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  • Publications
  • Research software
  • 2013-2022
  • FR
  • Hyper Article en Ligne
  • COVID-19
  • Digital Humanities and Cultural Heritage

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  • Open Access English
    Authors: 
    Romy Sauvayre; Jessica Vernier; Cédric Chauvière;
    Publisher: HAL CCSD
    Country: France

    As the pandemic progressed, disinformation, fake news and conspiracy spread through many parts of society. However, the disinformation spreading through social media is, according to the literature, one of the causes of increased COVID-19 vaccine hesitancy. In this context, the analysis of social media is particularly important, but the large amount of data exchanged on social networks requires specific methods. This is why machine learning and natural language processing (NLP) models are increasingly applied to social media data.Objective: The aim of this study is to examine the capability of the CamemBERT French language model to faithfully predict elaborated categories, with the knowledge that tweets about vaccination are often ambiguous, sarcastic or irrelevant to the studied topic.Methods: A total of 901,908 unique French tweets related to vaccination published between July 12, 2021, and August 11, 2021, were extracted using the Twitter API v2. Approximately 2,000 randomly selected tweets were labeled with two types of categorization: (1) arguments for ("pros") or against ("cons") vaccination (sanitary measures included) and (2) the type of content of tweets ("scientific", "political", "social", or "vaccination status"). The CamemBERT model was fine-tuned and tested for the classification of French tweets. The model performance was assessed by computing the F1-score, and confusion matrices were obtained. Results: The accuracy of the applied machine learning reached up to 70.6% for the first classification ("pros" and "cons" tweets) and up to 90.0% for the second classification ("scientific" and "political" tweets). Furthermore, a tweet was 1.86 times more likely to be incorrectly classified by the model if it contained fewer than 170 characters (odds ratio = 1.86; 1.20 < 95% confidence interval < 2.86).Conclusions: The accuracy is affected by the classification chosen and the topic of the message examined. When the vaccine debate is jostled by contested political decisions, tweet content becomes so heterogeneous that the accuracy of the models drops for less differentiated classes. However, our tests showed that it is possible to improve the accuracy of the model by selecting tweets using a new method based on tweet size. Les réseaux sociaux participent activement à la diffusion de la désinformation sur la covid-19 et, selon de nombreuses études, auraient causés l’augmentation de la réticence vis-à-vis des vaccins anti-covid. Dans ce contexte, l’analyse des réseaux sociaux est des plus importants en matière de santé publique. Toutefois, au regard du grand volume de données échangées chaque jour par les internautes, elle nécessite des méthodes spécifiques. C’est pourquoi les chercheurs ont de plus en plus souvent recours aux modèles d’apprentissage automatisé et au traitement du langage naturel (NLP) en particulier. L’objectif de la présente étude est d’examiner la capacité du modèle CamemBERT, pré-entraîné sur la langue française, à catégoriser automatiquement les tweets traitant de la vaccination alors qu’ils sont souvent ambigus, sarcastique ou sans rapport avec le sujet.Les résultats obtenus, sur 2 000 tweets francophones, montrent que la précision de l'apprentissage automatique atteint jusqu'à 70,6 % pour la première classification (tweets « pour » et « contre ») et jusqu'à 90,0 % pour la seconde (tweets « scientifiques » et « politiques »). De plus, un tweet a 1,86 fois plus de chances d'être mal classé par le modèle s'il contient moins de 170 caractères que s'il en contient plus de 170 (odd ratio = 1,86 ; 1,20 < intervalle de confiance à 95 % < 2,86).En conclusion, la précision du modèle est affectée par la classification choisie et le sujet du message examiné. Lorsque le débat sur le vaccin est bousculé par des décisions politiques contestées, les tweets deviennent si hétérogènes que la précision des modèles chute sur les classes les moins différenciantes. Toutefois, nos tests ont également montré qu'il serait possible d'améliorer la précision du modèle en sélectionnant les tweets à l'aide d'une nouvelle méthode basée sur la taille des tweets.

  • French
    Authors: 
    Le Coadic, Yves-François;
    Publisher: HAL CCSD
    Country: France

    Face à la pandémie du COVID-19, la science de l’information se trouve confrontée à un certain nombre de nouvelles problématiques, liées aux crises d’information, auxquelles elle n’était pas préparée. Qui dit crise mondiale, crise économique, crise énergétique, changement climatique, etc... dit crise d’information, crise caractérisée par une augmentation drastique de la quantité d’informations, par une épidémie d’information i.e. une infodémie et par de nouvelles et nombreuses mauvaises conduites des pratiques informationnelles. La crise sanitaire liée à la pandémie du COVID-19 n’échappe pas à la règle: infodémie de bonnes informations médicales et sanitaires mais aussi infodémie de mauvaises informations, bonnes pratiques informationnelles mais aussi nombreuses inconduites. Habituée au registre de la vérité scientifique, la science de l’information doit affronter le registre de la post-vérité et du complotisme qui semble caractériser nos sociétés actuelles. Information-soupçon, information-dénonciation, information- mensonge, c’est, pour la science de l’information, un nouvel horizon guère réjouissant qu’elle devrait pouvoir maîtriser sans problème en mobilisant son arsenal de concepts, de méthodes, de lois, de modèles et de théories bien établis en conservant et en développant l’horizon réjouissant de l’information-vérité. In the face of the COVID-19 pandemic, information science is facing a number of new challenges related to information crises for which it was not prepared. Who says global crisis, economic crisis, energy crisis, climate change, etc ... says information crisis, crisis characterized by a drastic increase in the amount of information, by an information epidemic ie an infodemic and by news and numerous misconduct of information practices. The health crisis linked to the COVID-19 pandemic is no exception to the rule: infodemic of good medical and health information but also infodemic of bad information, good informational practices but also numerous misconduct. Accustomed to the register of scientific truth, information science must confront the register of post-truth and conspiracy that seems to characterize our current societies. Information-suspicion, information-denunciation, information-lies, it is, for the science of information, a hardly encouraging new horizon that it should be able to master without problem by mobilizing its arsenal of concepts, methods, laws, of well- established models and theories by preserving and developing the joyous horizon of information-truth.

  • Open Access English
    Authors: 
    Ugofilippo Basellini; Carlo Giovanni Camarda;
    Publisher: HAL CCSD
    Country: France

    Italy was hit harshly by the Covid-19 pandemic, registering more than 35,000 Covid-19 deaths between February and July 2020. During this first wave of the epidemic, the virus spread unequally across the country, with northern regions witnessing more cases and deaths. We investigate demographic and socio-economic factors contributing to the diverse regional impact of the virus during the first wave. Using generalized additive mixed models, we find that Covid-19 mortality at regional level is negatively associated with the degree of intergenerational co-residence, number of intensive care unit beds per capita, and delay in the outbreak of the epidemic. Conversely, we do not find strong associations for several variables highlighted in recent literature, such as population density or the share of the population who are older or have at least one chronic disease. Our results underscore the importance of context-specific analysis for the study of a pandemic.

  • Open Access English
    Authors: 
    Andrew E. Clark; Conchita D'Ambrosio; Ilke Onur; Rong Zhu;
    Publisher: HAL CCSD
    Countries: France, Australia
    Project: EC | SERISS (654221), EC | SSHOC (823782), EC | SHARE-COHESION (870628), EC | SHARE-DEV3 (676536)

    This paper examines the empirical relationship between individuals’ cognitive and non-cognitive abilities and COVID-19 compliance behaviors using cross-country data from the Survey of Health, Ageing and Retirement in Europe (SHARE). We find that both cognitive and non-cognitive skills predict responsible health behaviors during the COVID-19 crisis. Episodic memory is the most important cognitive skill, while conscientiousness and neuroticism are the most significant personality traits. There is also some evidence of a role for an internal locus of control in compliance. usc Refereed/Peer-reviewed

  • Publication . Article . 2021
    Open Access
    Authors: 
    Roger Frutos; Olivier Pliez; Laurent Gavotte; Christian Devaux;
    Publisher: Elsevier BV
    Country: France

    International audience; Since the beginning of the COVID-19 pandemic in 2020 caused by SARS-CoV-2, the question of the origin of this virus has been a highly debated issue. Debates have been, and are still, very disputed and often violent between the two main hypotheses: a natural origin through the “spillover” model or a laboratory-leak origin. Tenants of these two options are building arguments often based on the discrepancies of the other theory. The main problem is that it is the initial question of the origin itself which is biased. Charles Darwin demonstrated in 1859 that all species are appearing through a process of evolution, adaptation and selection. There is no determined origin to any animal or plant species, simply an evolutionary and selective process in which chance and environment play a key role. The very same is true for viruses. There is no determined origin to viruses, simply also an evolutionary and selective process in which chance and environment play a key role. However, in the case of viruses the process is slightly more complex because the “environment” is another living organism. Pandemic viruses already circulate in humans prior to the emergence of a disease. They are simply not capable of triggering an epidemic yet. They must evolve in-host, i.e. in-humans, for that. The evolutionary process which gave rise to SARS-CoV-2 is still ongoing with regular emergence of novel variants more adapted than the previous ones. The real relevant question is how these viruses can emerge as pandemic viruses and what the society can do to prevent the future emergence of pandemic viruses.

  • Open Access English
    Authors: 
    Konstantin Mochalov; Pavel Samokhvalov; Galina Nifontova; T. Tsoi; Alyona Sukhanova; Igor Nabiev;
    Publisher: HAL CCSD
    Country: France

    Abstract Fast, sensitive, high-throughput detection of coronavirus antigens at physiologically relevant levels is essential for population screening that could prevent epidemics such as the current COVID-19 global pandemic. Optical methods based on surface-enhanced Raman scattering (SERS) spectroscopy are promising for this purpose because they ensure quick detection of even single biological molecules in a sample. For achieving such a high sensitivity, it is crucial to design SERS-active systems concentrating incident radiation into small sample volumes. Here, metal-dielectric cavities have been obtained through interaction of protein sulfhydryl groups with a SERS-active silver surface. The concentration of light in these cavities allows the differential detection of spike glycoprotein and nucleocapsid protein of SARS-COV-2, which are its key antigens, at physiologically relevant concentrations. The cavity Q-factor can be increased by additionally covering the dielectric protein film with a silver shell to form an ultrathin cavity, which provides an at least tenfold enhancement of the detection signal. The results could be used to design high-throughput systems for specific and sensitive detection of viral antigens and quick diagnosis of viral infections.

  • Open Access
    Authors: 
    Elizabeth Wrigley-Field; Mathew V. Kiang; Alicia R Riley; Magali Barbieri; Yea-Hung Chen; Kate A. Duchowny; Ellicott C. Matthay; David Van Riper; Kirrthana Jegathesan; Kirsten Bibbins-Domingo; +1 more
    Publisher: eScholarship, University of California
    Countries: United States, France

    COVID-19 mortality increases markedly with age and is also substantially higher among Black, Indigenous, and People of Color (BIPOC) populations in the United States. These two facts can have conflicting implications because BIPOC populations are younger than white populations. In analyses of California and Minnesota—demographically divergent states—we show that COVID vaccination schedules based solely on age benefit the older white populations at the expense of younger BIPOC populations with higher risk of death from COVID-19. We find that strategies that prioritize high-risk geographic areas for vaccination at all ages better target mortality risk than age-based strategies alone, although they do not always perform as well as direct prioritization of high-risk racial/ethnic groups. Vaccination schemas directly implicate equitability of access, both domestically and globally. Age-based COVID-19 vaccination prioritizes white people above higher-risk others; geographic prioritization improves equity. Description

  • Open Access English
    Authors: 
    Adrien Sedeaud; Adrien Sedeaud; Quentin De Larochelambert; Quentin De Larochelambert; Julien Schipman; Julien Schipman; Jean-Francois Toussaint; Jean-Francois Toussaint; Jean-Francois Toussaint;
    Publisher: HAL CCSD

    Objective: To measure the impact of restrictions due to COVID on the proportion of matches won at home, away and draw in professional soccer and rugby union.Materials and Methods: Two samples of professional soccer and rugby union matches were collected from 2012–13 to 2020–21 seasons. For soccer, data involved first and second division matches of the England, Spain, Germany, Italy, France, Belgium, Scotland, Greece, Portugal, and Turkey championships. For rugby union, championships concerned are Premiership Rugby, Celtic League, Top 14, and Pro D2. The proportions of home, away wins and draw were calculated and compared. A chi-square test of independence between years and types of result was realized to identify an overall inhomogeneity.Results: The proportion of away matches won between the 2012–13 and 2020–21 seasons increased significantly from 28.5 ± 1.2% to 32.5 ± 1.5% in soccer and from 38.0 ± 3.6% to 42.8 ± 5.0% in rugby union. In Premiership Rugby championship, the victory percentage at home dropped from 55.8 ± 3.1% when tifosi were present to 45.8 ± 12.8% when they were not.Conclusion: The home advantage was drastically reduced in empty stadiums for several European soccer and rugby union professional championships. It vanished in the Premiership Rugby and Celtic League during the 2020–21 season.

  • Publication . Article . Other literature type . Conference object . 2021
    Open Access English
    Authors: 
    Wissame Laddada; Lina Fatima Soualmia; Cecilia Zanni-Merk; Ali Ayadi; Claudia Frydman; India L'Hote; Isabelle Imbert;
    Publisher: HAL CCSD
    Country: France

    International audience; Understanding the replication machinery of viruses contributes to suggest and try effective antiviral strategies. Exhaustive knowledge about the proteins structure, their function, or their interaction is one of the preconditions for successfully modeling it. In this context, modeling methods based on a formal representation with a high semantic expressiveness would be relevant to extract proteins and their nucleotide or amino acid sequences as an element from the replication process. Consequently, our approach relies on the use of semantic technologies to design the SARS-CoV-2 replication machinery. This provides the ability to infer new knowledge related to each step of the virus replication. More specifically, we developed an ontology-based approach enriched with reasoning process of a complete replication machinery process for SARS-CoV-2. We present in this paper a partial overview of our ontology OntoRepliCov to describe one step of this process, namely, the continuous translation or protein synthesis, through classes, properties, axioms, and SWRL (Semantic Web Rule Language) rules.

  • Open Access
    Authors: 
    Karin E. Limburg; Françoise Daverat;
    Publisher: Wiley
    Country: France

    Abstract The global lockdowns brought on by the COVID-19 pandemic forced an immediate change in the way people moved about;namely, travel was slowed from a turbulent river to a trickle In-person meetings, often involving long-distance flights, were either canceled, postponed, or shifted over to virtual modes People who were unfamiliar with online meetings quickly became acquainted with them

Advanced search in
Research products
arrow_drop_down
Searching FieldsTerms
Any field
arrow_drop_down
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arrow_drop_down
Include:
51 Research products, page 1 of 6
  • Open Access English
    Authors: 
    Romy Sauvayre; Jessica Vernier; Cédric Chauvière;
    Publisher: HAL CCSD
    Country: France

    As the pandemic progressed, disinformation, fake news and conspiracy spread through many parts of society. However, the disinformation spreading through social media is, according to the literature, one of the causes of increased COVID-19 vaccine hesitancy. In this context, the analysis of social media is particularly important, but the large amount of data exchanged on social networks requires specific methods. This is why machine learning and natural language processing (NLP) models are increasingly applied to social media data.Objective: The aim of this study is to examine the capability of the CamemBERT French language model to faithfully predict elaborated categories, with the knowledge that tweets about vaccination are often ambiguous, sarcastic or irrelevant to the studied topic.Methods: A total of 901,908 unique French tweets related to vaccination published between July 12, 2021, and August 11, 2021, were extracted using the Twitter API v2. Approximately 2,000 randomly selected tweets were labeled with two types of categorization: (1) arguments for ("pros") or against ("cons") vaccination (sanitary measures included) and (2) the type of content of tweets ("scientific", "political", "social", or "vaccination status"). The CamemBERT model was fine-tuned and tested for the classification of French tweets. The model performance was assessed by computing the F1-score, and confusion matrices were obtained. Results: The accuracy of the applied machine learning reached up to 70.6% for the first classification ("pros" and "cons" tweets) and up to 90.0% for the second classification ("scientific" and "political" tweets). Furthermore, a tweet was 1.86 times more likely to be incorrectly classified by the model if it contained fewer than 170 characters (odds ratio = 1.86; 1.20 < 95% confidence interval < 2.86).Conclusions: The accuracy is affected by the classification chosen and the topic of the message examined. When the vaccine debate is jostled by contested political decisions, tweet content becomes so heterogeneous that the accuracy of the models drops for less differentiated classes. However, our tests showed that it is possible to improve the accuracy of the model by selecting tweets using a new method based on tweet size. Les réseaux sociaux participent activement à la diffusion de la désinformation sur la covid-19 et, selon de nombreuses études, auraient causés l’augmentation de la réticence vis-à-vis des vaccins anti-covid. Dans ce contexte, l’analyse des réseaux sociaux est des plus importants en matière de santé publique. Toutefois, au regard du grand volume de données échangées chaque jour par les internautes, elle nécessite des méthodes spécifiques. C’est pourquoi les chercheurs ont de plus en plus souvent recours aux modèles d’apprentissage automatisé et au traitement du langage naturel (NLP) en particulier. L’objectif de la présente étude est d’examiner la capacité du modèle CamemBERT, pré-entraîné sur la langue française, à catégoriser automatiquement les tweets traitant de la vaccination alors qu’ils sont souvent ambigus, sarcastique ou sans rapport avec le sujet.Les résultats obtenus, sur 2 000 tweets francophones, montrent que la précision de l'apprentissage automatique atteint jusqu'à 70,6 % pour la première classification (tweets « pour » et « contre ») et jusqu'à 90,0 % pour la seconde (tweets « scientifiques » et « politiques »). De plus, un tweet a 1,86 fois plus de chances d'être mal classé par le modèle s'il contient moins de 170 caractères que s'il en contient plus de 170 (odd ratio = 1,86 ; 1,20 < intervalle de confiance à 95 % < 2,86).En conclusion, la précision du modèle est affectée par la classification choisie et le sujet du message examiné. Lorsque le débat sur le vaccin est bousculé par des décisions politiques contestées, les tweets deviennent si hétérogènes que la précision des modèles chute sur les classes les moins différenciantes. Toutefois, nos tests ont également montré qu'il serait possible d'améliorer la précision du modèle en sélectionnant les tweets à l'aide d'une nouvelle méthode basée sur la taille des tweets.

  • French
    Authors: 
    Le Coadic, Yves-François;
    Publisher: HAL CCSD
    Country: France

    Face à la pandémie du COVID-19, la science de l’information se trouve confrontée à un certain nombre de nouvelles problématiques, liées aux crises d’information, auxquelles elle n’était pas préparée. Qui dit crise mondiale, crise économique, crise énergétique, changement climatique, etc... dit crise d’information, crise caractérisée par une augmentation drastique de la quantité d’informations, par une épidémie d’information i.e. une infodémie et par de nouvelles et nombreuses mauvaises conduites des pratiques informationnelles. La crise sanitaire liée à la pandémie du COVID-19 n’échappe pas à la règle: infodémie de bonnes informations médicales et sanitaires mais aussi infodémie de mauvaises informations, bonnes pratiques informationnelles mais aussi nombreuses inconduites. Habituée au registre de la vérité scientifique, la science de l’information doit affronter le registre de la post-vérité et du complotisme qui semble caractériser nos sociétés actuelles. Information-soupçon, information-dénonciation, information- mensonge, c’est, pour la science de l’information, un nouvel horizon guère réjouissant qu’elle devrait pouvoir maîtriser sans problème en mobilisant son arsenal de concepts, de méthodes, de lois, de modèles et de théories bien établis en conservant et en développant l’horizon réjouissant de l’information-vérité. In the face of the COVID-19 pandemic, information science is facing a number of new challenges related to information crises for which it was not prepared. Who says global crisis, economic crisis, energy crisis, climate change, etc ... says information crisis, crisis characterized by a drastic increase in the amount of information, by an information epidemic ie an infodemic and by news and numerous misconduct of information practices. The health crisis linked to the COVID-19 pandemic is no exception to the rule: infodemic of good medical and health information but also infodemic of bad information, good informational practices but also numerous misconduct. Accustomed to the register of scientific truth, information science must confront the register of post-truth and conspiracy that seems to characterize our current societies. Information-suspicion, information-denunciation, information-lies, it is, for the science of information, a hardly encouraging new horizon that it should be able to master without problem by mobilizing its arsenal of concepts, methods, laws, of well- established models and theories by preserving and developing the joyous horizon of information-truth.

  • Open Access English
    Authors: 
    Ugofilippo Basellini; Carlo Giovanni Camarda;
    Publisher: HAL CCSD
    Country: France

    Italy was hit harshly by the Covid-19 pandemic, registering more than 35,000 Covid-19 deaths between February and July 2020. During this first wave of the epidemic, the virus spread unequally across the country, with northern regions witnessing more cases and deaths. We investigate demographic and socio-economic factors contributing to the diverse regional impact of the virus during the first wave. Using generalized additive mixed models, we find that Covid-19 mortality at regional level is negatively associated with the degree of intergenerational co-residence, number of intensive care unit beds per capita, and delay in the outbreak of the epidemic. Conversely, we do not find strong associations for several variables highlighted in recent literature, such as population density or the share of the population who are older or have at least one chronic disease. Our results underscore the importance of context-specific analysis for the study of a pandemic.

  • Open Access English
    Authors: 
    Andrew E. Clark; Conchita D'Ambrosio; Ilke Onur; Rong Zhu;
    Publisher: HAL CCSD
    Countries: France, Australia
    Project: EC | SERISS (654221), EC | SSHOC (823782), EC | SHARE-COHESION (870628), EC | SHARE-DEV3 (676536)

    This paper examines the empirical relationship between individuals’ cognitive and non-cognitive abilities and COVID-19 compliance behaviors using cross-country data from the Survey of Health, Ageing and Retirement in Europe (SHARE). We find that both cognitive and non-cognitive skills predict responsible health behaviors during the COVID-19 crisis. Episodic memory is the most important cognitive skill, while conscientiousness and neuroticism are the most significant personality traits. There is also some evidence of a role for an internal locus of control in compliance. usc Refereed/Peer-reviewed

  • Publication . Article . 2021
    Open Access
    Authors: 
    Roger Frutos; Olivier Pliez; Laurent Gavotte; Christian Devaux;
    Publisher: Elsevier BV
    Country: France

    International audience; Since the beginning of the COVID-19 pandemic in 2020 caused by SARS-CoV-2, the question of the origin of this virus has been a highly debated issue. Debates have been, and are still, very disputed and often violent between the two main hypotheses: a natural origin through the “spillover” model or a laboratory-leak origin. Tenants of these two options are building arguments often based on the discrepancies of the other theory. The main problem is that it is the initial question of the origin itself which is biased. Charles Darwin demonstrated in 1859 that all species are appearing through a process of evolution, adaptation and selection. There is no determined origin to any animal or plant species, simply an evolutionary and selective process in which chance and environment play a key role. The very same is true for viruses. There is no determined origin to viruses, simply also an evolutionary and selective process in which chance and environment play a key role. However, in the case of viruses the process is slightly more complex because the “environment” is another living organism. Pandemic viruses already circulate in humans prior to the emergence of a disease. They are simply not capable of triggering an epidemic yet. They must evolve in-host, i.e. in-humans, for that. The evolutionary process which gave rise to SARS-CoV-2 is still ongoing with regular emergence of novel variants more adapted than the previous ones. The real relevant question is how these viruses can emerge as pandemic viruses and what the society can do to prevent the future emergence of pandemic viruses.

  • Open Access English
    Authors: 
    Konstantin Mochalov; Pavel Samokhvalov; Galina Nifontova; T. Tsoi; Alyona Sukhanova; Igor Nabiev;
    Publisher: HAL CCSD
    Country: France

    Abstract Fast, sensitive, high-throughput detection of coronavirus antigens at physiologically relevant levels is essential for population screening that could prevent epidemics such as the current COVID-19 global pandemic. Optical methods based on surface-enhanced Raman scattering (SERS) spectroscopy are promising for this purpose because they ensure quick detection of even single biological molecules in a sample. For achieving such a high sensitivity, it is crucial to design SERS-active systems concentrating incident radiation into small sample volumes. Here, metal-dielectric cavities have been obtained through interaction of protein sulfhydryl groups with a SERS-active silver surface. The concentration of light in these cavities allows the differential detection of spike glycoprotein and nucleocapsid protein of SARS-COV-2, which are its key antigens, at physiologically relevant concentrations. The cavity Q-factor can be increased by additionally covering the dielectric protein film with a silver shell to form an ultrathin cavity, which provides an at least tenfold enhancement of the detection signal. The results could be used to design high-throughput systems for specific and sensitive detection of viral antigens and quick diagnosis of viral infections.

  • Open Access
    Authors: 
    Elizabeth Wrigley-Field; Mathew V. Kiang; Alicia R Riley; Magali Barbieri; Yea-Hung Chen; Kate A. Duchowny; Ellicott C. Matthay; David Van Riper; Kirrthana Jegathesan; Kirsten Bibbins-Domingo; +1 more
    Publisher: eScholarship, University of California
    Countries: United States, France

    COVID-19 mortality increases markedly with age and is also substantially higher among Black, Indigenous, and People of Color (BIPOC) populations in the United States. These two facts can have conflicting implications because BIPOC populations are younger than white populations. In analyses of California and Minnesota—demographically divergent states—we show that COVID vaccination schedules based solely on age benefit the older white populations at the expense of younger BIPOC populations with higher risk of death from COVID-19. We find that strategies that prioritize high-risk geographic areas for vaccination at all ages better target mortality risk than age-based strategies alone, although they do not always perform as well as direct prioritization of high-risk racial/ethnic groups. Vaccination schemas directly implicate equitability of access, both domestically and globally. Age-based COVID-19 vaccination prioritizes white people above higher-risk others; geographic prioritization improves equity. Description

  • Open Access English
    Authors: 
    Adrien Sedeaud; Adrien Sedeaud; Quentin De Larochelambert; Quentin De Larochelambert; Julien Schipman; Julien Schipman; Jean-Francois Toussaint; Jean-Francois Toussaint; Jean-Francois Toussaint;
    Publisher: HAL CCSD

    Objective: To measure the impact of restrictions due to COVID on the proportion of matches won at home, away and draw in professional soccer and rugby union.Materials and Methods: Two samples of professional soccer and rugby union matches were collected from 2012–13 to 2020–21 seasons. For soccer, data involved first and second division matches of the England, Spain, Germany, Italy, France, Belgium, Scotland, Greece, Portugal, and Turkey championships. For rugby union, championships concerned are Premiership Rugby, Celtic League, Top 14, and Pro D2. The proportions of home, away wins and draw were calculated and compared. A chi-square test of independence between years and types of result was realized to identify an overall inhomogeneity.Results: The proportion of away matches won between the 2012–13 and 2020–21 seasons increased significantly from 28.5 ± 1.2% to 32.5 ± 1.5% in soccer and from 38.0 ± 3.6% to 42.8 ± 5.0% in rugby union. In Premiership Rugby championship, the victory percentage at home dropped from 55.8 ± 3.1% when tifosi were present to 45.8 ± 12.8% when they were not.Conclusion: The home advantage was drastically reduced in empty stadiums for several European soccer and rugby union professional championships. It vanished in the Premiership Rugby and Celtic League during the 2020–21 season.

  • Publication . Article . Other literature type . Conference object . 2021
    Open Access English
    Authors: 
    Wissame Laddada; Lina Fatima Soualmia; Cecilia Zanni-Merk; Ali Ayadi; Claudia Frydman; India L'Hote; Isabelle Imbert;
    Publisher: HAL CCSD
    Country: France

    International audience; Understanding the replication machinery of viruses contributes to suggest and try effective antiviral strategies. Exhaustive knowledge about the proteins structure, their function, or their interaction is one of the preconditions for successfully modeling it. In this context, modeling methods based on a formal representation with a high semantic expressiveness would be relevant to extract proteins and their nucleotide or amino acid sequences as an element from the replication process. Consequently, our approach relies on the use of semantic technologies to design the SARS-CoV-2 replication machinery. This provides the ability to infer new knowledge related to each step of the virus replication. More specifically, we developed an ontology-based approach enriched with reasoning process of a complete replication machinery process for SARS-CoV-2. We present in this paper a partial overview of our ontology OntoRepliCov to describe one step of this process, namely, the continuous translation or protein synthesis, through classes, properties, axioms, and SWRL (Semantic Web Rule Language) rules.

  • Open Access
    Authors: 
    Karin E. Limburg; Françoise Daverat;
    Publisher: Wiley
    Country: France

    Abstract The global lockdowns brought on by the COVID-19 pandemic forced an immediate change in the way people moved about;namely, travel was slowed from a turbulent river to a trickle In-person meetings, often involving long-distance flights, were either canceled, postponed, or shifted over to virtual modes People who were unfamiliar with online meetings quickly became acquainted with them

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