Sequencing identifies multiple early introductions of SARS-CoV-2 to the New York City region

Maurano, Matthew T.; Ramaswami, Sitharam; Zappile, Paul; Dimartino, Dacia; Boytard, Ludovic; Ribeiro-dos-Santos, André M.; Vulpescu, Nicholas A.; Westby, Gael; Shen, Guomiao; Feng, Xiaojun; Hogan, Megan S.; Ragonnet‐Cronin, Manon; Geidelberg, Lily; Marier, Christian; Meyn, Peter; Zhang, Yutong; Cadley, John; Ordóñez, Raquel; Luther, Raven; Huang, Emily; Guzman, Emily; Argüelles-Grande, Carolina; Argyropoulos, Kimon V.; Black, Margaret A.; Serrano, Antonio E.; Call, Melissa; Kim, Min Jae; Belovarac, Brendan; Gindin, Tatyana; Lytle, Andrew; Pinnell, Jared; Vougiouklakis, Theodore; Chen, John; Lin, Lawrence Hsu; Rapkiewicz, Amy; Raabe, Vanessa; Samanovic, Marie I.; Jour, George; Osman, Iman; Agüero-Rosenfeld, Maria E.; Mulligan, Mark J.; Volz, Erik; Cotzia, Paolo; Snuderl, Matija; Heguy, Adriana

doi:10.1101/gr.266676.120

Cited by 73 publications

(77 citation statements)

References 35 publications

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“…By late February, wide-spread community transmission of SARS-CoV-2 in the U.S. was identified in Washington state 7 , New York City 8 , and Santa Clara County in California 9 , but it is estimated that local transmission in the U.S. started earlier and was more wide-spread than recognized at the time 10,11 . Elsewhere, outside of these early virus 'hotspots' in the U.S., transmission of SARS-CoV-2 occurred mostly silently due to lack of testing until the second week of March 10,12,13 .…”

Section: Introductionmentioning

confidence: 99%

Emergence of an early SARS-CoV-2 epidemic in the United States

Zeller

Gangavarapu

Anderson

et al. 2021

Preprint

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The emergence of the early COVID-19 epidemic in the United States (U.S.) went largely undetected, due to a lack of adequate testing and mitigation efforts. The city of New Orleans, Louisiana experienced one of the earliest and fastest accelerating outbreaks, coinciding with the annual Mardi Gras festival, which went ahead without precautions. To gain insight into the emergence of SARS-CoV-2 in the U.S. and how large, crowded events may have accelerated early transmission, we sequenced SARS-CoV-2 genomes during the first wave of the COVID-19 epidemic in Louisiana. We show that SARS-CoV-2 in Louisiana initially had limited sequence diversity compared to other U.S. states, and that one successful introduction of SARS-CoV-2 led to almost all of the early SARS-CoV-2 transmission in Louisiana. By analyzing mobility and genomic data, we show that SARS-CoV-2 was already present in New Orleans before Mardi Gras and that the festival dramatically accelerated transmission, eventually leading to secondary localized COVID-19 epidemics throughout the Southern U.S.. Our study provides an understanding of how superspreading during large-scale events played a key role during the early outbreak in the U.S. and can greatly accelerate COVID-19 epidemics on a local and regional scale.

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Section: Introductionmentioning

confidence: 99%

Emergence of an early SARS-CoV-2 epidemic in the United States

Zeller

Gangavarapu

Anderson

et al. 2021

Preprint

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“…NGS methods have been rapidly adapted to the SARS-CoV-2 paradigm and shown to be applicable to a wide variety of associated biological questions [ 35 , 69 , 82 , 88 , 90 , 93 , 99 ]. The rate of data production and analysis has been unprecedented and would have been inconceivable only a few years ago.…”

Section: Resultsmentioning

confidence: 99%

“…While one hand, Xiao et al . [ 87 ] found that hybrid capture sequencing is less sensitive than amplicon-based methods for the sequencing of SARS-CoV-2 genomes, and did not recommend its application for challenging samples with low viral loads, in other studies enrichment by hybridization has been successful even for samples with very low viral loads [ 93 ]. Capture-based methods may also offer unbiased representation of intra-sample variants.…”

Section: High-throughput Sequencing For Covid-19 Pandemicmentioning

confidence: 99%

Next generation sequencing of SARS-CoV-2 genomes: challenges, applications and opportunities

Chiara

D’Erchia

Gissi

et al. 2020

Briefings in Bioinformatics

191

153

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Various next generation sequencing (NGS) based strategies have been successfully used in the recent past for tracing origins and understanding the evolution of infectious agents, investigating the spread and transmission chains of outbreaks, as well as facilitating the development of effective and rapid molecular diagnostic tests and contributing to the hunt for treatments and vaccines. The ongoing COVID-19 pandemic poses one of the greatest global threats in modern history and has already caused severe social and economic costs. The development of efficient and rapid sequencing methods to reconstruct the genomic sequence of SARS-CoV-2, the etiological agent of COVID-19, has been fundamental for the design of diagnostic molecular tests and to devise effective measures and strategies to mitigate the diffusion of the pandemic. Diverse approaches and sequencing methods can, as testified by the number of available sequences, be applied to SARS-CoV-2 genomes. However, each technology and sequencing approach has its own advantages and limitations. In the current review, we will provide a brief, but hopefully comprehensive, account of currently available platforms and methodological approaches for the sequencing of SARS-CoV-2 genomes. We also present an outline of current repositories and databases that provide access to SARS-CoV-2 genomic data and associated metadata. Finally, we offer general advice and guidelines for the appropriate sharing and deposition of SARS-CoV-2 data and metadata, and suggest that more efficient and standardized integration of current and future SARS-CoV-2-related data would greatly facilitate the struggle against this new pathogen. We hope that our ‘ vademecum ’ for the production and handling of SARS-CoV-2-related sequencing data, will contribute to this objective.

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“…Genomic evidence from early SARS-CoV-2 strains suggests that initial infections in the United States were introduced to Washington State, a hub for travel to East Asia, by travelers from China [ 18 - 20 ]. There is also evidence that suggests New York City likely served as a primary entry point to the United States for the SARS-CoV-2 contagion from European hotspots in Spain and Italy early in the pandemic [ 19 , 21 , 22 ]. Chicago, Boston, and Detroit were likely additional entry points because of the large number of international travelers—Detroit’s metro airport had over 1100 flights per day to 4 continents including direct flights to China and Europe [ 23 ], and was the least busy of the airports.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Panel Data Modeling and Surveillance of COVID-19 in Metropolitan Areas in the United States: Longitudinal Trend Analysis

Oehmke¹,

Post²,

Moss³

et al. 2021

J Med Internet Res

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Background The COVID-19 pandemic has had profound and differential impacts on metropolitan areas across the United States and around the world. Within the United States, metropolitan areas that were hit earliest with the pandemic and reacted with scientifically based health policy were able to contain the virus by late spring. For other areas that kept businesses open, the first wave in the United States hit in mid-summer. As the weather turns colder, universities resume classes, and people tire of lockdowns, a second wave is ascending in both metropolitan and rural areas. It becomes more obvious that additional SARS-CoV-2 surveillance is needed at the local level to track recent shifts in the pandemic, rates of increase, and persistence. Objective The goal of this study is to provide advanced surveillance metrics for COVID-19 transmission that account for speed, acceleration, jerk and persistence, and weekly shifts, to better understand and manage risk in metropolitan areas. Existing surveillance measures coupled with our dynamic metrics of transmission will inform health policy to control the COVID-19 pandemic until, and after, an effective vaccine is developed. Here, we provide values for novel indicators to measure COVID-19 transmission at the metropolitan area level. Methods Using a longitudinal trend analysis study design, we extracted 260 days of COVID-19 data from public health registries. We used an empirical difference equation to measure the daily number of cases in the 25 largest US metropolitan areas as a function of the prior number of cases and weekly shift variables based on a dynamic panel data model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. Results Minneapolis and Chicago have the greatest average number of daily new positive results per standardized 100,000 population (which we refer to as speed). Extreme behavior in Minneapolis showed an increase in speed from 17 to 30 (67%) in 1 week. The jerk and acceleration calculated for these areas also showed extreme behavior. The dynamic panel data model shows that Minneapolis, Chicago, and Detroit have the largest persistence effects, meaning that new cases pertaining to a specific week are statistically attributable to new cases from the prior week. Conclusions Three of the metropolitan areas with historically early and harsh winters have the highest persistence effects out of the top 25 most populous metropolitan areas in the United States at the beginning of their cold weather season. With these persistence effects, and with indoor activities becoming more popular as the weather gets colder, stringent COVID-19 regulations will be more important than ever to flatten the second wave of the pandemic. As colder weather grips more of the nation, southern metropolitan areas may also see large spikes in the number of cases.

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Sequencing identifies multiple early introductions of SARS-CoV-2 to the New York City region

Cited by 73 publications

References 35 publications

Emergence of an early SARS-CoV-2 epidemic in the United States

Emergence of an early SARS-CoV-2 epidemic in the United States

Next generation sequencing of SARS-CoV-2 genomes: challenges, applications and opportunities

Dynamic Panel Data Modeling and Surveillance of COVID-19 in Metropolitan Areas in the United States: Longitudinal Trend Analysis

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