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  • Publications
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  • 2018-2022
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  • Mémoires en Sciences de l'Information et de la Communication
  • Hyper Article en Ligne - Sciences de l'Homme et de la Société
  • 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.

  • 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

  • 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 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 English
    Authors: 
    F.G. Katz Brian; David Poirier-Quinot; Jean-Marc Lyzwa;
    Publisher: HAL CCSD
    Country: France

    International audience; In honour of the International Year of Sound and for the 1 year memorial of the Notre-Dame cathedral fire, a team of researchers and sound engineers created a virtual reconstruction of a concert in the cathedral, using close-mic recordings made on 24-April-2013 of a performance of La Vierge by Jules Massenet. This reconstruction was carried out during the period of strict COVID-19 confinement. With 83 musicians, 6 singers and a 160person choir spatially distributed throughout the cathedral, the original performance offered a spatial composition highlighting the complex acoustics and interactions between source and listener positions. Individual tracks were convolved with spatial room impulse responses, created from a calibrated geometrical acoustic simulation. Several listening positions were binaurally rendered, along with an artistic mix created by a sound engineer, offering a unique 3d-audio experience approaching the reality of the moment in the past. Distributed on-line, the website presenting this virtual reconstruction has been visited from users around the world. A listener opinion survey was included on the website. This paper presents an overview of the source material, the production workflow, and the challenges of realising such a production during confinement. Finally, an overview of visitor statistics and survey results is presented, providing insight into the reception of the virtual recreation and the interest towards future productions.

  • Open Access English
    Authors: 
    A. Perciaccante; Alessia Coralli; Philippe Charlier;
    Publisher: Elsevier Masson SAS.
    Country: France

    International audience; Background: In the absence of a treatment still considered universally effective, and of a vaccine validated by the health authorities, we wanted to know which Catholic saint the European Christian community turned to in the event of infection with Covid-19 to request a miraculous healing. Methodology: An online survey was carried out on a sample of 1158 adults using social media tools. Results: All results are presented in this research, with a few saints in the majority, and some dictated by the symptomatology of the Covid-19 infection or the personalities of certain « doctor guru ». Conclusion: This medico-anthropological study is revealing the psychology of Western patients vis-à-vis the magic-religious means used in the fight against diseases, particularly in the epidemic/pandemic context.

  • Open Access English
    Authors: 
    Alexis Chapelan;
    Publisher: HAL CCSD
    Country: France

    Pandemic disease is not merely a biological reality but also a cognitive and socially constructed phenomenon which intensely mobilizes a multiplicity of political frames. Far-right political entrepreneurs are, despite their remoteness from actual decision-making processes, active stakeholders in the current crisis. Existential threats to societies breed a sense of urgency and heightened cultural warfare that is a hotbed for extremism. Our study seeks to map, compare and contrast the symbolic responses to the Coronavirus crisis articulated by various far-right actors in two established democracies in the transatlantic area: The United States and France. We aim to shed light on how entrenched far-right mythologies and tropes—which appear increasingly transatlantic—are channeled into a new synthesis as part of an “alternative” political epistemology. Infused with the mythos of resistance and insurgency, resolutely anti-systemic, this alternative epistemology can better be described, following Michael Barkun, as a form of “stigmatized knowledge”. Our study will employ a Critical Discourse Analysis framework to bring into focus, in the response of the Euro-American far-right to the COVID-19 crisis, the ideological semiotics of the current “infodemic”.

  • Open Access English
    Authors: 
    Leila Chassery; Gaëtan Texier; Vincent Pommier de Santi; Hervé Chaudet; Nathalie Bonnardel; Liliane Pellegrin;
    Publisher: HAL CCSD
    Country: France

    In late 2019, an epidemic of SARS-CoV-2 broke out in central China. Within a few months, this new virus had spread right across the globe, officially being classified as a pandemic on 11 March 2020. In France, which was also being affected by the virus, the government applied specific epidemiological management strategies and introduced unprecedented public health measures. This article describes the outbreak management system that was applied within the French military and, more specifically, analyzes an outbreak of COVID-19 that occurred on board a nuclear aircraft carrier. We applied the AcciMap systemic analysis approach to understand the course of events that led to the outbreak and identify the relevant human and organizational failures. Results highlight causal factors at several levels of the outbreak management system. They reveal problems with the benchmarks used for diagnosis and decision-making, and underscore the importance of good communication between different levels. We discuss ways of improving epidemiological management in military context.

Advanced search in
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arrow_drop_down
Searching FieldsTerms
Any field
arrow_drop_down
includes
arrow_drop_down
Include:
44 Research products, page 1 of 5
  • 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.

  • 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

  • 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 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 English
    Authors: 
    F.G. Katz Brian; David Poirier-Quinot; Jean-Marc Lyzwa;
    Publisher: HAL CCSD
    Country: France

    International audience; In honour of the International Year of Sound and for the 1 year memorial of the Notre-Dame cathedral fire, a team of researchers and sound engineers created a virtual reconstruction of a concert in the cathedral, using close-mic recordings made on 24-April-2013 of a performance of La Vierge by Jules Massenet. This reconstruction was carried out during the period of strict COVID-19 confinement. With 83 musicians, 6 singers and a 160person choir spatially distributed throughout the cathedral, the original performance offered a spatial composition highlighting the complex acoustics and interactions between source and listener positions. Individual tracks were convolved with spatial room impulse responses, created from a calibrated geometrical acoustic simulation. Several listening positions were binaurally rendered, along with an artistic mix created by a sound engineer, offering a unique 3d-audio experience approaching the reality of the moment in the past. Distributed on-line, the website presenting this virtual reconstruction has been visited from users around the world. A listener opinion survey was included on the website. This paper presents an overview of the source material, the production workflow, and the challenges of realising such a production during confinement. Finally, an overview of visitor statistics and survey results is presented, providing insight into the reception of the virtual recreation and the interest towards future productions.

  • Open Access English
    Authors: 
    A. Perciaccante; Alessia Coralli; Philippe Charlier;
    Publisher: Elsevier Masson SAS.
    Country: France

    International audience; Background: In the absence of a treatment still considered universally effective, and of a vaccine validated by the health authorities, we wanted to know which Catholic saint the European Christian community turned to in the event of infection with Covid-19 to request a miraculous healing. Methodology: An online survey was carried out on a sample of 1158 adults using social media tools. Results: All results are presented in this research, with a few saints in the majority, and some dictated by the symptomatology of the Covid-19 infection or the personalities of certain « doctor guru ». Conclusion: This medico-anthropological study is revealing the psychology of Western patients vis-à-vis the magic-religious means used in the fight against diseases, particularly in the epidemic/pandemic context.

  • Open Access English
    Authors: 
    Alexis Chapelan;
    Publisher: HAL CCSD
    Country: France

    Pandemic disease is not merely a biological reality but also a cognitive and socially constructed phenomenon which intensely mobilizes a multiplicity of political frames. Far-right political entrepreneurs are, despite their remoteness from actual decision-making processes, active stakeholders in the current crisis. Existential threats to societies breed a sense of urgency and heightened cultural warfare that is a hotbed for extremism. Our study seeks to map, compare and contrast the symbolic responses to the Coronavirus crisis articulated by various far-right actors in two established democracies in the transatlantic area: The United States and France. We aim to shed light on how entrenched far-right mythologies and tropes—which appear increasingly transatlantic—are channeled into a new synthesis as part of an “alternative” political epistemology. Infused with the mythos of resistance and insurgency, resolutely anti-systemic, this alternative epistemology can better be described, following Michael Barkun, as a form of “stigmatized knowledge”. Our study will employ a Critical Discourse Analysis framework to bring into focus, in the response of the Euro-American far-right to the COVID-19 crisis, the ideological semiotics of the current “infodemic”.

  • Open Access English
    Authors: 
    Leila Chassery; Gaëtan Texier; Vincent Pommier de Santi; Hervé Chaudet; Nathalie Bonnardel; Liliane Pellegrin;
    Publisher: HAL CCSD
    Country: France

    In late 2019, an epidemic of SARS-CoV-2 broke out in central China. Within a few months, this new virus had spread right across the globe, officially being classified as a pandemic on 11 March 2020. In France, which was also being affected by the virus, the government applied specific epidemiological management strategies and introduced unprecedented public health measures. This article describes the outbreak management system that was applied within the French military and, more specifically, analyzes an outbreak of COVID-19 that occurred on board a nuclear aircraft carrier. We applied the AcciMap systemic analysis approach to understand the course of events that led to the outbreak and identify the relevant human and organizational failures. Results highlight causal factors at several levels of the outbreak management system. They reveal problems with the benchmarks used for diagnosis and decision-making, and underscore the importance of good communication between different levels. We discuss ways of improving epidemiological management in military context.

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