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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

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

The high neutrophil to lymphocyte ratio observed in critically ill patients with COVID-19 is associated with excessive levels of reactive oxygen species (ROS), which promote a cascade of biological events that drive pathological host responses. ROS induce tissue damage, thrombosis and red blood cell dysfunction, which contribute to COVID-19 disease severity. We suggest that free radical scavengers could be beneficial for the most vulnerable patients. In this Comment article, Becker and colleagues consider how the excessive release of reactive oxygen species by neutrophils may perpetuate red blood cell dysfunction, thrombosis and tissue damage in severe cases of COVID-19.

Abstract In March 2020, the coronavirus disease 2019 outbreak was declared a pandemic by the World Health Organization and became a global health crisis. Authorities worldwide implemented lockdowns to restrict travel and social exchanges in a global effort to counter the pandemic. In France, and in French overseas departments, the lockdown was effective from 17 March to 11 May 2020. It was in this context that the 2–6 April 2020 eruption of Piton de la Fournaise (La Réunion Island, Indian Ocean) took place. Upon the announcement of the lockdown in France, a reduced activity plan was set up by the Institut de Physique du Globe de Paris, which manages the Observatoire Volcanologique du Piton de la Fournaise (OVPF). The aim was to (1) maintain remote monitoring operations by teleworking and (2) authorize fieldwork only for critical reasons, such as serious breakdowns of stations or transmission relays. This eruption provided an opportunity for the observatory to validate its capacity to manage a volcanic crisis with 100% remotely operated monitoring networks. We thus present the long- and short-term precursors to the eruption, and the evolution of the eruption recorded using the real-time monitoring data as communicated to the stakeholders. The data were from both continuously recording and transmitting field instruments as well as satellites. The volcano observatory staff remotely managed the volcano crisis with the various stakeholders based only on these remotely functioning networks. Monitoring duties were also assured in the absence of ad hoc field investigation of the eruption by observatory staff or face-to-face communications. The density and reliability of the OVPF networks, combined with satellite observations, allowed for trustworthy instrument-based monitoring of the eruption and continuity of the OVPF duties in issuing regular updates of volcanic activity in the context of a double crisis: volcanic and health.

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.

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.

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.

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.