbiology, the Chinese University of Hong Kong, Hong Kong, China – sequence: 11 givenname: Eng-kiong surname: Yeoh fullname: Yeoh, Eng-kiong email: yeoh_ek@cuhk.edu.hk organization: JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Hong Kong, China – sequence: 12 givenname: Maggie H surname: Wang fullname: Wang, Maggie H email: maggiew@cuhk.edu.hk organization: JC School of Public Health and Primary Care, the Chinese University of Hong Kong, Hong Kong, China |
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| Keywords | SARS-CoV-2 Virus activities Government interventions G-measure |
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| Title | Quantifying the effect of government interventions and virus mutations on transmission advantage during COVID-19 pandemic |
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