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International audience; Daily pandemic surveillance, often achieved through the estimation of the reproduction number, constitutes a critical challenge for national health authorities to design countermeasures. In an earlier work, we proposed to formulate the estimation of the reproduction number as an optimization problem, combining data-model fidelity and space-time regularity constraints, solved by nonsmooth convex proximal minimizations. Though promising, that first formulation significantly lacks robustness against the Covid-19 data low quality (irrelevant or missing counts, pseudo-seasonalities,.. .) stemming from the emergency and crisis context, which significantly impairs accurate pandemic evolution assessments. The present work aims to overcome these limitations by carefully crafting a functional permitting to estimate jointly, in a single step, the reproduction number and outliers defined to model low quality data. This functional also enforces epidemiology-driven regularity properties for the reproduction number estimates, while preserving convexity, thus permitting the design of efficient minimization algorithms, based on proximity operators that are derived analytically. The explicit convergence of the proposed algorithm is proven theoretically. Its relevance is quantified on real Covid-19 data, consisting of daily new infection counts for 200+ countries and for the 96 metropolitan France counties, publicly available at Johns Hopkins University and Santé-Publique-France. The procedure permits automated daily updates of these estimates, reported via animated and interactive maps. Open-source estimation procedures will be made publicly available.

We study the impact on the epidemiological dynamics of a class of restrictive measures that are aimed at reducing the number of contacts of individuals who have a higher risk of being infected with a transmittable disease. Such measures are currently either implemented or at least discussed in numerous countries worldwide to ward off a potential new wave of COVID-19 across Europe. They come in the form of Health Passes (HP), which grant full access to public life only to individuals with a certificate that proves that they have either been fully vaccinated, have recovered from a previous infection or have recently tested negative to SARS-Cov-19 . We develop both a compartmental model as well as an epidemic Renormalisation Group approach, which is capable of describing the dynamics over a longer period of time, notably an entire epidemiological wave. Introducing different versions of HPs in this model, we are capable of providing quantitative estimates on the effectiveness of the underlying measures as a function of the fraction of the population that is vaccinated and the vaccination rate. We apply our models to the latest COVID-19 wave in several European countries, notably Germany and Austria, which validate our theoretical findings. Comment: 40 pages, 39 figures

Background: Reliable information is an essential component for responding to the COVID-19 epidemic, especially regarding the availability of critical care beds (CCBs). We propose three contributions: a) ICUBAM (ICU Bed Availability Monitor), a tool which both collects and visualizes information on CCB availability entered directly by intensivists. b) An analysis of CCB availability and ICU admissions and outcomes using collected by \ICUBAM during a 6-week period in the hard-hit Grand Est region of France, and c) Explanatory and predictive models adapted to CCB availability prediction, and fitted to availability information collected by ICUBAM. Methods: We interact directly with intensivists twice a day, by sending a SMS with a web link to the ICUBAM form where they enter 8 numbers: number of free and occupied CCBs (ventilator-equipped) for both COVID-19 positive and COVID-19- negative patients, the number of COVID-19 related ICU deaths and discharges, the number of ICU refusals, and the number of patients transferred to another region due to bed shortages. The collected data are described using univariate and multivariate methods such as correspondence analysis and then modeled at different scales: a medium and long term prediction using SEIR models, and a short term statistical model to predict the number of CCBs. Results: ICUBAM was brought online March 25, and is currently being used in the Grand-Est region by 109 intensivists representing 40 ICUs (95% of ICUs). ICUBAM allows for the calculation of CCB availability, admission and discharge statistics. Our analysis of data describes the evolution and extent of the COVID-19 health crisis in the Grand-Est region: on April 6th, at maximum bed capacity, 1056 ventilator-equipped CCBs were present, representing 211% of the nominal regional capacity of 501 beds. From March 19th to March 31st, average daily COVID-19 ICU inflow was 68 patients/day, and 314 critical care patients were transferred out of the Grand-Est region. With French lockdown starting on March 17th, a decrease of the daily inflow was found starting on April 1st: 23 patients/day during the first fortnight of April, and 7 patients/day during the last fortnight. However, treatment time for COVID-19 occupied CCBs is long: 15 days after the peak on March 31st, only 20% of ICU beds have been freed (50% after 1 month). Region-wide COVID-19 related in-ICU mortality is evaluated at 31%. Models trained from ICUBAM data are able to describe and predict the evolution of bed usage for the Grand-Estregion. Conclusion: We observe strong uptake of the ICUBAM tool, amongst both physicians and local healthcare stakeholders (health agencies, first responders etc.). We are able to leverage data collected with ICUBAM to better understand the evolution of the COVID-19 epidemic in the Grand Est region. We also present how data ingested by ICUBAM can be used to anticipate CCB shortages and predict future admissions. Most importantly, we demonstrate the importance of having a cross-functional team involving physicians, statisticians and computer scientists working both with first-line medical responders and local health agencies. This allowed us to quickly implement effective tools to assist in critical decision-making processes.

As part of the virtual BioHackathon 2020, we formed a working group that focused on the analysis of gene expression in the context of COVID-19. More specifically, we performed transcriptome analyses on published datasets in order to better understand the interaction between the human host and the SARS-CoV-2 virus.The ideas proposed during this hackathon were divided into five projects. Projects 1 and 2 aimed to identify human genes that are important in the process of viral infection of human cells. Projects 3 and 4 aimed to take the candidate genes identified in projects 1 and 2, as well as by independent studies, and relate them to clinical information and to possible therapeutic interventions. Finally, Project 5 aimed to package and containerize software and workflows used and generated here in a reusable manner, ultimately providing scalable and reproducible workflows.

This paper aims at reconstructing the evolution of all the available COVID-19 vaccines trials extracted from the COVID-NMA database by applying the phylomemy reconstruction process. We visualize the textual contents of 1,794 trials descriptions and explore their collective structure along with their semantic dynamics. We map the continuous progress of the main COVID-19 vaccine platforms from their early-stage trials in February 2020 to their most recent combinations driven by the rise of variants of concern, third dose issues and heterologous vaccinations. This paper brings insights for the global coordination between research teams especially in crisis situations such as the COVID-19 pandemic.

By using panel data of 15 Chinese manufacturing industries over the 2005-2014 period from OECD TiVA and WIOD databases, the impact of China's GVCs participation on labor productivity is estimated. We find that while the productivity elasticity of the share of sector's foreign value added relative to sector's exports known as sector backward linkages is negative, that relative to China's gross exports named structure backward linkage is positive. As the annual average growth rates of both backward linkages are negative, China's backward linkages have contributed to productivity growth of 6.41% per year on average. We find that the positive productivity elasticity of the share of domestic intermediate goods embodied in exports of third countries relative to sector's exports, named sector forward linages together with a positive annual average growth rate, and that relative to China's exports named structure forward linkages together with a negative annual average growth rate, have increased productivity of 1.97% per year on average. We find finally that GVCs position is improved from 0.3 in 2005 to 0.7 in 2014. China's GVCs participation exerted positive productivity effects via optimizing resource allocation inside sectors towards more efficiency ones, via moving up from low productivity backward linkages to higher productivity forward linkages and via improving its position. This diminished the risk to be entrenched in low-profitability low productivity growth GVCs activities in China. However, the productivity contribution of backward linkages 3 times higher than that of forward linkage suggests that the future positive productivity impact of GVCs moving up may be much more difficult in a less favorable context (trade war between China and USA, reindustrialization and trade protection related to Covid-19 for example).

The Covid-19 pandemic upset both the economies of most countries, but also the field of medical science. As never, public opinion has interfered in the choice of therapeutic trials as evidenced by the controversies surrounding protocols using hydroxychloroquine. The public's choice for these treatments is explained as the application of a kind of individual "Pascal's wager". This article analyses the formation of the belief system of individuals by applying ambiguity theory's insights and information entropy. It shows that the public's choices are the result of efficient communication strategies chosen by these treatments' promoters.

La pandémie de Covid-19 a bouleversé non seulement l'économie de la plupart des pays, mais aussi le domaine scientifique médical. Les opinions publiques se sont immiscées dans les choix des essais thérapeutiques comme le montrent les controverses autour des protocoles utilisant l'hydroxychloroquine. Le choix du public pour ces traitements est expliqué comme l'application d'un "pari de Pascal". Cet article analyse la formation du système de croyance des individus en appliquant la théorie de l'ambiguïté et la théorie de l'entropie d'information. Il montre que les choix du public sont le fruit de stratégies communication choisies par les promoteurs de tel ou tel traitement.

The worldwide COVID-19 pandemic has affected the tourism sector by closing borders, reducing both the transportation of tourists and tourist demand. Developing countries, such as Tanzania, where the tourism sector contributes a high share to gross domestic product, are facing considerable economic consequences. Tourism interlinks domestic sectors such as transport, accommodation, beverages and food, and retail trade and thus plays an important role in household income. Our study assessed the macroeconomic impacts of COVID-19 on the tourism sector and the Tanzanian economy as a case study of an impacted developing economy. We used a computable general equilibrium model framework to simulate the economic impacts resulting from the COVID-19 pandemic and quantitatively analysed the economic impacts.