Comparative prevalence of COVID–19 in european countries: a time window at second wave

Introduction: The pandemic generated by COVID–19 completely changed people's daily lives, their relationship with family and friends, unexpectedly disrupted their working conditions and enhanced the need for an enduring resilience to face yet a second wave of the disease. It is crucial to keep continuously updating our knowledge about COVID–19 prevalence and incidence evolutions over large connected territories, where the disease is striking in alarming proportions. Objective: The main objective of this research is to identify and describe COVID–19 prevalence, incidence and mortality profiles in EU and EEE/EFTA countries, seven months after the start of the pandemic in Europe, and more recent tendencies, probably associated to the beginning of a second wave. Methods: This COVID–19 study covers thirty–one European countries. Six epidemiological variables where analyzed per 100 000 inhabitants on October 25 2020, two of them evaluated over the seven previous days. A multivariate statistical exploratory analysis based on rank principal components and cluster analysis was applied. Results: A COVID–19 prevalence typology of six country clusters was identified regarding 31 countries (EU, UK and three EEE/EFTA countries). The five epidemiological variables and number of tests revealed a wider dispersion with outlier observations. The rank transformation of data and their multivariate statistical analysis allowed us to construct a rational to better discriminate and describe these clusters, identifying specific behaviours related to the global prevalence from March until the end of October or highlight recent evolutions of COVID–19 incidence in the context of a second wave of pandemic. In fact we pinpointed country clusters where COVID–19 reached alarming levels which persist, or have even worsen, at the beginning of the second wave. Additionally, two other clusters were identified: one with countries that seems to be evolving into a situation under control, and another cluster of countries very weakly struck on the first wave, but are now facing a very complex surge, that will test their health systems capacity and timely response regarding covid and non–covid patients. Finally, the worst and more dramatic situation occurred in countries where the number of deaths per 100 000 inhabitants attained an impressive cumulative score.

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Examination of the Eating Behavior of the Hungarian Population Based on the TFEQ-R21 Model

Nutrients ◽

10.3390/nu12113514 ◽

2020 ◽

Vol 12 (11) ◽

pp. 3514

Author(s):

Zoltán Szakály ◽

Bence Kovács ◽

Márk Szakály ◽

Dorka T. Nagy-Pető ◽

Tímea Gál ◽

...

Keyword(s):

Eating Behavior ◽

Emotional Eating ◽

Population Based ◽

Eating Attitudes ◽

Multivariate Statistical ◽

Confirmatory Factor Analyses ◽

Reduction Techniques ◽

Three Factor Eating Questionnaire ◽

And Cluster Analysis ◽

Three Factor

Several theories have emerged to study types of eating behavior leading to obesity, but most of the applied models are mainly related to food choice decisions and food consumer behavior. The purpose of this paper was to examine the eating attitudes of Hungarian consumers by applying the Three-Factor Eating Questionnaire (TFEQ-R21). The national representative questionnaire involved 1000 individuals in Hungary in 2019. Several multivariate statistical techniques were applied for the data analysis: exploratory and confirmatory factor analyses, multivariate data reduction techniques, and cluster analysis. This study successfully managed to distinguish the following factors: emotional eating, uncontrolled eating, and cognitive restraint. By using the factors, five clusters were identified: Uncontrolled Emotional Eaters; Overweight, Uncontrolled Eaters; Controlled, Conscious Eaters; the Uninterested; and the Rejecters; all of these could be addressed by public health policy with individually tailored messages. The empirical results led to rejection of the original Three-Factor Eating Questionnaire (TFEQ-R21), while the TFEQ-R16 model could be validated on a representative sample of adults, for the first time in Hungary.

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A versatile web app for identifying the drivers of COVID-19 epidemics

Journal of Translational Medicine ◽

10.1186/s12967-021-02736-2 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Wayne M. Getz ◽

Richard Salter ◽

Ludovica Luisa Vissat ◽

Nir Horvitz

Keyword(s):

Model Fitting ◽

Likelihood Estimation ◽

Stochastic Simulations ◽

Social Distancing ◽

Epidemic Curve ◽

Policies And Measures ◽

Incidence And Mortality ◽

Credible Intervals ◽

Web App ◽

Second Wave

Abstract Background No versatile web app exists that allows epidemiologists and managers around the world to comprehensively analyze the impacts of COVID-19 mitigation. The http://covid-webapp.numerusinc.com/ web app presented here fills this gap. Methods Our web app uses a model that explicitly identifies susceptible, contact, latent, asymptomatic, symptomatic and recovered classes of individuals, and a parallel set of response classes, subject to lower pathogen-contact rates. The user inputs a CSV file of incidence and, if of interest, mortality rate data. A default set of parameters is available that can be overwritten through input or online entry, and a user-selected subset of these can be fitted to the model using maximum-likelihood estimation (MLE). Model fitting and forecasting intervals are specifiable and changes to parameters allow counterfactual and forecasting scenarios. Confidence or credible intervals can be generated using stochastic simulations, based on MLE values, or on an inputted CSV file containing Markov chain Monte Carlo (MCMC) estimates of one or more parameters. Results We illustrate the use of our web app in extracting social distancing, social relaxation, surveillance or virulence switching functions (i.e., time varying drivers) from the incidence and mortality rates of COVID-19 epidemics in Israel, South Africa, and England. The Israeli outbreak exhibits four distinct phases: initial outbreak, social distancing, social relaxation, and a second wave mitigation phase. An MCMC projection of this latter phase suggests the Israeli epidemic will continue to produce into late November an average of around 1500 new case per day, unless the population practices social-relaxation measures at least 5-fold below the level in August, which itself is 4-fold below the level at the start of July. Our analysis of the relatively late South African outbreak that became the world’s fifth largest COVID-19 epidemic in July revealed that the decline through late July and early August was characterised by a social distancing driver operating at more than twice the per-capita applicable-disease-class (pc-adc) rate of the social relaxation driver. Our analysis of the relatively early English outbreak, identified a more than 2-fold improvement in surveillance over the course of the epidemic. It also identified a pc-adc social distancing rate in early August that, though nearly four times the pc-adc social relaxation rate, appeared to barely contain a second wave that would break out if social distancing was further relaxed. Conclusion Our web app provides policy makers and health officers who have no epidemiological modelling or computer coding expertise with an invaluable tool for assessing the impacts of different outbreak mitigation policies and measures. This includes an ability to generate an epidemic-suppression or curve-flattening index that measures the intensity with which behavioural responses suppress or flatten the epidemic curve in the region under consideration.

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Evaluation of the anthropogenic impact in Suat Ugurlu Dam lake using multivariate statistical techniques

Global NEST Journal ◽

10.30955/gnj.002387 ◽

2018 ◽

Vol 20 (1) ◽

pp. 161-168 ◽

Cited By ~ 1

Keyword(s):

Anthropogenic Impact ◽

Sediment Quality ◽

Principal Component ◽

Statistical Techniques ◽

Multivariate Statistical Techniques ◽

Cluster Number ◽

Multivariate Statistical ◽

Oxidation Reduction ◽

Oxidation Reduction Potential ◽

And Cluster Analysis

Sediments play an important role in the quality of aquatic ecosystems in the Dam Lake where they can either be a sink or a source of contaminants, depending on the management. This purpose of this study is to identify the sediment quality in order to find out the causes for the malodor and the eutrophication that is causing a bad scenario. Solutions for improving the dam are proposed. Multivariate statistical techniques, such as a principal component analysis (PCA) and cluster analysis (CA), were applied to the data regarding sediment quality in relation to anthropogenic impact in Suat Ugurlu Dam Lake. This data was generated during 2014-2015, with monitoring at four sites for 11 parameters. A PCA and CA were used in the study of the samples. The total variance of 84.1%, 74.3%, 87.4% and 91.5% suggest 4, 3, 3 and 4 principle components (PCs) in the four locations: LC1, LC2, LC3 and LC4, respectively. Also, a CA was applied to both the variables and the observations. Some variables and observations showed a high similarity based on the results of variables in the CA. Also, the similarity ratio of temperature-mercury (Hg) and oxidation reduction potential (ORP) was high and generally, the cluster number of variables was 5, according to the selected similarity level.

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Incidence and mortality due to cervical cancer in 4 south European countries

Porto Biomedical Journal ◽

10.1016/j.pbj.0000000000000026 ◽

2019 ◽

Vol 4 (1) ◽

pp. e26

Author(s):

Cristina Teixeira ◽

Ana Afonso ◽

Luciana Rodrigues ◽

Muriela Madureira ◽

António Nogueira

Keyword(s):

Cervical Cancer ◽

European Countries ◽

Incidence And Mortality

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DSC, FTIR and Raman Spectroscopy Coupled with Multivariate Analysis in a Study of Co-Crystals of Pharmaceutical Interest

Molecules ◽

10.3390/molecules23092136 ◽

2018 ◽

Vol 23 (9) ◽

pp. 2136 ◽

Cited By ~ 6

Author(s):

Patrycja Garbacz ◽

Marek Wesolowski

Keyword(s):

Principal Component ◽

Water Soluble ◽

Crystal Formation ◽

Multivariate Statistical ◽

Molar Ratios ◽

Pharmaceutical Ingredients ◽

Multivariate Statistical Approach ◽

And Cluster Analysis ◽

Dsc Curves ◽

Physical Mixtures

Co-crystals have garnered increasing interest in recent years as a beneficial approach to improving the solubility of poorly water soluble active pharmaceutical ingredients (APIs). However, their preparation is a challenge that requires a simple approach towards co-crystal detection. The objective of this work was, therefore, to verify to what extent a multivariate statistical approach such as principal component analysis (PCA) and cluster analysis (CA) can be used as a supporting tool for detecting co-crystal formation. As model samples, physical mixtures and co-crystals of indomethacin with saccharin and furosemide with p-aminobenzoic acid were prepared at API/co-former molar ratios 1:1, 2:1 and 1:2. Data acquired from DSC curves and FTIR and Raman spectroscopies were used for CA and PCA calculations. The results obtained revealed that the application of physical mixtures as reference samples allows a deeper insight into co-crystallization than is possible with the use of API and co-former or API and co-former with physical mixtures. Thus, multivariate matrix for PCA and CA calculations consisting of physical mixtures and potential co-crystals could be considered as the most profitable and reliable way to reflect changes in samples after co-crystallization. Moreover, complementary interpretation of results obtained using DSC, FTIR and Raman techniques is most beneficial.

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Queering kinship, overcoming heteronorms

Human Affairs ◽

10.1515/humaff-2020-0003 ◽

2020 ◽

Vol 30 (1) ◽

pp. 27-37

Author(s):

Diego Lasio ◽

João Manuel De Oliveira ◽

Francesco Serri

Keyword(s):

European Countries ◽

Equal Rights ◽

Same Sex ◽

Daily Lives ◽

Same Sex Couples ◽

Lesbian And Gay ◽

Feminist Perspectives ◽

Lgbt Individuals

AbstractAlthough same-sex couples and their offspring have been legitimised in many European countries, heteronormativity is still embedded in institutions and practices, thereby continuing to affect the daily lives of LGBT individuals. Italy represents a clear example of the hegemonic power of heteronormativity because of the fierce opposition to recognising lesbian and gay parenthood among many parts of society. This paper focuses on the peculiarities of the Italian scenario with the aim of highlighting how heteronormativity works in contemporary neoliberal contexts. By drawing on queer and feminist perspectives, the article also analyses how LGBT equal rights demands can contribute, to some extent, to reinforcing heteronormativity. Implications concerning strategies for challenging the regime of normality and queering kinship are discussed.

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Stringent containment measures without complete city lockdown to achieve low incidence and mortality across two waves of COVID-19 in Hong Kong

BMJ Global Health ◽

10.1136/bmjgh-2020-003573 ◽

2020 ◽

Vol 5 (10) ◽

pp. e003573

Author(s):

Martin C S Wong ◽

Rita W Y Ng ◽

Ka Chun Chong ◽

Christopher K C Lai ◽

Junjie Huang ◽

...

Keyword(s):

Hong Kong ◽

Moving Average ◽

Linear Regression Analysis ◽

Leisure Activities ◽

Multiple Linear Regression Analysis ◽

Mitigation Strategies ◽

Incidence And Mortality ◽

Containment Measures ◽

Epidemiological Characteristics ◽

Second Wave

IntroductionAn international city, Hong Kong, in proximity to the first epicentre of COVID- 19, experienced two epidemic waves with different importation pressure. We compared the epidemiological features of patients with COVID-19 in the context of containment policies between the first and second waves.MethodsWe retrieved information on the first 1038 cases detected in Hong Kong (23 January to 25 April 2020) to analyse the epidemiological characteristics including age/gender-specific incidence, clustering, reproduction number (Rt) and containment delay; in relation to the containment measures implemented. Factors associated with containment delay were evaluated by multiple linear regression analysis with age, gender, epidemic wave and infection source as covariates. A time series of 5-day moving average was plotted to examine the changes across the two epidemic waves.ResultsThe incidence and mortality (135.5 and 0.5 per 1 000 000 population) was among the lowest in the world. Aggressive escalation of border control correlated with reductions in Rt from 1.35 to 0.57 and 0.92 to 0.18, and aversions of 450 and 1650 local infections during the first and second waves, respectively. Implementing COVID-19 tests for overseas returners correlated with an upsurge of asymptomatic case detection, and shortened containment delay in the second wave. Medium-sized cluster events in the first wave were family gatherings, whereas those in the second wave were leisure activities among youngsters. Containment delay was associated with older age (adjusted OR (AOR)=1.01, 95% CI 1.00 to 1.02, p=0.040), male gender (AOR=1.41, 95% CI 1.02 to 1.96, p=0.039) and local cases (AOR=11.18, 95% CI 7.43 to 16.83, p<0.001), and with significant improvement in the second wave compared with the first wave (average: 6.8 vs 3.7 days). A higher incidence rate was observed for males, raising possibility of gender predilection in susceptibility of developing symptoms.ConclusionPrompt and stringent all-round containment strategies represent successful measures in pandemic control. These findings could inform formulation and implementation of pandemic mitigation strategies.

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Screening for Early Gastric Cancer Using a Noninvasive Urine Metabolomics Approach

Cancers ◽

10.3390/cancers12102904 ◽

2020 ◽

Vol 12 (10) ◽

pp. 2904

Author(s):

Hyuk Nam Kwon ◽

Hyuk Lee ◽

Ji Won Park ◽

Young-Ho Kim ◽

Sunghyouk Park ◽

...

Keyword(s):

Gastric Cancer ◽

Early Detection ◽

Performance Test ◽

Screening Method ◽

Endoscopic Examination ◽

Multivariate Statistical ◽

Metabolic Markers ◽

Intramucosal Cancer ◽

Incidence And Mortality ◽

Urine Metabolomics

The early detection of gastric cancer (GC) could decrease its incidence and mortality. However, there are currently no accurate noninvasive markers for GC screening. Therefore, we developed a noninvasive diagnostic approach, employing urine nuclear magnetic resonance (NMR) metabolomics, to discover putative metabolic markers associated with GC. Changes in urine metabolite levels during oncogenesis were evaluated using samples from 103 patients with GC and 100 age- and sex-matched healthy controls. Approximately 70% of the patients with GC (n = 69) had stage I GC, with the majority (n = 56) having intramucosal cancer. A multivariate statistical analysis of the urine NMR data well discriminated between the patient and control groups and revealed nine metabolites, including alanine, citrate, creatine, creatinine, glycerol, hippurate, phenylalanine, taurine, and 3-hydroxybutyrate, that contributed to the difference. A diagnostic performance test with a separate validation set exhibited a sensitivity and specificity of more than 90%, even with the intramucosal cancer samples only. In conclusion, the NMR-based urine metabolomics approach may have potential as a convenient screening method for the early detection of GC and may facilitate consequent endoscopic examination through risk stratification.

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Student Employees and Recreational Sports Administrators: A Comparison of Perceptions

Recreational Sports Journal ◽

10.1123/rsj.30.1.53 ◽

2006 ◽

Vol 30 (1) ◽

pp. 53-69 ◽

Cited By ~ 4

Author(s):

Gary L. Miller ◽

Thomas E. Grayson

Keyword(s):

Recreational Sports ◽

Multivariate Statistical ◽

Big Ten ◽

Work Tasks ◽

Multivariate Statistical Approach ◽

Student Employees ◽

The Mean ◽

And Cluster Analysis ◽

The Individual ◽

Ladder Graphs

This study evaluates the differences in perceptions between student employees and recreational sports administrators over a consistent set of work tasks and responsibilities typically done by student employees in a recreational sports setting. The focus of the study was to provide a method of improving the effectiveness and efficiency by which recreational sports programs deliver their services and programs. Nine of the 11 schools in the Big Ten Conference participated in the study with a total of eighty-five participants taking part. Concept mapping, a multivariate statistical approach using multidimensional scaling and cluster analysis was used to analyze the data. Ninety-five work tasks were sorted for similarity and rated on scales for importance toward attaining recreational sports goals and frequency of performance. Cluster maps, ladder graphs and go-to-zones were developed from the data defining the results of the analysis. Results were presented in a composite form for the nine schools participating in the study with the intent to provide comparison between individual schools and the conference composite as requested. Cluster maps illustrated the levels of importance among the six clusters, ladder graphs demonstrated the differences between the student employees and the recreational sports administrators and go-to zones broke out the individual tasks into areas of alignment, gap zones where either importance or frequency were below the mean, and a “?” zone where neither importance nor frequency rose to the mean rating on that scale. The results allow administrators now to compare, examine, and make decisions based each of the 95 work tasks in a guided manner.

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ICP–MS Analysis of Multi-Elemental Profile of Greek Wines and Their Classification According to Variety, Area and Year of Production

Separations ◽

10.3390/separations8080119 ◽

2021 ◽

Vol 8 (8) ◽

pp. 119

Author(s):

Konstantina Pasvanka ◽

Marios Kostakis ◽

Maria Tarapoulouzi ◽

Pavlos Nisianakis ◽

Nikolaos S. Thomaidis ◽

...

Keyword(s):

Inductively Coupled Plasma ◽

Mineral Content ◽

Statistical Investigation ◽

Multivariate Statistical ◽

Annual Fluctuation ◽

Inductively Coupled ◽

Elemental Profile ◽

Icp Ms ◽

Plasma Mass Spectrometry ◽

And Cluster Analysis

Major, minor and trace elements in wines from Greece were determined by inductively coupled plasma–mass spectrometry (ICP–MS). The concentrations of 44 elements (Na, Mg, P, K, Ca, Cu, Co, Cr, Zn, Sn, Fe, Mn, Li, Be, B, V, Sr, Ba, Al, Ag, Ni, As, Sn, Hg, Pb, Sb, Cd, Ti, Ga, Zr, Nb, Pd, Te, La, Sm, Ho, Tm, Yb, W, Os, Au, Tl, Th, U) in 90 white and red wines from six different regions in Greece for two consecutive vinification years, 2017 and 2018, were determined. Results for the elements aforementioned were evaluated by multivariate statistical methods, such as discriminant analysis and cluster analysis, and the wines were discriminated according to wine variety and geographical origin. Due to the specific choice of the analytes for multivariate statistical investigation, a prediction rate by cross-validation of 98% could be achieved. The aim of this study was not only to reveal specific relationships between the wine samples or between the chemical variables in order to classify the wines from different regions and varieties according to their elemental profile (wine authentication), but also to observe the annual fluctuation in the mineral content of the studied wine samples.

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