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 (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 Schiffer ◽  
...  
Keyword(s):  
United States ◽  
Statistical Learning ◽  
Learning Strategy ◽  
The United States ◽  
Spike Protein ◽  
Protein Mutations

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.

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 Racial, Ethnic, and Geographic Disparities in Novel Coronavirus (SARS-CoV-2) Test Positivity in North Carolina

2020 ◽  
Author(s):  
Nicholas A Turner ◽  
William Pan ◽  
Viviana S Martinez-Bianchi ◽  
Gabriela M Maradiaga Panayotti ◽  
Arrianna M Planey ◽  
...  
Keyword(s):  
Population Density ◽  
Generalized Additive Models ◽  
Black Population ◽  
The United States ◽  
Household Size ◽  
Additive Models ◽  
Area Deprivation ◽  
Individual Level ◽  
Novel Coronavirus ◽  
Neighborhood Level

Abstract Background Emerging evidence suggests that Black and Hispanic communities in the United States are disproportionately affected by coronavirus disease 2019 (COVID-19). A complex interplay of socioeconomic and healthcare disparities likely contribute to disproportionate COVID-19 risk Methods We conducted a geospatial analysis to determine whether individual and neighborhood level attributes predict local odds of testing positive for SARS-CoV-2. We analyzed 29,138 SARS-CoV-2 tests within the 6 -county catchment area for Duke University Health System from March to June 2020. We used generalized additive models to analyze the spatial distribution of SARS-CoV-2 positivity. Adjusted models included individual-level age, gender, and race, as well as neighborhood level ADI, population density, demographic composition, and household size Results Our dataset included 27,099 negative and 2,039 positive unique SARS-CoV-2 tests. The odds of a positive SARS-CoV-2 test were higher for males (OR 1.43, 95% CI 1.30-1.58), Blacks (OR 1.47, 95% CI 1.27-1.70), and Hispanics (OR 4.25, 955 CI 3.55-5.12). Among neighborhood-level predictors, percent Black population (OR 1.14, 95% CI 1.05-1.25) and percent Hispanic population (OR 1.23, 95% CI 1.07-1.41) also influenced the odds of a positive SARS-CoV-2 test. Population density, average household size, and area deprivation index were not associated with SARS-CoV-2 test results after adjusting for race Conclusions The odds of testing positive for SARS-CoV-2 were higher for both Black and Hispanic individuals, as well as within neighborhoods with a higher proportion of Black or Hispanic residents – confirming that Black and Hispanic communities are disproportionately affected by SARS-CoV-2

scholarly journals Spatial and temporal dynamics of Atlantic menhaden (Brevoortia tyrannus) recruitment in the Northwest Atlantic Ocean

2016 ◽  
Vol 73 (4) ◽  
pp. 1147-1159 ◽  
Author(s):  
Andre Buchheister ◽  
Thomas J. Miller ◽  
Edward D. Houde ◽  
David H. Secor ◽  
Robert J. Latour
Keyword(s):  
Time Series ◽  
Temporal Dynamics ◽  
The United States ◽  
Additive Models ◽  
Atlantic Menhaden ◽  
Dynamic Factor ◽  
Spatial And Temporal Variability ◽  
Brevoortia Tyrannus ◽  
Regional Patterns ◽  
Northwest Atlantic

Abstract Atlantic menhaden, Brevoortia tyrannus, is an abundant, schooling pelagic fish that is widely distributed in the coastal Northwest Atlantic. It supports the largest single-species fishery by volume on the east coast of the United States. However, relatively little is known about factors that control recruitment, and its stock–recruitment relationship is poorly defined. Atlantic menhaden is managed as a single unit stock, but fisheries and environmental variables likely act regionally on recruitments. To better understand spatial and temporal variability in recruitment, fishery-independent time-series (1959–2013) of young-of-year (YOY) abundance indices from the Mid-Atlantic to Southern New England (SNE) were analysed using dynamic factor analysis and generalized additive models. Recruitment time-series demonstrated low-frequency variability and the analyses identified two broad geographical groupings, the Chesapeake Bay (CB) and SNE. Each of these two regions exhibited changes in YOY abundance and different periods of relatively high YOY abundance that were inversely related to each other; CB indices were highest from ca. 1971 to 1991, whereas SNE indices were high from ca. 1995 to 2005. We tested for effects of climatic, environmental, biological, and fishing-related variables that have been documented or hypothesized to influence stock productivity. A broad-scale indicator of climate, the Atlantic Multidecadal Oscillation, was the best single predictor of coast-wide recruitment patterns, and had opposing effects on the CB and SNE regions. Underlying mechanisms of spatial and interannual variability in recruitment likely derive from interactions among climatology, larval transport, adult menhaden distribution, and habitat suitability. The identified regional patterns and climatic effects have implications for the stock assessment of Atlantic menhaden, particularly given the geographically constrained nature of the existing fishery and the climatic oscillations characteristic of the coastal ocean.

scholarly journals Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models

2020 ◽  
Vol 17 (17) ◽  
pp. 6339
Author(s):  
François Freddy Ateba ◽  
Manuel Febrero-Bande ◽  
Issaka Sagara ◽  
Nafomon Sogoba ◽  
Mahamoudou Touré ◽  
...  
Keyword(s):  
Wind Speed ◽  
Malaria Incidence ◽  
Generalized Additive Models ◽  
Community Health Center ◽  
Additive Model ◽  
Additive Models ◽  
Functional Regression ◽  
Novel Approach ◽  
Malaria Incidence Rate ◽  
Mean Wind Speed

Mali aims to reach the pre-elimination stage of malaria by the next decade. This study used functional regression models to predict the incidence of malaria as a function of past meteorological patterns to better prevent and to act proactively against impending malaria outbreaks. All data were collected over a five-year period (2012–2017) from 1400 persons who sought treatment at Dangassa’s community health center. Rainfall, temperature, humidity, and wind speed variables were collected. Functional Generalized Spectral Additive Model (FGSAM), Functional Generalized Linear Model (FGLM), and Functional Generalized Kernel Additive Model (FGKAM) were used to predict malaria incidence as a function of the pattern of meteorological indicators over a continuum of the 18 weeks preceding the week of interest. Their respective outcomes were compared in terms of predictive abilities. The results showed that (1) the highest malaria incidence rate occurred in the village 10 to 12 weeks after we observed a pattern of air humidity levels >65%, combined with two or more consecutive rain episodes and a mean wind speed <1.8 m/s; (2) among the three models, the FGLM obtained the best results in terms of prediction; and (3) FGSAM was shown to be a good compromise between FGLM and FGKAM in terms of flexibility and simplicity. The models showed that some meteorological conditions may provide a basis for detection of future outbreaks of malaria. The models developed in this paper are useful for implementing preventive strategies using past meteorological and past malaria incidence.

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