ArticlemedRxiv : the preprint server for health sciences2020
Sequencing identifies multiple early introductions of SARS-CoV-2 to the New York City Region.
Article in medRxiv : the preprint server for health sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Mortality and associated risk factors in patients hospitalized due to COVID-19 in a Peruvian reference hospital.PloS one · 2022Article
- Cognitive factors influenced physical distancing adherence during the COVID-19 pandemic in a population-specific way.PloS one · 2022Article
- Optimal use of COVID-19 Ag-RDT screening at border crossings to prevent community transmission: A modeling analysis.PLOS global public health · 2022Article
- Temperature-induced microstructural changes in shells of laboratory-grown Arctica islandica (Bivalvia).PloS one · 2021Article
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45 authors.
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Abstract
Effective public response to a pandemic relies upon accurate measurement of the extent and dynamics of an outbreak. Viral genome sequencing has emerged as a powerful approach to link seemingly unrelated cases, and large-scale sequencing surveillance can inform on critical epidemiological parameters. Here, we report the analysis of 864 SARS-CoV-2 sequences from cases in the New York City metropolitan area during the COVID-19 outbreak in Spring 2020. The majority of cases had no recent travel history or known exposure, and genetically linked cases were spread throughout the region. Comparison to global viral sequences showed that early transmission was most linked to cases from Europe. Our data are consistent with numerous seeds from multiple sources and a prolonged period of unrecognized community spreading. This work highlights the complementary role of genomic surveillance in addition to traditional epidemiological indicators.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.