Tracking the UK SARS-CoV-2 outbreak

doi:10.1126/science.abg2297

GSTDTAP > 气候变化

DOI	10.1126/science.abg2297
	Tracking the UK SARS-CoV-2 outbreak
	Martha I. Nelson
	2021-02-12
发表期刊	Science
出版年	2021
英文摘要	Following a year of relatively uneventful evolution, the emergence and global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants signals an urgent need for better genetic tracking ([ 1 ][1]). The United Kingdom (UK) has emerged as a leader in this domain. A £20 million investment in March 2020 established the COVID-19 Genomics UK (COG-UK) Consortium ([ 2 ][2]), which has produced >200,000 SARS-CoV-2 genomes, more than twice the number produced by any other country. Such a large volume of data provides an unprecedented opportunity to trace which human activities drive epidemic growth during a rapidly changing pandemic, but also introduces numerous bioinformatic challenges. On page 708 of this issue, du Plessis et al. ([ 3 ][3]) describe a new hybrid phylogenetic approach that integrates genetic data with epidemiological and travel data to uncover the roots of the UK's severe spring epidemic. Notably, they find that the UK epidemic resulted from more than 1000 transmission lineages seeded by travelers from Europe. The study shows how last winter's control efforts were consistently one step behind the virus, allowing SARS-CoV-2 to permeate national borders. Their analysis of ∼26,000 UK sequences from January to June 2020, the largest study of its kind, reveals that the UK epidemic was primarily brought into the country by travelers from European neighbors: first Italy, then Spain and France. Peak viral flow into the UK occurred in March as the virus expanded across Western Europe, but surveillance lags led to restrictions still focusing on travelers arriving from Asia. By capturing large numbers of small transmission lineages that would not be detected at lower levels of virological surveillance, as well as >1600 singleton viruses with no observed progeny, the authors uncovered an unprecedented volume of cross-border virus traffic. Genetic patterns mirrored human movement patterns, as the number of viruses entering the UK rose and then fell after international travel plummeted in March. The UK is not the only country whose early focus on Asia as the pandemic epicenter allowed viruses to enter from European sources. Genetic data also traced the origins of epidemics in Brazil ([ 4 ][4]), Boston ([ 5 ][5]), and New York City ([ 6 ][6]) back to Europe. Travel restrictions can be highly effective when stringently implemented, but these studies collectively highlight how easily SARS-CoV-2 infection can arise during even small lapses in border control, including the repatriation of Americans from Asia at the beginning of the pandemic ([ 7 ][7]). There is no magic bullet for triangulating scalability, speed, and statistical rigor as genomic data exceed the capacity of existing platforms. Du Plessis et al. confronted the methodological challenges experienced in previous evolutionary analyses of SARS-CoV-2 ([ 8 ][8]), magnified in this case by a substantially larger dataset. These challenges include low phylogenetic signal among genetically similar viruses, exceeding the capacity of standard phylogenetic software, as well as biases that emerge when other countries sequence different numbers of viruses relative to national case counts. The authors pursue a new approach that uses genetic data to infer the timing and number of virus introductions but uses epidemiological metadata to infer the country of origin. Better integration of genomic and epidemiological data will continue to improve outbreak responses but can be cumbersome without open-access data repositories—for example, for fluctuating global air travel volumes. Epidemiologists increasingly turn to crowd-sourced digital and cellular data to trace human movements and social contact patterns ([ 9 ][9]). Contact tracing has been effective in controlling early COVID-19 outbreaks, such as Europe's first outbreak in Munich, Germany ([ 10 ][10]), and providing key insights into community transmission and the role of superspreading ([ 11 ][11]). But contact tracing is laborious and is often abandoned as epidemics grow. Genetic data can add a new dimension to these efforts by efficiently determining whether two cases belong to the same transmission lineage despite gaps in sampling among individuals in the chain. Du Plessis et al. did not explore heterogeneities in transmission at a city level ([ 5 ][5]), but their observations reveal the growth and size-dependent extinction of hundreds of co-circulating lineages as the national epidemic was brought under control by nonpharmaceutical interventions (NPIs). The study of du Plessis et al. made use of a fraction of the UK sequences generated to date. The risk of new variants emerging increases as SARS-CoV-2 populations surge globally, spilling into immunocompromised, chronically infected, or even nonhuman hosts where they encounter different selection pressures. As SARS-CoV-2 becomes more evolutionarily dynamic, the UK's well-sampled data provide a resource for the global community. Denmark, Australia, and other countries also have intensive SARS-CoV-2 sequencing operations. But the UK is currently the only country with more than 1 million COVID-19 cases that sequences more than 1% of SARS-CoV-2 genomes (the UK sequences ∼5%). The most vexing evolutionary questions require broad population-level analyses based on continuous representative national sampling, with randomized selection of viruses to be sequenced ([ 12 ][12]). A centrally coordinated sampling strategy is a highly advantageous feature of the UK's virological monitoring program, even if it is less quantifiable than speed or volume ([ 2 ][2]). The United States has generated the second-largest number of SARS-CoV-2 genomes, but the proportion of cases sequenced varies markedly among cities and states owing to differences in resources. Large-scale studies become methodologically challenging when datasets are amassed from smaller studies originally designed to address other research questions, introducing biases. At times it has been difficult to assess intriguing hypotheses, such as whether SARS-CoV-2 containing the spike protein D614G mutation spread globally because of fitness advantages or random chance ([ 13 ][13]). Variants that arise in one country quickly become a threat to neighbors. Countries must reciprocate each other's virological monitoring efforts in a rapidly changing global viral landscape. The UK ARTIC network actively shares resources and protocols for SARS-CoV-2 sequencing. NextStrain provides a user-friendly visual platform for tracking SARS-CoV-2 evolution in almost real time. Numerous open-access bioinformatic tools were developed to analyze SARS-CoV-2 sequences ([ 14 ][14]). But a lesson from the UK is the importance of sustained government investment in scalable national infrastructure. Intrepid academic researchers can build popular tools but struggle to scale up as the amount of genomic data explodes. Global coordination would also be helpful, including the universal adoption of a single nomenclature for SARS-CoV-2 lineages. The COVID-19 pandemic has galvanized long-overdue investments in promising research areas at the frontiers of technology and big data. Over the past two decades, faster, cheaper, and more portable sequencing technologies and flexible bioinformatic platforms have laid the groundwork for real-time genomic epidemiology. Leaps in progress tend to be spurred by public health crises, including outbreaks of influenza, Ebola, and Zika ([ 15 ][15]). The COVID-19 leap has begun. 1. [↵][16]1. E. 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领域	气候变化 ; 资源环境
URL	查看原文
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/314077
专题	气候变化资源环境科学
推荐引用方式 GB/T 7714	Martha I. Nelson. Tracking the UK SARS-CoV-2 outbreak[J]. Science,2021.
APA	Martha I. Nelson.(2021).Tracking the UK SARS-CoV-2 outbreak.Science.
MLA	Martha I. Nelson."Tracking the UK SARS-CoV-2 outbreak".Science (2021).