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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

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.

AbstractBackgroundAs evidence shows that vaccine immunity to COVID-19 wanes with time and decreases due to variants, several countries are implementing booster vaccination campaigns. The objective of this study was to analyze the morbidity and mortality burdens of different primary and booster vaccination strategies against COVID-19, using France as a case study.MethodsWe used a deterministic, age-structured, compartmental model fitted to hospital admission data and validated against sero-prevalence data in France to analyze the impact of primary and booster vaccination strategies on morbidity and mortality assuming waning of immunity and increased virus transmissibility during winter.FindingsStrategies prioritizing primary vaccinations were systematically more effective than strategies prioritizing boosters. Regarding booster strategies targeting different age groups, their effectiveness varied with the levels of virus transmissibility, and according to the assumed loss of immunity for each age group. If the immunity reduction affects all age groups, people aged 30 to 49 years should be boosted in priority, even for low transmissibility levels. If the immunity reduction is restricted to people older than 65 years, boosting younger people becomes effective only above certain levels of transmissibility.InterpretationIncreasing the primary vaccination coverage should remain a priority to reduce morbidity and mortality due to COVID-19. If a plateau of primary vaccination has been reached, boosting immunity in younger age-groups could prevent more hospitalizations and deaths than boosting the immunity of older people, especially under conditions increasing SARS-CoV-2 transmissibility, or when facing new variants.FundingThe study was partially funded by the French national research agency through project SPHINX-17-CE36-0008-0.Research in contextEvidence before this studyMany countries have started booster vaccination programs against Covid-19, while others are still struggling to vaccinate their population. However, evidence is scarce regarding the optimal vaccination strategy to pursue in a rapidly evolving epidemiological context. A search of the literature on Nov 27 2021, using the terms (booster OR third dose) AND vaccine AND strategy AND (COVID* OR SARS*) AND (effect OR impact), returned 45 studies on PubMed and 1602 on medRxiv. However, very few studies assessed the public health impact of a booster strategy, and none of them compared different allocations strategies between primary and booster vaccinations, or investigated which age-group should be targeted for booster vaccination to maximize the public health impact of the strategy.Added value of this studyUsing an epidemiological model able to replicate the dynamic of the SARS-CoV-2 epidemic and able to account for the use of multiple vaccines and booster, we analyzed the effectiveness of different vaccination strategies, either based on prioritization of primary vaccination versus booster, or based on the age-group targeted for the booster vaccination. We evaluated the strategies in terms of hospitalizations and deaths avoided, in various epidemic scenarios during winter 2021-2022. To our knowledge, this is the first modeling study evaluating such strategies. We found that increasing primary vaccination of all adults is always more beneficial than giving a booster dose to elderly individuals, and that the age-group to target for a booster dose for optimal effectiveness depends on the level of transmission of the virus. As the level of SARS-CoV-2 transmission increases, boosting immunity in younger age-groups becomes the most effective strategy to decrease hospitalizations and deaths in the general population.Implications of all available evidenceCountries that have not reached the plateau of primary vaccination should focus their effort towards extending the overall primary vaccination coverage rather than boosting the immunity of fully vaccinated people, even for elderly individuals that may be facing waning immunity. When considering booster vaccination, the choice of which age groups to target should consider the level of virus transmissibility in the population. Considering the emergence of new, more transmissible SARS-CoV-2 variants, increasing the worldwide vaccination coverage should remain a priority.

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.

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

ABSTRACTThe virus neutralization test (VNT) is the reference for the assessment of the functional ability of neutralizing antibodies (NAb) to block SARS-CoV-2 entry into cells. New competitive immunoassays measuring antibodies preventing interaction between the spike protein and its cellular receptor are proposed as surrogate VNT (sVNT). We tested three commercial sVNT (a qualitative immunochromatographic test and two quantitative immunoassays named YHLO and TECO) together with a conventional anti-spike IgG assay (bioMérieux) in comparison with an in-house plaque reduction neutralization test (PRNT50) using the original 19A strain and different variants of concern (VOC), on a panel of 306 sera from naturally-infected or vaccinated patients. The qualitative test was rapidly discarded because of poor sensitivity and specificity. Areas under the curve of YHLO and TECO assays were, respectively, 85.83 and 84.07 (p-value >0.05) using a positivity threshold of 20 for PRNT50, and 95.63 and 90.35 (p-value =0.02) using a threshold of 80. However, the performances of YHLO and bioMérieux were very close for both thresholds, demonstrating the absence of added value of sVNT compared to a conventional assay for the evaluation of the presence of NAb in seropositive subjects. In addition, the PRNT50 assay showed a reduction of NAb titers towards different VOC in comparison to the 19A strain that could not be appreciated by the commercial tests. Despite the good correlation between the anti-spike antibody titer and the titer of NAb by PRNT50, our results highlight the difficulty to distinguish true NAb among the anti-RBD antibodies with commercial user-friendly immunoassays.