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COVID-19 outbreaks at nursing homes during the recent pandemic, which received ample media coverage, may have lasting negative impacts on individuals’ perceptions regarding ursing homes. We argue that this could have sizable and persistent implications for savings and long-term care policies. We first develop a theoretical model predicting that higher nurs- ing home aversion should induce higher savings and stronger support for policies subsidizing home care. We further document, based on a survey on Canadians in their 50s and 60s, that higher nursing home aversion is widespread: 72% of respondents are less inclined to enter a nursing home because of the pandemic. Consistent with our model, we find that the latter are much more likely to have higher intended savings for older age because of the pandemic. We also find that they are more likely to strongly support home care subsidies.

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.

Abstract This article analyzes the audio diaries of a Tibetan physician, originally from Amdo (Qinghai Province, China), now living in New York City. Dr. Kunchog Tseten describes his experiences during the first wave of the COVID-19 pandemic, in spring and summer 2020, when Queens, New York—the location where he lives and works—was the “epicenter of the epicenter” of the novel coronavirus outbreak in the United States. The collaborative research project of which this diary is a part combines innovative methodological approaches to qualitative, ethnographic study during this era of social distancing with an attunement to the relationship between language, culture, and health care. Dr. Kunchog’s diary and our analysis of its contents illustrate the ways that Tibetan medicine and Tibetan cultural practices, including those emergent from Buddhism, have helped members of the Himalayan and Tibetan communities in New York City navigate this unprecedented moment with care and compassion.

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.

Author(s): Laffoley, Dan; Baxter, John M; Amon, Diva J; Claudet, Joachim; Hall-Spencer, Jason M; Grorud-Colvert, Kirsten; Levin, Lisa A; Reid, P Chris; Rogers, Alex D; Taylor, Michelle L; Woodall, Lucy C; Andersen, Natalie F

International audience

Covid-19, climate change, flash floods, heat waves, wildfires: the present contribution originates at a moment in history when emergencies and their retinue of emergency measures proliferate, supersede and worsen (less often improve) each other, and when the side effects of one inform and transform our perceptions of the other. Between the environmental consequences of the current disease outbreak, its economic impacts on clean energy transition and the increased frequency of natural disaster...

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.

Therapies - Sous presse. Epreuves corrigees par l'auteur. Disponible en ligne depuis le jeudi 10 juin 2021

International audience; The exhaustive automatic detection of symptoms in social media posts is made difficult by the presence of colloquial expressions, misspellings and inflected forms of words. The detection of self-reported symptoms is of major importance for emergent diseases like the Covid-19. In this study, we aimed to (1) develop an algorithm based on fuzzy matching to detect symptoms in tweets, (2) establish a comprehensive list of Covid-19-related symptoms and (3) evaluate the fuzzy matching for Covid-19-related symptom detection in French tweets. The Covid-19-related symptom list was built based on the aggregation of different data sources. French Covid-19-related tweets were automatically extracted using a dedicated data broker during the first wave of the pandemic in France. The fuzzy matching parameters were finetuned using all symptoms from MedDRA and then evaluated on a subset of 5000 Covid-19-related tweets in French for the detection of symptoms from our Covid-19-related list. The fuzzy matching improved the detection by the addition of 42% more correct matches with an 81% precision.