| Autor: |
Shi Zhao, Jingzhi Lou, Lirong Cao, Hong Zheng, Marc K. C. Chong, Zigui Chen, Renee W. Y. Chan, Benny C. Y. Zee, Paul K. S. Chan, Maggie H. Wang |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
BMC Infectious Diseases, Vol 21, Iss 1, Pp 1-8 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1471-2334 |
| DOI: |
10.1186/s12879-021-06729-w |
| Popis: |
Abstract Background The COVID-19 pandemic poses serious threats to global health, and the emerging mutation in SARS-CoV-2 genomes, e.g., the D614G substitution, is one of the major challenges of disease control. Characterizing the role of the mutation activities is of importance to understand how the evolution of pathogen shapes the epidemiological outcomes at population scale. Methods We developed a statistical framework to reconstruct variant-specific reproduction numbers and estimate transmission advantage associated with the mutation activities marked by single substitution empirically. Using likelihood-based approach, the model is exemplified with the COVID-19 surveillance data from January 1 to June 30, 2020 in California, USA. We explore the potential of this framework to generate early warning signals for detecting transmission advantage on a real-time basis. Results The modelling framework in this study links together the mutation activity at molecular scale and COVID-19 transmissibility at population scale. We find a significant transmission advantage of COVID-19 associated with the D614G substitution, which increases the infectivity by 54% (95%CI: 36, 72). For the early alarming potentials, the analytical framework is demonstrated to detect this transmission advantage, before the mutation reaches dominance, on a real-time basis. Conclusions We reported an evidence of transmission advantage associated with D614G substitution, and highlighted the real-time estimating potentials of modelling framework. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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