Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 5 de 5
Filtrar
Mais filtros










Base de dados
Intervalo de ano de publicação
1.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-22269922

RESUMO

Regional connectivity and land-based travel have been identified as important drivers of SARS-CoV-2 transmission. However, the generalizability of this finding is understudied outside of well-sampled, highly connected regions such as Europe. In this study, we investigated the relative contributions of regional and intercontinental connectivity to the source-sink dynamics of SARS-CoV-2 for Jordan and the wider Middle East. By integrating genomic, epidemiological and travel data we show that the source of introductions into Jordan was dynamic across 2020, shifting from intercontinental seeding from Europe in the early pandemic to more regional seeding for the period travel restrictions were in place. We show that land-based travel, particularly freight transport, drove introduction risk during the period of travel restrictions. Consistently, high regional connectivity and land-based travel also disproportionately drove Jordans export risk to other Middle Eastern countries. Our findings emphasize regional connectedness and land-based travel as drivers of viral transmission in the Middle East. This demonstrates that strategies aiming to stop or slow the spread of viral introductions (including new variants) with travel restrictions need to prioritize risk from land-based travel alongside intercontinental air travel to be effective. HighlightsO_LIRegional connectivity drove SARS-CoV-2 introduction risk in Jordan during the period travel restrictions were in place in genomic and travel data. C_LIO_LILand-based travel rather than air travel disproportionately drove introduction risk during travel restrictions. C_LIO_LIHigh regional connectivity disproportionately drove Jordans export risk, with significant contribution from land-based travel. C_LIO_LIRegional transmission dynamics were underestimated in genomic data due to unrepresentative sampling. C_LI

2.
Smruthi Karthikeyan; Joshua I Levy; Peter De Hoff; Greg Humphrey; Amanda Birmingham; Kristen Jepsen; Sawyer Farmer; Helena M. Tubb; Tommy Valles; Caitlin E Tribelhorn; Rebecca Tsai; Stefan Aigner; Shashank Sathe; Niema Moshiri; Benjamin Henson; Abbas Hakim; Nathan A Baer; Tom Barber; Pedro Belda-Ferre; Marisol Chacon; Willi Cheung; Evelyn S Crescini; Emily R Eisner; Alma L Lastrella; Elijah S Lawrence; Clarisse A Marotz; Toan T Ngo; Tyler Ostrander; Ashley Plascencia; Rodolfo A Salido; Phoebe Seaver; Elizabeth W Smoot; Daniel McDonald; Robert M Neuhard; Angela L Scioscia; Alysson M. Satterlund; Elizabeth H Simmons; Dismas B. Abelman; David Brenner; Judith Carbone Bruner; Anne Buckley; Michael Ellison; Jeffrey Gattas; Steven L Gonias; Matt Hale; Faith Kirkham Hawkins; Lydia Ikeda; Hemlata Jhaveri; Ted Johnson; Vince Kellen; Brendan Kremer; Gary C. Matthews; Ronald McLawhon; Pierre Ouillet; Daniel Park; Allorah Pradenas; Sharon Reed; Lindsay Riggs; Alison M. Sanders; Bradley Sollenberger; Angela Song; Benjamin White; Terri Winbush; Christine M Aceves; Catelyn Anderson; Karthik Gangavarapu; Emory Hufbauer; Ezra Kurzban; Justin Lee; Nathaniel L Matteson; Edyth Parker; Sarah A Perkins; Karthik S Ramesh; Refugio Robles-Sikisaka; Madison A Schwab; Emily Spencer; Shirlee Wohl; Laura Nicholson; Ian H Mchardy; David P Dimmock; Charlotte A Hobbs; Omid Bakhtar; Aaron Harding; Art Mendoza; Alexandre Bolze; David Becker; Elizabeth T Cirulli; Magnus Isaksson; Kelly M Schiabor Barrett; Nicole L Washington; John D Malone; Ashleigh Murphy Schafer; Nikos Gurfield; Sarah Stous; Rebecca Fielding-Miller; Tommi Gaines; Richard Garfein; Cheryl A. M. Anderson; Natasha K. Martin; Robert T Schooley; Brett Austin; Duncan R. MacCannell; Stephen F Kingsmore; William Lee; Seema Shah; Eric McDonald; Alexander T. Yu; Mark Zeller; Kathleen M Fisch; Christopher A. Longhurst; Patty Maysent; David Pride; Pradeep K. Khosla; Louise C Laurent; Gene W Yeo; Kristian G Andersen; Rob Knight.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-21268143

RESUMO

As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing/sequencing capacity, which can also introduce biases. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here, we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We develop and deploy improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detect emerging variants of concern up to 14 days earlier in wastewater samples, and identify multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

3.
Sydney C Morgan; Stefan Aigner; Catelyn Anderson; Pedro Belda-Ferre; Peter De Hoff; Clarisse A Marotz; Shashank Sathe; Mark Zeller; Noorsher Ahmed; Xaver Audhya; Nathan A Baer; Tom Barber; Bethany Barrick; Lakshmi Batachari; Maryann Betty; Steven M Blue; Brent Brainard; Tyler Buckley; Jamie Case; Anelizze Castro-Martinez; Marisol Chacon; Willi Cheung; LaVonnye Chong; Nicole G Coufal; Evelyn S Crescini; Scott DeGrand; David P Dimmock; J Joelle Donofrio-Odmann; Emily R Eisner; Mehrbod Estaki; Lizbeth Franco Vargas; Michele Freddock; Robert M Gallant; Andrea Galmozzi; Nina J Gao; Sheldon Gilmer; Edyta M Grzelak; Abbas Hakim; Jonathan Hart; Charlotte Hobbs; Greg Humphrey; Nadja Ilkenhans; Marni Jacobs; Christopher A Kahn; Bhavika K Kapadia; Matthew Kim; Sunil Kurian; Alma L Lastrella; Elijah S Lawrence; Kari Lee; Qishan Liang; Hanna Liliom; Valentina Lo Sardo; Robert Logan; Michal Machnicki; Celestine G Magallanes; Clarence K Mah; Denise Malacki; Ryan J Marina; Christopher Marsh; Natasha K Martin; Nathaniel L Matteson; Daniel J Maunder; Kyle McBride; Bryan McDonald; Daniel McDonald; Michelle McGraw; Audra R Meadows; Michelle Meyer; Amber L Morey; Jasmine R Mueller; Toan T Ngo; Julie Nguyen; Viet Nguyen; Laura J Nicholson; Alhakam Nouri; Victoria Nudell; Eugenio Nunez; Kyle O'Neill; R Tyler Ostrander; Priyadarshini Pantham; Samuel S Park; David Picone; Ashley Plascencia; Isaraphorn Pratumchai; Michael Quigley; Michelle Franc Ragsac; Andrew C Richardson; Refugio Robles-Sikisaka; Christopher A Ruiz; Justin Ryan; Lisa Sacco; Sharada Saraf; Phoebe Seaver; Leigh Sewall; Elizabeth W Smoot; Kathleen M Sweeney; Chandana Tekkatte; Rebecca Tsai; Holly Valentine; Shawn Walsh; August Williams; Min Yi Wu; Bing Xia; Brian Yee; Jason Z Zhang; Kristian G Andersen; Lauge Farnaes; Rob Knight; Gene W Yeo; Louise C Laurent.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-21257885

RESUMO

BackgroundSuccessful containment strategies for SARS-CoV-2, the causative virus of the COVID-19 pandemic, have involved widespread population testing that identifies infections early and enables rapid contact tracing. In this study, we developed a rapid and inexpensive RT- qPCR testing pipeline for population-level SARS-CoV-2 detection, and used this pipeline to establish a clinical laboratory dedicated to COVID-19 testing at the University of California San Diego (UCSD) with a processing capacity of 6,000 samples per day and next-day result turnaround times. Methods and findingsUsing this pipeline, we screened 6,786 healthcare workers and first responders, and 21,220 students, faculty, and staff from UCSD. Additionally, we screened 6,031 preschool-grade 12 students and staff from public and private schools across San Diego County that remained fully or partially open for in-person teaching during the pandemic. Between April 17, 2020 and February 5, 2021, participants provided 161,582 nasal swabs that were tested for the presence of SARS-CoV-2. Overall, 752 positive tests were obtained, yielding a test positivity rate of 0.47%. While the presence of symptoms was significantly correlated with higher viral load, most of the COVID-19 positive participants who participated in symptom surveys were asymptomatic at the time of testing. The positivity rate among preschool-grade 12 schools that remained open for in-person teaching was similar to the positivity rate at UCSD and lower than that of San Diego County, with the children in private schools being less likely to test positive than the adults at these schools. ConclusionsMost schools across the United States have been closed for in-person learning for much of the 2020-2021 school year, and their safe reopening is a national priority. However, as there are no vaccines against SARS-CoV-2 currently available to the majority of school-aged children, the traditional strategies of mandatory masking, physical distancing, and repeated viral testing of students and staff remain key components of risk mitigation in these settings. The data presented here suggest that the safety measures and repeated testing actions taken by participating healthcare and educational facilities were effective in preventing outbreaks, and that a similar combination of risk-mitigation strategies and repeated testing may be successfully adopted by other healthcare and educational systems.

4.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-21251235

RESUMO

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.

5.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-21251159

RESUMO

As of January of 2021, the highly transmissible B.1.1.7 variant of SARS-CoV-2, which was first identified in the United Kingdom (U.K.), has gained a strong foothold across the world. Because of the sudden and rapid rise of B.1.1.7, we investigated the prevalence and growth dynamics of this variant in the United States (U.S.), tracking it back to its early emergence and onward local transmission. We found that the RT-qPCR testing anomaly of S gene target failure (SGTF), first observed in the U.K., was a reliable proxy for B.1.1.7 detection. We sequenced 212 B.1.1.7 SARS-CoV-2 genomes collected from testing facilities in the U.S. from December 2020 to January 2021. We found that while the fraction of B.1.1.7 among SGTF samples varied by state, detection of the variant increased at a logistic rate similar to those observed elsewhere, with a doubling rate of a little over a week and an increased transmission rate of 35-45%. By performing time-aware Bayesian phylodynamic analyses, we revealed several independent introductions of B.1.1.7 into the U.S. as early as late November 2020, with onward community transmission enabling the variant to spread to at least 30 states as of January 2021. Our study shows that the U.S. is on a similar trajectory as other countries where B.1.1.7 rapidly became the dominant SARS-CoV-2 variant, requiring immediate and decisive action to minimize COVID-19 morbidity and mortality.

SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...