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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

International audience; Estimation of dynamical systems - in particular, identification of their parameters - is fundamental in computational biology, e.g., pharmacology, virology, or epidemiology, to reconcile model runs with available measurements. Unfortunately, the mean and variance priorities of the parameters must be chosen very appropriately to balance our distrust of the measurements when the data are sparse or corrupted by noise. Otherwise, the identification procedure fails. One option is to use repeated measurements collected in configurations with common priorities - for example, with multiple subjects in a clinical trial or clusters in an epidemiological investigation. This shared information is beneficial and is typically modeled in statistics using nonlinear mixed-effects models. In this paper, we present a data assimilation method that is compatible with such a mixed-effects strategy without being compromised by the potential curse of dimensionality. We define population-based estimators through maximum likelihood estimation. We then develop an equivalent robust sequential estimator for large populations based on filtering theory that sequentially integrates data. Finally, we limit the computational complexity by defining a reduced-order version of this population-based Kalman filter that clusters subpopulations with common observational backgrounds. The performance of the resulting algorithm is evaluated against classical pharmacokinetics benchmarks. Finally, the versatility of the proposed method is tested in an epidemiological study using real data on the hospitalisation of COVID-19 patients in the regions and departments of France.

In this work, we provide a general mathematical formalism to study the optimal control of an epidemic, such as the COVID-19 pandemic, via incentives to lockdown and testing. In particular, we model the interplay between the government and the population as a principal-agent problem with moral hazard, à la Cvitanić et al. (Finance Stoch 22(1):1-37, 2018), while an epidemic is spreading according to dynamics given by compartmental stochastic SIS or SIR models, as proposed respectively by Gray et al. (SIAM J Appl Math 71(3):876-902, 2011) and Tornatore et al. (Phys A Stat Mech Appl 354(15):111-126, 2005). More precisely, to limit the spread of a virus, the population can decrease the transmission rate of the disease by reducing interactions between individuals. However, this effort-which cannot be perfectly monitored by the government-comes at social and monetary cost for the population. To mitigate this cost, and thus encourage the lockdown of the population, the government can put in place an incentive policy, in the form of a tax or subsidy. In addition, the government may also implement a testing policy in order to know more precisely the spread of the epidemic within the country, and to isolate infected individuals. In terms of technical results, we demonstrate the optimal form of the tax, indexed on the proportion of infected individuals, as well as the optimal effort of the population, namely the transmission rate chosen in response to this tax. The government's optimisation problems then boils down to solving an Hamilton-Jacobi-Bellman equation. Numerical results confirm that if a tax policy is implemented, the population is encouraged to significantly reduce its interactions. If the government also adjusts its testing policy, less effort is required on the population side, individuals can interact almost as usual, and the epidemic is largely contained by the targeted isolation of positively-tested individuals.

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.

Using the United Nations Comtrade database, we perform the Google matrix analysis of the multiproduct World Trade Network (WTN) for the years 2018-2020 comprising the emergence of the COVID-19 as a global pandemic. The applied algorithms -- the PageRank, the CheiRank and the reduced Google matrix -- take into account the multiplicity of the WTN links providing new insights on the international trade comparing to the usual import-export analysis. These algorithms establish new rankings and trade balances of countries and products considering every countries on equal grounds, independently of their wealth, and every products on the basis of their relative exchanged volumes. In comparison with the pre-COVID-19 period, significant changes in these metrics occur for the year 2020 highlighting a major rewiring of the international trade flows induced by the COVID-19 pandemic crisis. We define a new PageRank-CheiRank product trade balance, either export or import oriented, which is significantly perturbed by the pandemic. Comment: 22 pages, 2 tables, 13 figures, 2 appendices

Prix du meilleur second choix dans la catégorie recherche lors de la conférence UIIN.; International audience; To leverage the emerging potential of new technologies, digital transformation has been a clear priority for most large- and mid-sized organizations for over a decade now (Vial, 2019). However, COVID-19 pandemic has recently pushed several microbusinesses (MBs) to hurriedly initiate digital transformation (DT) efforts and keep their businesses afloat (Mandviwalla & Flanagan, 2021). MBs comprise a class of small and medium enterprise category (SMEs) that typically have fewer than 10 employees and lesser resources (OECD, 2021). They represent about 93 percent of all businesses in the Europe (European Commission, 2019). Their economic significance is also shown through ha survey which predicted that by 2024 small businesses through their DT efforts have the potential to add over 2.3 trillion USD to the global GDP, which would be key for the post pandemic economic recovery (CISCO, 2020). Prior research has shown that DT effectiveness varies significantly with firm size (Mandviwalla & Flanagan, 2021). Following these, the aim of our study is to examine to identify the enablers and inhibitors of digital transformation within the MB sector in Ireland.

Comme lors de la première vague épidémique de Covid-19 en 2020, les communications du Ministère de la santé et la presse nous alertent sur le fait que les hôpitaux de France sont saturés ou sur le point de l’être. Dans plusieurs régions françaises, des opérations chirurgicales sont déprogrammées. Les soignants, remobilisés même lorsqu’ils sont malades, montrent des signes d’épuisement, voire se mettent en grève. Que savons-nous de la tension hospitalière ? L’objectif de cette note est de clarifier la manière dont elle est mesurée, de clarifier comment l’indicateur de tension hospitalière est construit, à partir de l’analyse de ses tendances nationales et régionales. Cela nous amène à mettre en lumière le rôle de cet indicateur dans les politiques mises en place en période épidémique. Pour cela, nous allons aborder quatre questions.Comment a évolué la tension hospitalière depuis le début de la pandémie ? Connait-elle de grandes disparités régionales ? Comment la tension hospitalière est-elle calculée ? Que ne dit pas cet indicateur de tension hospitalière ?

Objectives In the early stages of the coronavirus disease 2019 (COVID-19) pandemic, chloroquine and its derivatives such as hydroxychloroquine (HCQ) were widely commented upon both within the scientific community and in the media. This paper explores the different factors that influenced public perceptions in France of the efficacy of HCQ as well as their evolution between April 2020 and June 2021. Methods This article draws on 5 surveys conducted among representative samples of the French population (projects COCONEL and TRACTRUST; quota method, n = 1006; 1004; 2006; 1014 and 1005). We asked questions on the effectiveness of chloroquine against COVID-19. We also collected sociodemographic variables and attitudes toward politics and science. Results Between April and June 2021, the proportion of respondents who believed in the efficacy of HCQ decreased rapidly from 35% to 14%. The proportion of respondents who believed that HCQ is ineffective rose gradually from 6% to 21%. After adjusting for the temporal effect, the logistic regression showed a very strong association between political orientation and the belief in the efficacy of HCQ. Respondents who felt closest to the more radical parties (far-right and far-left) were more likely to believe in the efficacy of HCQ than those who felt closest to the political center (O.R. 2.48 [1.95–3.15] and 1.87 [1.44–2.43]). The role of trust in the government and in science and of the degree of political engagement were investigated in the two waves conducted after the scientific consensus was established during the summer of 2020. High levels of trust in the government and in science and of politicization are associated with belief of HCQ proven inefficacy. Across the whole period, a majority of respondents were uncertain. Even in 2021, 41.5% stated that the data were insufficient to decide whether or not HCQ is effective and 25.2% stating that they did not know. Conclusion Because media coverage of scientific controversies is higher in times of uncertainty than after these controversies have died down, the publicization of therapeutic promises can have lasting consequences on attitudes towards science and medicine.

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.