Shi Zhao1,2, Jingzhi Lou1, Lirong Cao1, Hong Zheng1, Marc K C Chong1,2, Zigui Chen3, Benny C Y Zee1,2, Paul K S Chan3, Maggie H Wang4,5. 1. JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China. 2. CUHK Shenzhen Research Institute, Shenzhen, China. 3. Department of Microbiology, Chinese University of Hong Kong, Hong Kong, China. 4. JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China. maggiew@cuhk.edu.hk. 5. CUHK Shenzhen Research Institute, Shenzhen, China. maggiew@cuhk.edu.hk.
Abstract
BACKGROUND: The COVID-19 pandemic poses a serious threat to global health, and pathogenic mutations are a major challenge to disease control. We developed a statistical framework to explore the association between molecular-level mutation activity of SARS-CoV-2 and population-level disease transmissibility of COVID-19. METHODS: We estimated the instantaneous transmissibility of COVID-19 by using the time-varying reproduction number (Rt). The mutation activity in SARS-CoV-2 is quantified empirically depending on (i) the prevalence of emerged amino acid substitutions and (ii) the frequency of these substitutions in the whole sequence. Using the likelihood-based approach, a statistical framework is developed to examine the association between mutation activity and Rt. We adopted the COVID-19 surveillance data in California as an example for demonstration. RESULTS: We found a significant positive association between population-level COVID-19 transmissibility and the D614G substitution on the SARS-CoV-2 spike protein. We estimate that a per 0.01 increase in the prevalence of glycine (G) on codon 614 is positively associated with a 0.49% (95% CI: 0.39 to 0.59) increase in Rt, which explains 61% of the Rt variation after accounting for the control measures. We remark that the modeling framework can be extended to study other infectious pathogens. CONCLUSIONS: Our findings show a link between the molecular-level mutation activity of SARS-CoV-2 and population-level transmission of COVID-19 to provide further evidence for a positive association between the D614G substitution and Rt. Future studies exploring the mechanism between SARS-CoV-2 mutations and COVID-19 infectivity are warranted.
BACKGROUND: The COVID-19 pandemic poses a serious threat to global health, and pathogenic mutations are a major challenge to disease control. We developed a statistical framework to explore the association between molecular-level mutation activity of SARS-CoV-2 and population-level disease transmissibility of COVID-19. METHODS: We estimated the instantaneous transmissibility of COVID-19 by using the time-varying reproduction number (Rt). The mutation activity in SARS-CoV-2 is quantified empirically depending on (i) the prevalence of emerged amino acid substitutions and (ii) the frequency of these substitutions in the whole sequence. Using the likelihood-based approach, a statistical framework is developed to examine the association between mutation activity and Rt. We adopted the COVID-19 surveillance data in California as an example for demonstration. RESULTS: We found a significant positive association between population-level COVID-19 transmissibility and the D614G substitution on the SARS-CoV-2spike protein. We estimate that a per 0.01 increase in the prevalence of glycine (G) on codon 614 is positively associated with a 0.49% (95% CI: 0.39 to 0.59) increase in Rt, which explains 61% of the Rt variation after accounting for the control measures. We remark that the modeling framework can be extended to study other infectious pathogens. CONCLUSIONS: Our findings show a link between the molecular-level mutation activity of SARS-CoV-2 and population-level transmission of COVID-19 to provide further evidence for a positive association between the D614G substitution and Rt. Future studies exploring the mechanism between SARS-CoV-2 mutations and COVID-19 infectivity are warranted.
Authors: Shi Zhao; Jingzhi Lou; Marc K C Chong; Lirong Cao; Hong Zheng; Zigui Chen; Renee W Y Chan; Benny C Y Zee; Paul K S Chan; Maggie H Wang Journal: Viruses Date: 2021-04-08 Impact factor: 5.048
Authors: Shi Zhao; Salihu S Musa; Marc Kc Chong; Jinjun Ran; Mohammad Javanbakht; Lefei Han; Kai Wang; Nafiu Hussaini; Abdulrazaq G Habib; Maggie H Wang; Daihai He Journal: J Glob Health Date: 2021-12-25 Impact factor: 4.413
Authors: 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 Journal: J Infect Public Health Date: 2022-02-04 Impact factor: 3.718
Authors: Shi Zhao; Kai Wang; Marc K C Chong; Salihu S Musa; Mu He; Lefei Han; Daihai He; Maggie H Wang Journal: J Theor Biol Date: 2022-03-21 Impact factor: 2.405