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13 Research products

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  • 2017-2021
  • Preprint
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  • English
  • 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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  • Authors: Jean-Noel Barrot; Maxime Bonelli; Basile Grassi; Julien Sauvagnat;

    We estimate the causal effect of state-mandated business closures on economic and health outcomes in the context of the COVID-19 crisis in the US. We first show that business closures lead to a substantial drop in sales, earnings, and market values for affected firms. We then exploit sectoral variations in exposure to these restrictions across areas within the same state, and show that locking down 10% of the labor force is associated with a significant contraction in employment, but allows to reduce COVID-19 weekly infection and death rates by respectively 0.023 and 0.0015 percentage points. The findings translate into 24,000 saved lives for a cost of $115 billion. Finally, our empirical analysis suggests that the cost per life saved associated to business closures could have been significantly reduced if restrictions had targeted areas with intense workplace interactions

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Cédric Gil-Jardiné; Gabrielle Chenais; Catherine Pradeau; Eric Tentillier; +5 Authors

    Abstract Objectives During periods such as the COVID-19 crisis, there is a need for responsive public health surveillance indicators related to the epidemic and to preventative measures such as lockdown. The automatic classification of the content of calls to emergency medical communication centers could provide relevant and responsive indicators. Methods We retrieved all 796,209 free-text call reports from the emergency medical communication center of the Gironde department, France, between 2018 and 2020. We trained a natural language processing neural network model with a mixed unsupervised/supervised method to classify all reasons for calls in 2020. Validation and parameter adjustment were performed using a sample of 20,000 manually-coded free-text reports. Results The number of daily calls for flu-like symptoms began to increase from February 21, 2020 and reached an unprecedented level by February 28, 2020 and peaked on March 14, 2020, 3 days before lockdown. It was strongly correlated with daily emergency room admissions, with a delay of 14 days. Calls for chest pain, stress, but also those mentioning dyspnea, ageusia and anosmia peaked 12 days later. Calls for malaises with loss of consciousness, non-voluntary injuries and alcohol intoxications sharply decreased, starting one month before lockdown. Discussion This example of the COVID-19 crisis shows how the availability of reliable and unbiased surveillance platforms can be useful for a timely and relevant monitoring of all events with public health consequences. The use of an automatic classification system using artificial intelligence makes it possible to free itself from the context that could influence a human coder, especially in a crisis situation. Conclusion The content of calls to emergency medical communication centers is an efficient epidemiological surveillance data source that provides insights into the societal upheavals induced by a health crisis.

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    Preprint
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    Other literature type . 2021
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      Other literature type . 2021
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    Authors: Gamze Ozturk Danisman; Amine Tarazi;

    We examine the influence of economic policy uncertainty on bank stability post-2007-2008 global financial crisis. We rely on the economic policy uncertainty (EPU) index introduced by Baker et al. (2016). We use 176,477 quarterly observations for US commercial banks over the period from 2011Q1 to 2020Q3 and find consistent and robust evidence that bank stability decreases as the level of economic policy uncertainty increases. We specifically control for demand-side effects which indicates that the decrease in bank stability not only originates from borrowers' and customers' conditions but also from a change in bank behavior. A deeper investigation shows that the negative impact of policy uncertainty on bank stability is stronger for larger banks, and weaker for highly capitalized banks as well as for more liquid banks. Our findings have important implications particularly for the COVID-19 policy implementations.

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    Article . 2021
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    Authors: Pranesh, Raj Ratn; Farokhnejad, Mehrdad; Shekhar, Ambesh; Vargas-Solar, Genoveva;

    International audience; This paper presents the Multilingual COVID-19 Analysis Method (CMTA) for detecting and observing the spread of misinformation about this disease within texts. CMTA proposes a data science (DS) pipeline that applies machine learning models for processing, classifying (Dense-CNN) and analyzing (MBERT) multilingual (micro)-texts. DS pipeline data preparation tasks extract features from multilingual textual data and categorize it into specific information classes (i.e., 'false', 'partly false', 'misleading'). The CMTA pipeline has been experimented with multilingual micro-texts (tweets), showing misinformation spread across different languages. To assess the performance of CMTA and put it in perspective, we performed a comparative analysis of CMTA with eight monolingual models used for detecting misinformation. The comparison shows that CMTA has surpassed various monolingual models and suggests that it can be used as a general method for detecting misinformation in multilingual micro-texts. CMTA experimental results show misinformation trends about COVID-19 in different languages during the first pandemic months.

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  • Authors: Serge Blondel; Francois Langot; Judith Mueller; Jonathan Sicsic;

    This paper shows that prospect theory, extended to account for differences across individuals in their patience and their valuation of the vaccination as a common good can explain why more than 40% of the population has intent to reject the Covid-19 vaccination, as well as the differences in vaccination intentions across population subgroups. Indeed, prospect theory by over-weighting the side effect explains the reject of vaccination. This can be partially compensated by a high patience and/or a large valuation of the collective immunity. The calibrated version of our model, based on an original survey carried out on a representative sample of the adult population living in France allowing us to identify curvatures of their value function, their discount rates and their willingness to cooperate, can predict the evolution of the vaccination intentions between November 2020 an March 2021. We also show that the international differences in the vaccination intentions are closely related to the valuation of the vaccination as a common good.

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    Authors: Elizabeth Wrigley-Field; Mathew V. Kiang; Alicia R Riley; Magali Barbieri; +7 Authors

    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

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    Europe PubMed Central
    Article . 2021
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    Article . 2021
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      Article . 2021
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    Authors: Mazhar Mughal; Rashid Javed;

    An aspect of the Covid-19 pandemic that merits attention is its effects on marriage and childbirth. Although the direct fertility effects of people getting the virus may be minor, the impact of delayed marriages due to the first preventive lockdown, such as that imposed in Pakistan from March 14 to May 8 2020, and the closure of marriage halls that lasted till September 14 may be non-negligible. These demographic consequences are of particular import to developing countries such as Pakistan where birth rates remain high, marriage is nearly universal, and almost all child-bearing takes place within marriage. Based on historic marriage patterns, we estimate that the delay in nuptiality during the first wave of coronavirus outbreak may affect about half of the marriages that were to take place during the year. In Pakistan, childbearing begins soon after marriage, and about 37% of Pakistani married women give birth to their first child within twelve months of marriage. A sizeable number out of these around 400,000 annual births that occur within twelve months of the marriage may consequently be delayed. Postponement of marriages due to the accompanying difficult economic situation and employment precariousness should accentuate this fertility effect. The net fertility impact of the Covid-19 outbreak would ultimately depend not only on the delay in marriages but also on the reproductive behavior of existing couples.; Un aspect de la pandémie de Covid-19 qui mérite une attention particulière concerne ses effets sur le mariage et la naissance des enfants. Les conséquences démographiques sont particulièrement importantes pour les pays en développement tels que le Pakistan. Dans ce pays, le taux de natalité est élevé, le mariage est presque universel et la procréation se fait exclusivement dans le cadre dumariage. Bien que les effets directs du virus sur la fertilité des personnes infectées puissent être moins importants, l'impact des mariages retardés en raison des mesures de confinement tecomme celles qui avaient cours au Pakistan du 14 mars au 8 mai 2020, et de la fermeture des salles de mariage qui a duré jusqu'au 14 septembre peut être sérieux. Sur la base des modèles de mariage historiques, nous estimons que le retard de la nuptialité pendant la première vague de la pandémie de coronavirus pourrait affecter environ la moitié des mariages qui devaient avoir lieu pendant l'année. Au Pakistan, la réproduction commence peu après le mariage et environ 37 % des femmes mariées pakistanaises donnent naissance à leur premier enfant dans les douze mois suivant leur mariage. Un nombre non négligeable des 400 000 naissances annuelles qui surviennent dans les douze mois suivant le mariage pourrait donc être retardé. Le report des mariages en raison d'une situation économique difficile et de la précarité de l'emploi devrait accentuer cet effet sur la fécondité. En fin, l'impact net de l'épidémie de Covid-19 sur la fécondité dépendrait en fin de compte non seulement du report des mariages, mais aussi du comportementdes couples existants en matière de reproduction.

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    Authors: Christelle Baunez; Mickael Degoulet; Stéphane Luchini; Patrick A. Pintus; +2 Authors

    We show that the acceleration index, a novel indicator that measures acceleration and deceleration of viral spread (Baunez et al. 2020a,b), is essentially a test-controlled version of the reproduction number. As such it is a more accurate indicator to track the dynamics of an infectious disease outbreak in real time. We indicate a discrepancy between the acceleration index and the reproduction number, based on the infectivity and test rates and we provide a formal decomposition of this difference. When applied to French data for the ongoing COVID-19 pandemic, our decomposition shows that the reproduction number consistently underestimates the resurgence of the pandemic since the summer of 2020, compared to the acceleration index which accounts for the time-varying volume of tests. Because the acceleration index aggregates all the relevant information and captures in real time the sizeable time variation featured by viral circulation, it is a sufficient statistic to track the pandemic’s propagation.

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    Authors: Al-mouksit Akim; Firmin Ayivodji; Jeffrey Kouton;

    The objective of this paper is to assess the mitigating role of remittances during the adverse COVID-19 employment shock on Nigeriai¯s food insecurity. Based on pre-COVID-19 and postCOVID-19 surveys, we use a difference-in-difference approach while controlling for the time and household fixed effects. Results indicate that remittances are mitigating the negative consequences of COVID-19 employment shocks, especially in the short run. We find that 100% of the deterioration in food insecurity, owing to the shock, is offset by the remittances received. While the adverse effects of the shock persist over time, the mitigation effect of remittances appears to be effective only at the early stages of the pandemic, however. Furthermore, the mitigation effect of remittances seems heterogeneous regarding the origin of remittances, residence area, and poverty status. The mitigation effect of remittances is higher for remittances from abroad than for Domestic ones. We also find a higher mitigating effect of remittances in the rural area and for non-poor households. Finally, our results shed light on the capital channel as a crucial mechanism explaining the mitigation effect of remittances. Notably, findings suggest that formal financial inclusion, capital ownership like livestock or rental earnings, amplifies the attenuating effect of remittances.

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    Authors: Kandoussi, Malak; Langot, François;

    We develop a matching model that predicts the impact of the COVID-19 lockdown on US unemployment, while accounting for the contrasted impacts across various job types. The model is calibrated on the subprime experience and is then used to identify the job-specific lockdown shocks, using observed worker flows by diploma. The model persistence-which is significantly larger than in the Diamond-Mortensen-Pissarides model-is dampened by CARES act that facilitates the use of temporary separations. Counterfactual experiments show that time-varying risk, hiring cost externalities, and wage rigidity are needed to account for these crises.

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  • Authors: Jean-Noel Barrot; Maxime Bonelli; Basile Grassi; Julien Sauvagnat;

    We estimate the causal effect of state-mandated business closures on economic and health outcomes in the context of the COVID-19 crisis in the US. We first show that business closures lead to a substantial drop in sales, earnings, and market values for affected firms. We then exploit sectoral variations in exposure to these restrictions across areas within the same state, and show that locking down 10% of the labor force is associated with a significant contraction in employment, but allows to reduce COVID-19 weekly infection and death rates by respectively 0.023 and 0.0015 percentage points. The findings translate into 24,000 saved lives for a cost of $115 billion. Finally, our empirical analysis suggests that the cost per life saved associated to business closures could have been significantly reduced if restrictions had targeted areas with intense workplace interactions

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Cédric Gil-Jardiné; Gabrielle Chenais; Catherine Pradeau; Eric Tentillier; +5 Authors

    Abstract Objectives During periods such as the COVID-19 crisis, there is a need for responsive public health surveillance indicators related to the epidemic and to preventative measures such as lockdown. The automatic classification of the content of calls to emergency medical communication centers could provide relevant and responsive indicators. Methods We retrieved all 796,209 free-text call reports from the emergency medical communication center of the Gironde department, France, between 2018 and 2020. We trained a natural language processing neural network model with a mixed unsupervised/supervised method to classify all reasons for calls in 2020. Validation and parameter adjustment were performed using a sample of 20,000 manually-coded free-text reports. Results The number of daily calls for flu-like symptoms began to increase from February 21, 2020 and reached an unprecedented level by February 28, 2020 and peaked on March 14, 2020, 3 days before lockdown. It was strongly correlated with daily emergency room admissions, with a delay of 14 days. Calls for chest pain, stress, but also those mentioning dyspnea, ageusia and anosmia peaked 12 days later. Calls for malaises with loss of consciousness, non-voluntary injuries and alcohol intoxications sharply decreased, starting one month before lockdown. Discussion This example of the COVID-19 crisis shows how the availability of reliable and unbiased surveillance platforms can be useful for a timely and relevant monitoring of all events with public health consequences. The use of an automatic classification system using artificial intelligence makes it possible to free itself from the context that could influence a human coder, especially in a crisis situation. Conclusion The content of calls to emergency medical communication centers is an efficient epidemiological surveillance data source that provides insights into the societal upheavals induced by a health crisis.

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    Authors: Gamze Ozturk Danisman; Amine Tarazi;

    We examine the influence of economic policy uncertainty on bank stability post-2007-2008 global financial crisis. We rely on the economic policy uncertainty (EPU) index introduced by Baker et al. (2016). We use 176,477 quarterly observations for US commercial banks over the period from 2011Q1 to 2020Q3 and find consistent and robust evidence that bank stability decreases as the level of economic policy uncertainty increases. We specifically control for demand-side effects which indicates that the decrease in bank stability not only originates from borrowers' and customers' conditions but also from a change in bank behavior. A deeper investigation shows that the negative impact of policy uncertainty on bank stability is stronger for larger banks, and weaker for highly capitalized banks as well as for more liquid banks. Our findings have important implications particularly for the COVID-19 policy implementations.

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    Authors: Pranesh, Raj Ratn; Farokhnejad, Mehrdad; Shekhar, Ambesh; Vargas-Solar, Genoveva;

    International audience; This paper presents the Multilingual COVID-19 Analysis Method (CMTA) for detecting and observing the spread of misinformation about this disease within texts. CMTA proposes a data science (DS) pipeline that applies machine learning models for processing, classifying (Dense-CNN) and analyzing (MBERT) multilingual (micro)-texts. DS pipeline data preparation tasks extract features from multilingual textual data and categorize it into specific information classes (i.e., 'false', 'partly false', 'misleading'). The CMTA pipeline has been experimented with multilingual micro-texts (tweets), showing misinformation spread across different languages. To assess the performance of CMTA and put it in perspective, we performed a comparative analysis of CMTA with eight monolingual models used for detecting misinformation. The comparison shows that CMTA has surpassed various monolingual models and suggests that it can be used as a general method for detecting misinformation in multilingual micro-texts. CMTA experimental results show misinformation trends about COVID-19 in different languages during the first pandemic months.

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