scholarly journals Tracking SARS-CoV-2 Spike Protein Mutations in the United States (January 2020–March 2021) Using a Statistical Learning Strategy

Viruses ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 9
Author(s):  
Lue Ping Zhao ◽  
Terry P. Lybrand ◽  
Peter B. Gilbert ◽  
Thomas R. Hawn ◽  
Joshua T. Schiffer ◽  
...  
Keyword(s):  
United States ◽  
Unsupervised Learning ◽  
Statistical Learning ◽  
Learning Strategy ◽  
Temporal Dynamics ◽  
Generalized Additive Models ◽  
The United States ◽  
Additive Models ◽  
Spike Protein ◽  
Protein Mutations

The emergence and establishment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of interest (VOIs) and variants of concern (VOCs) highlight the importance of genomic surveillance. We propose a statistical learning strategy (SLS) for identifying and spatiotemporally tracking potentially relevant Spike protein mutations. We analyzed 167,893 Spike protein sequences from coronavirus disease 2019 (COVID-19) cases in the United States (excluding 21,391 sequences from VOI/VOC strains) deposited at GISAID from 19 January 2020 to 15 March 2021. Alignment against the reference Spike protein sequence led to the identification of viral residue variants (VRVs), i.e., residues harboring a substitution compared to the reference strain. Next, generalized additive models were applied to model VRV temporal dynamics and to identify VRVs with significant and substantial dynamics (false discovery rate q-value < 0.01; maximum VRV proportion >10% on at least one day). Unsupervised learning was then applied to hierarchically organize VRVs by spatiotemporal patterns and identify VRV-haplotypes. Finally, homology modeling was performed to gain insight into the potential impact of VRVs on Spike protein structure. We identified 90 VRVs, 71 of which had not previously been observed in a VOI/VOC, and 35 of which have emerged recently and are durably present. Our analysis identified 17 VRVs ~91 days earlier than their first corresponding VOI/VOC publication. Unsupervised learning revealed eight VRV-haplotypes of four VRVs or more, suggesting two emerging strains (B1.1.222 and B.1.234). Structural modeling supported a potential functional impact of the D1118H and L452R mutations. The SLS approach equally monitors all Spike residues over time, independently of existing phylogenic classifications, and is complementary to existing genomic surveillance methods.

scholarly journals Tracking SARS-CoV-2 Spike Protein Mutations in the United States (2020/01 – 2021/03) Using a Statistical Learning Strategy

2021 ◽  
Author(s):  
Lue Ping Zhao ◽  
Terry P. Lybrand ◽  
Peter B. Gilbert ◽  
Thomas R. Hawn ◽  
Joshua T. Schiffer ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Statistical Learning ◽  
Learning Strategy ◽  
Temporal Dynamics ◽  
Homology Modelling ◽  
Generalized Additive Models ◽  
The United States ◽  
Additive Models ◽  
Spike Protein ◽  
Protein Mutations

The emergence and establishment of SARS-CoV-2 variants of interest (VOI) and variants of concern (VOC) highlight the importance of genomic surveillance. We propose a statistical learning strategy (SLS) for identifying and spatiotemporally tracking potentially relevant Spike protein mutations. We analyzed 167,893 Spike protein sequences from US COVID-19 cases (excluding 21,391 sequences from VOI/VOC strains) deposited at GISAID from January 19, 2020 to March 15, 2021. Alignment against the reference Spike protein sequence led to the identification of viral residue variants (VRVs), i.e., residues harboring a substitution compared to the reference strain. Next, generalized additive models were applied to model VRV temporal dynamics, to identify VRVs with significant and substantial dynamics (false discovery rate q-value <0.01; maximum VRV proportion > 10% on at least one day). Unsupervised learning was then applied to hierarchically organize VRVs by spatiotemporal patterns and identify VRV-haplotypes. Finally, homology modelling was performed to gain insight into potential impact of VRVs on Spike protein structure. We identified 90 VRVs, 71 of which have not previously been observed in a VOI/VOC, and 35 of which have emerged recently and are durably present. Our analysis identifies 17 VRVs ~91 days earlier than their first corresponding VOI/VOC publication. Unsupervised learning revealed eight VRV-haplotypes of 4 VRVs or more, suggesting two emerging strains (B1.1.222 and B.1.234). Structural modeling supported potential functional impact of the D1118H and L452R mutations. The SLS approach equally monitors all Spike residues over time, independently of existing phylogenic classifications, and is complementary to existing genomic surveillance methods.

scholarly journals Tracking SARS-CoV-2 Spike Protein Mutations in the United States (2020/01 – 2021/03) Using a Statistical Learning Strategy

2021 ◽  
Author(s):  
Lue Ping Zhao ◽  
Terry P. Lybrand ◽  
Peter B. Gilbert ◽  
Thomas R. Hawn ◽  
Joshua Schiffer ◽  
...  
Keyword(s):  
United States ◽  
Statistical Learning ◽  
Learning Strategy ◽  
The United States ◽  
Spike Protein ◽  
Protein Mutations

Trends in Ketamine Use, Exposures, and Seizures in the United States

2021 ◽  
pp. e1-e4
Author(s):  
Joseph J. Palamar ◽  
Caroline Rutherford ◽  
Katherine M. Keyes
Keyword(s):  
Public Health ◽  
United States ◽  
Media Coverage ◽  
Generalized Additive Models ◽  
The United States ◽  
Additive Models ◽  
Nonmedical Use ◽  
Medical Indications

Objectives. To determine whether there have been shifts in nonmedical ketamine use, poisonings (“exposures”), and seizures. Methods. We used generalized additive models to detect trends in past-year use (2006–2019), exposures (1991–2019), and seizures (2000–2019) involving ketamine in the United States. Results. There was a quarterly increase in self-reported past-year nonmedical ketamine use in 2006 to 2014 (Β = 0.21; P = .030) and an increase in 2015 to 2019 (Β = 0.29; P = .036), reaching a peak of 0.9% in late 2019. The rate of exposures increased from 1991 through 2019 (Β = 0.87; P = .006), and there was an increase to 1.1 exposures per 1 000 000 population in 2014, with rates remaining stable through 2019. The rate of ketamine seizures increased from 2000 through 2019 (Β = 2.27; P < .001), with seizures reaching a peak in 2019 at 3.2 per 1000 seizures. Conclusions. Indicators suggest that ketamine use and availability has increased, including before increased medical indications, but nonmedical use is still currently uncommon despite increased acceptance and media coverage. (Am J Public Health. Published online ahead of print October 7, 2021:e1–e4. https://doi.org/10.2105/AJPH.2021.306486 )

scholarly journals Spatial and temporal analysis of fatal off-piste and backcountry avalanche accidents in Austria with a comparison of results in Switzerland, France, Italy and the United States

2016 ◽  
Author(s):  
Christian Pfeifer ◽  
Peter Höller ◽  
Achim Zeileis
Keyword(s):  
United States ◽  
Generalized Additive Models ◽  
Regional Distribution ◽  
Temporal Analysis ◽  
The United States ◽  
Additive Models ◽  
Spatial And Temporal Patterns ◽  
Federal States ◽  
Increasing Trend ◽  
Avalanche Accidents

Abstract. In this article we analyzed spatial and temporal patterns of fatal Austrian avalanche accidents caused by backcountry and off-piste skiers and snowboarders within the winter periods 1967/68–2010/11. The data were based on reports of the Austrian Board for Alpine Safety and reports of the information services of the federal states. Using the date and the location of the recorded avalanche accidents we were able to carry out spatial and temporal analyses applying generalized additive models and Markov random field models. As the result of the trend analysis we noticed an increasing trend of avalanche fatalities within the winter periods from 1967/68 to 2010/11, which is in contradiction to the widespread opinion that the number of fatalities is constant over time. Additionally, we compared Austrian results with results of Switzerland, France, Italy and the United States based on data from the International Commission of Alpine Rescue (ICAR). As the result of the spatial analysis we noticed two hotspots of avalanche fatalities ("Arlberg-Silvretta" and "Sölden"). Because of the increasing trend and the rather "narrow" regional distribution of the fatalities consequences on prevention of avalanche accidents were highly recommended.

scholarly journals Rapidly emerging SARS-CoV-2 B.1.1.7 sub-lineage in the United States of America with spike protein D178H and membrane protein V70L mutations

2021 ◽  
pp. 1-13
Author(s):  
Lishuang Shen ◽  
Jennifer Dien Bard ◽  
Timothy J. Triche ◽  
Alexander R. Judkins ◽  
Jaclyn A. Biegel ◽  
...  
Keyword(s):  
United States ◽  
Membrane Protein ◽  
United States Of America ◽  
The United States ◽  
Spike Protein

Wealth and governance in unstable states : South Africa as a context for Mancur Olson's theory of collective action

2016 ◽  
Author(s):  
◽  
Pamela E. Kelrick
Keyword(s):  
South Africa ◽  
Developing Countries ◽  
Collective Action ◽  
Temporal Dynamics ◽  
Generalized Additive Models ◽  
Political Stability ◽  
Developed Countries ◽  
Additive Models ◽  
Policy Implications ◽  
Empirical Analyses

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Mancur Olson's theory of collective action has primarily been construed and applied to developed countries with formal economies and (generally) socio-political stability. Yet, he asserted that his theory of collective action would apply in developing countries, even those which are far less stable. This study examined Olson's assertion that collective action applies in developing countries, using South Africa as a case study. The empirical analyses included canonical correlation analysis and generalized additive models, using attribute, spatial, and temporal data to understand the spatial and temporal dynamics between wealth and governance in South Africa. Geographic clustering by race and economic class remains persistent despite democratic reforms and improved governance engagement. In addition, findings of the empirical analyses were used to evaluate Olson's theory of collective action and frame the policy implications. Collective action is consistent with findings, but, in the context of developing countries, ought to include more prominent considerations of path dependency, increasing returns, and historical institutionalism.

scholarly journals Impact of the first COVID-19 shelter-in-place order in the United States on emergency department utilization, Marin County, California

2020 ◽  
Author(s):  
Brett R. Bayles ◽  
Michaela F George ◽  
Haylea Hannah ◽  
Patti Culross ◽  
Rochelle R. Ereman ◽  
...  
Keyword(s):  
United States ◽  
San Francisco ◽  
Large Scale ◽  
Temporal Dynamics ◽  
The United States ◽  
Emergency Department Utilization ◽  
Marin County ◽  
Before And After ◽  
Ed Visits ◽  
The Impact

Background: The first shelter-in-place (SIP) order in the United States was issued across six counties in the San Francisco Bay Area to reduce the impact of COVID-19 on critical care resources. We sought to assess the impact of this large-scale intervention on emergency departments (ED) in Marin County, California. Methods: We conducted a retrospective descriptive and trend analysis of all ED visits in Marin County, California from January 1, 2018 to May 4, 2020 to quantify the temporal dynamics of ED utilization before and after the March 17, 2020 SIP order. Results: The average number of ED visits per day decreased by 52.3% following the SIP order compared to corresponding time periods in 2018 and 2019. Both respiratory and non-respiratory visits declined, but this negative trend was most pronounced for non-respiratory admissions. Conclusions: The first SIP order to be issued in the United States in response to COVID-19 was associated with a significant reduction in ED utilization in Marin County.

scholarly journals COVID-19 In United States: Statistical Analysis and Inferences From the Empirical Data

2021 ◽  
Author(s):  
Nivedita Rethnakar
Keyword(s):  
United States ◽  
Statistical Analysis ◽  
Statistical Inference ◽  
Empirical Data ◽  
Respiratory Diseases ◽  
Temporal Dynamics ◽  
The United States ◽  
Mortality Statistics ◽  
Race Ethnicity ◽  
The Impact

Abstract This paper investigates the mortality statistics of the COVID-19 pandemic from the United States perspective. Using empirical data analysis and statistical inference tools, we bring out several exciting and important aspects of the pandemic, otherwise hidden. Specific patterns seen in demo- graphics such as race/ethnicity and age are discussed both qualitatively and quantitatively. We also study the role played by factors such as population density. Connections between COVID-19 and other respiratory diseases are also covered in detail. The temporal dynamics of the COVID-19 outbreak and the impact of vaccines in controlling the pandemic are also looked at with suf- ficient rigor. It is hoped that statistical inference such as the ones gathered in this paper would be helpful for better scientific understanding, policy prepa- ration and thus adequately preparing, should a similar situation arise in the future.

scholarly journals Undiagnosed SARS-CoV-2 Seropositivity During the First Six Months of the COVID-19 Pandemic in the United States

2021 ◽  
pp. eabh3826
Author(s):  
Heather Kalish ◽  
Carleen Klumpp-Thomas ◽  
Sally Hunsberger ◽  
Holly Ann Baus ◽  
Michael P Fay ◽  
...  
Keyword(s):  
United States ◽  
The United States ◽  
Spike Protein ◽  
Behavioral Risk ◽  
Urban Centers ◽  
Blood Samples ◽  
Protein Receptor ◽  
Behavioral Risk Factor Surveillance ◽  
Urban Rural ◽  
Quota Sampling

Asymptomatic SARS-CoV-2 infection and delayed implementation of diagnostics have led to poorly defined viral prevalence rates in the United States and elsewhere. To address this, we analyzed seropositivity in 9,089 adults in the United States who had not been diagnosed previously with COVID-19. Individuals with characteristics that reflected the US population (n = 27,716) were selected by quota sampling from 462,949 volunteers. Enrolled participants (n = 11,382) provided medical, geographic, demographic, and socioeconomic information, and dried blood samples. Survey questions coincident with the Behavioral Risk Factor Surveillance System survey, a large probability-based national survey, were used to adjust for selection bias. The majority (88.7%) of blood samples were collected between May 10th and July 31st, 2020 and were processed using ELISA to measure seropositivity (IgG and IgM antibodies against SARS-CoV-2 spike protein and the spike protein receptor binding domain). The overall weighted undiagnosed seropositivity estimate was 4.6% (95% CI: 2.6-6.5%) with race, age, sex, ethnicity, and urban/rural subgroup estimates ranging from 1.1% to 14.2%; the highest seropositivity estimates were in African American participants, younger, female, and Hispanic participants, and residents of urban centers. These data indicate that there were 4.8 undiagnosed SARS-CoV-2 infections for every diagnosed case of COVID-19, and an estimated 16.8 million infections were undiagnosed by mid-July 2020 in the United States.

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