Taiwan’s experience with severe acute respiratory syndrome coronavirus (SARS-CoV) in 2003 guided its development of strategies to defend against SARS-CoV-2 in 2020, which enabled the successful control of Coronavirus disease 2019 (COVID-19) cases from 2020 through March 2021. However, in late-April 2021, the imported Alpha variant began to cause COVID-19 outbreaks at an exceptional rate in Taiwan. In this study, we aimed to determine what epidemiological conditions enabled the SARS-CoV-2 Alpha variant strains to become dominant and decline later during a surge in the outbreak. In conjunction with contact-tracing investigations, we used our bioinformatics software, CoVConvert and IniCoV, to analyze whole-genome sequences of 101 Taiwan Alpha strains. Univariate and multivariable regression analyses revealed the epidemiological factors associated with viral dominance. Univariate analysis showed the dominant Alpha strains were preferentially selected in the surge’s epicenter (p = 0.0024) through intensive human-to-human contact and maintained their dominance for 1.5 months until the Zero-COVID Policy was implemented. Multivariable regression found that the epidemic periods (p = 0.007) and epicenter (p = 0.001) were two significant factors associated with the dominant virus strains spread in the community. These dominant virus strains emerged at the outbreak’s epicenter with frequent human-to-human contact and low vaccination coverage. The Level 3 Restrictions and Zero-COVID policy successfully controlled the outbreak in the community without city lockdowns. Our integrated method can identify the epidemiological conditions for emerging dominant virus with increasing epidemiological potential and support decision makers in rapidly containing outbreaks using public health measures that target fast-spreading virus strains. Graphical abstract This figure summarizes our major findings in this study. Before the outbreak (T0, Pre-outbreak), the imported Alpha variant strains were heterogeneous with high viral genome divergence. However, such diversity significantly decreased during the T1 period (versus T0, p < 0.0001), when the dominant virus strains with selective advantages appeared. We also investigated the epidemiological conditions in Taiwan that facilitated the emergence of the predominant virus strain in the T1 period. The effective reproductive numbers over time (Rt) for the viruses from the imported cases were all zero at T0 period before the outbreak. However, the mean Rt values of the viruses from the pilots to quarantine hotel staff and subsequent dominant virus strains in the community (i.e. same sequence identities as the ID-3445/1263/1186) increased rapidly. Specific epidemiological conditions, including unmasked dining in many teahouses, and customers’ movement across teahouses, helped the dominant Alpha variant strains with a selective advantage. This study demonstrated that natural selection of a dominant virus strain (prior to immune selection) progressed in three stages: (1) selection started from a diverse virus pool (i.e. imported viruses at T0), (2) selection advantages increased through virus replication, in which the progeny virus had more advantages than its parent virus, and (3) selection of a fast-spreading virus strain through human-to-human transmission when a community had suitable epidemiological conditions (i.e. our T1 period and epicenter). Most importantly, COVID-19 cases dropped sharply alongside the two important population-intervention strategies (Level 3 Restrictions and Zero-COVID policy). Image 1 Highlights • Frequent human-to-human contact at the epicenter selected dominant SARS-CoV-2 strains. • Dominant SARS-CoV-2 strains had the highest Rt values under specific epidemiological conditions. • Level 3 Restrictions and Zero-COVID Policy were effective without lockdown. • Individual- and population-based prevention measures contained fast-spreading dominant virus strains. • Preventing cluster cases that would have aided viral selection effectively defended against VOCs.
【저자키워드】 whole-genome sequencing, SARS-CoV-2, bioinformatics, Transmission, Reproduction number, Viral variants, Taiwan, community outbreak, Spatio-temporal analysis, epicenter,