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International audience; Purpose of Review We reviewed the occurrence of outbreaks at past Olympics and discuss the threat of the COVID-19 pandemic at the Tokyo Games. Recent Findings Evidence for large respiratory tract infection outbreaks at past Olympics is scant. Nevertheless, in order to control the spread of the COVID-19 outbreak, the Tokyo 2020 Olympics were postponed for 2021. Given the high contagiousness of the disease and the epidemiology of COVID-19 in Japan, this decision was appropriate and important in order to safeguard athletes and the public. However, it is a major problem for Japan, involving massive financial losses and a lost opportunity for athletes, coaches, and instructors. Up-to-date epidemiological data is needed on which to base an appropriate decision regarding the Tokyo 2021 Olympics. The actual effect of cancellations of such events in reducing the spread of COVID-19 needs to be determined.

Article paru dans Peer Community in Evolutionary Biology; International audience; A recommendation – based on reviews by Luca Ferretti and two anonymous reviewers – of the article: Danesh, G., Elie, B., Michalakis, Y., Sofonea, M. T., Bal, A., Behillil, S., Destras, G., Boutolleau, D., Burrel, S., Marcelin, A.-G., Plantier, J.-C., Thibault, V., Simon-Loriere, E., van der Werf, S., Lina, B., Josset, L., Enouf, V. and Alizon, S. and the COVID SMIT PSL group (2020) Early phylodynamics analysis of the COVID-19 epidemic in France. medRxiv, 2020.06.03.20119925, ver. 3 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/2020.06.03.20119925

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

International audience

The recent emergence of the SARS-CoV-2 in China has raised the spectre of a novel, potentially catastrophic pandemic in both scientific and lay communities throughout the world. In this particular context, people have been accused of being excessively pessimistic regarding the future consequences of this emerging health threat. However, consistent with previous research in social psychology, a large survey conducted in Europe in the early stage of the COVID-19 epidemic shows that the majority of respondents was actually overly optimistic about the risk of infection. https://psyarxiv.com/364qj/

BackgroundA novel disease poses special challenges for informatics solutions. Biomedical informatics relies for the most part on structured data, which require a preexisting data or knowledge model; however, novel diseases do not have preexisting knowledge models. In an emergent epidemic, language processing can enable rapid conversion of unstructured text to a novel knowledge model. However, although this idea has often been suggested, no opportunity has arisen to actually test it in real time. The current coronavirus disease (COVID-19) pandemic presents such an opportunity. ObjectiveThe aim of this study was to evaluate the added value of information from clinical text in response to emergent diseases using natural language processing (NLP). MethodsWe explored the effects of long-term treatment by calcium channel blockers on the outcomes of COVID-19 infection in patients with high blood pressure during in-patient hospital stays using two sources of information: data available strictly from structured electronic health records (EHRs) and data available through structured EHRs and text mining. ResultsIn this multicenter study involving 39 hospitals, text mining increased the statistical power sufficiently to change a negative result for an adjusted hazard ratio to a positive one. Compared to the baseline structured data, the number of patients available for inclusion in the study increased by 2.95 times, the amount of available information on medications increased by 7.2 times, and the amount of additional phenotypic information increased by 11.9 times. ConclusionsIn our study, use of calcium channel blockers was associated with decreased in-hospital mortality in patients with COVID-19 infection. This finding was obtained by quickly adapting an NLP pipeline to the domain of the novel disease; the adapted pipeline still performed sufficiently to extract useful information. When that information was used to supplement existing structured data, the sample size could be increased sufficiently to see treatment effects that were not previously statistically detectable.

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.