The major economic and health consequences of COVID-19 called for various protective measures and mass vaccination campaigns. A previsional model was used to predict the future impacts of various measure combinations on COVID-19 mortality over a 400-day period in France. Calibrated on previous national hospitalization and mortality data, an agent-based epidemiological model was used to predict individual and combined effects of booster doses, vaccination of refractory adults, and vaccination of children, according to infection severity, immunity waning, and graded non-pharmaceutical interventions (NPIs). Assuming a 1.5 hospitalization hazard ratio and rapid immunity waning, booster doses would reduce COVID-19-related deaths by 50–70% with intensive NPIs and 93% with moderate NPIs. Vaccination of initially-refractory adults or children ≥5 years would half the number of deaths whatever the infection severity or degree of immunity waning. Assuming a 1.5 hospitalization hazard ratio, rapid immunity waning, moderate NPIs and booster doses, vaccinating children ≥12 years, ≥5 years, and ≥6 months would result in 6212, 3084, and 3018 deaths, respectively (vs. 87,552, 64,002, and 48,954 deaths without booster, respectively). In the same conditions, deaths would be 2696 if all adults and children ≥12 years were vaccinated and 2606 if all adults and children ≥6 months were vaccinated (vs. 11,404 and 3624 without booster, respectively). The model dealt successfully with single measures or complex combinations. It can help choosing them according to future epidemic features, vaccination extensions, and population immune status.

Face au constat d’une heterogeneite grandissante des savoir-faire et connaissances en informatique des etudiants a l’arrivee en premiere annee, et le risque de son exacerbation dans le contexte du « nouveau bac », nous avons voulu experimenter une approche pedagogique, qui permette une gestion de cette heterogeneite tout en respectant les contraintes d’un emploi du temps homogene et un cout constant. Les actions menees s’articulent autour de 4 poles : la constitution de groupes de niveau, avec une attention particuliere portee sur les 2 niveaux extremes (renforcement et avance/en autonomie), la mise en place de QCMs reguliers, l’utilisation ponctuelle de l’Apprentissage Par Probleme (APP), et un auto-positionnement. L’experimentation est encore en cours, mais deja de premiers elements permettent d’ouvrir les echanges.

The outbreak of the SARS-CoV-2 virus, enhanced by rapid spreads of variants, has caused a major international health crisis, with serious public health and economic consequences. An agent-based model was designed to simulate the evolution of the epidemic in France over 2021 and the first six months of 2022. The study compares the efficiencies of four theoretical vaccination campaigns (over 6, 9, 12, and 18 months), combined with various non-pharmaceutical interventions. In France, with the emergence of the Alpha variant, without vaccination and despite strict barrier measures, more than 600,000 deaths would be observed. An efficient vaccination campaign (i.e., total coverage of the French population) over six months would divide the death toll by 10. A vaccination campaign of 12, instead of 6, months would slightly increase the disease-related mortality (+6%) but require a 77% increase in ICU bed–days. A campaign over 18 months would increase the disease-related mortality by 17% and require a 244% increase in ICU bed–days. Thus, it seems mandatory to vaccinate the highest possible percentage of the population within 12, or better yet, 9 months. The race against the epidemic and virus variants is really a matter of vaccination strategy.

Background The outbreak of SARS-CoV-2 virus has caused a major international health crisis with serious consequences in terms of public health and economy. In France, two lockdown periods were decided in 2020 to avoid the saturation of intensive care units (ICU) and an increase in mortality. The rapid dissemination of variant SARS-CoV-2 VOC 202012/01 has strongly influenced the course of the epidemic. Vaccines have been rapidly developed. Their efficacy against the severe forms of the disease has been established, and their efficacy against disease transmission is under evaluation. The aim of this paper is to compare the efficacy of several vaccination strategies in the presence of variants in controlling the COVID-19 epidemic through population immunity. Methods An agent-based model was designed to simulate with different scenarios the evolution of COVID-19 pandemic in France over 2021 and 2022. The simulations were carried out ignoring the occurrence of variants then taking into account their diffusion over time. The expected effects of three Non-Pharmaceutical Interventions (Relaxed-NPI, Intensive-NPI, and Extended-NPI) to limit the epidemic extension were compared. The expected efficacy of vaccines were the values recently estimated in preventing severe forms of the disease (75% and 94%) for the current used vaccines in France (Pfizer-BioNTech and Moderna since January 11, 2021, and AstraZeneca since February 2, 2021). All vaccination campaigns reproduced an advanced age-based priority advised by the Haute Autorit[c] de Sant[c]. Putative reductions of virus transmission were fixed at 0, 50, 75 and 90%. The effects of four vaccination campaign durations (6-month, 12-month, 18-month and 24-month) were compared. Results In the absence of vaccination, the presence of variants led to reject the Relaxed-NPI because of a high expected number of deaths (170 to 210 thousands) and the significant overload of ICUs from which 35 thousand patients would be deprived. In comparison with the situation without vaccination, the number of deaths was divided by 7 without ICU saturation with a 6-month vaccination campaign. A 12-month campaign would divide the number of deaths by 3 with Intensive-NPI and by 6 with Extended-NPI (the latter being necessary to avoid ICU saturation). With 18-month and 24-month vaccination campaigns without Extended-NPI, the number of deaths and ICU admissions would explode. Conclusion Among the four compared strategies the 6-month vaccination campaign seems to be the best response to changes in the dynamics of the epidemic due to the variants. The race against the COVID-19 epidemic is a race of vaccination strategy. Any further vaccination delay would increase the need of strengthened measures such as Extended-NPI to limit the number of deaths and avoid ICU saturation.

The estimation of R0, the so-called basic reproductive ratio, of the COVID-19 pandemic is of particular importance to help decision-makers take the necessary safeguard measures to protect the population. In this work, we examine a method based on the successive estimation of R0 over 3 non-overlapping periods (before lockdown, during lockdown and after). The approach is based on a variant of the, simple but flexible, SEIR compartmental model that allows to exploit the number of recovered individuals that are reported in the daily database published by national health agencies. The results of the approach is analysed w.r.t. data from France, at two levels of geographical subdivisions, i.e. the 13 regions and 96 departments that make up the metropolitan territory.

This paper presents the first available system for mining patterns from Displacement Field Time Series (DFTS) along with the confidence measures inherent to these series. It consists of four main modules for data preprocessing, pattern extraction, pattern ranking and pattern visualization. It is based on an efficient extraction of reliable grouped frequent sequential patterns and on swap randomization. It can be for example used to assess climate change impacts on glacier dynamics.

For more than 40 years, Earth observation satellites have been regularly providing images of glaciers that can be used to derive surface displacement fields and study their dynamics. In the context of global warming, the analysis of displacement field time series (DFTS) can provide useful information. Efficient data mining techniques are, thus, required to extract meaningful displacement evolutions from such large and complex datasets. In this paper, a pattern-based data mining approach, which handles confidence measures, is proposed to analyze DFTS. In order to focus on the most reliable measurements, a displacement evolution reliability measure is defined. It is aimed at assessing the quality of each evolution and pruning the search space. Experiments on two different DFTS (annual displacement fields derived from optical data over Greenland ice sheet and 11-day displacement fields derived from synthetic aperture radar data over Alpine glaciers) show the potential of the proposed approach.

Satellite Image Time Series (SITS) are large datasets containing spatiotemporal information about the surface of the Earth. In order to exploit the potential of such series, SITS analysis techniques have been designed for various applications such as earthquake monitoring, urban expansion assessment or glacier dynamic analysis. In this paper, we present an unsupervised technique for browsing SITS in preliminary explorations, before deciding whether to start deeper and more time consuming analyses. Such methods are lacking in today’s analyst toolbox, especially when it comes to stimulating the reuse of the ever growing list of available SITS. The method presented in this paper builds a summary of a SITS in the form of a set of maps depicting spatiotemporal phenomena. These maps are selected using an entropy-based ranking and a swap randomization technique. The approach is general and can handle either optical or radar SITS. As illustrated on both kinds of SITS, meaningful summaries capturing crustal deformation and environmental phenomena are produced. They can be computed on demand or precomputed once and stored together with the SITS for further usage.

Dynamic systems such as glaciers can be studied using Displacement Field Time Series (DFTS), often derived from Satellite Image Time Series. Even if data mining patterns expressing interesting displacement evolutions can be extracted from DFTS, confidence measures coming along with these series have to be considered to focus on reliable evolutions. This paper introduces a new approach for selecting displacement evolutions that are reliable, informative and complementary. Reported experiments exhibit consistent displacement evolutions of Alpine glaciers and complete the current knowledge of the area.

Les balmes lyonnaises, zones fortement urbanises, concentrent de nombreux risques gravitaires. L'objectif de cette etude est de presenter un modele geologique 3D cartographiant la colline de la Croix-Rousse et ses versants peripheriques. Cet outil d'information a pour but de cartographier la repartition des niveaux litho-stratigraphiques en 3D afin de distinguer celles dites « a risques ». Il peut donc constituer une aide supplementaire de prevision et de gestion des risques sur la zone etudiee.

Displacement Field Time Series (DFTS) are often derived from Satellite Image Time Series to study dynamic systems such as glaciers. This analysis can be performed with pattern-based data mining techniques that search DFTS for all possible displacement evolutions. Nevertheless, existing pattern oriented methods do not take into account the coherence measures coming along with such DFTS data. This paper introduces an approach for handling the coherence when extracting displacement evolutions. In addition to defining a coherence notion for these evolutions, we show that focusing on the coherent patterns allows to prune the search space. Reported experiments exhibit consistent displacement evolutions of Greenland ice sheet glaciers.

This paper presents a mining system for extracting patterns from Satellite Image Time Series. This system is a fully-fledged tool comprising four main modules for pre-processing, pattern extraction, pattern ranking and pattern visualization. It is based on the extraction of grouped frequent sequential patterns and on swap randomization.

Swap randomization has been shown to be an effective technique for assessing the significance of data mining results such as Boolean matrices, frequent itemsets, correlations or clusterings. Basically, instead of applying statistical tests on selected attributes, the global structure of the actual dataset is taken into account by checking whether obtained results are likely or not to occur in randomized datasets whose column and row margins are equal to the ones of the actual dataset. In this paper, a swap randomization approach for bases of sequences is proposed with the aim of assessing sequential patterns extracted from Satellite Image Time Series (SITS). This assessment relies on the spatiotemporal locations of the extracted patterns. Using an entropy-based measure, the locations obtained on the actual dataset and a single swap randomized dataset are compared. The potential and generality of the proposed approach is evidenced by experiments on both optical and radar SITS.

Grouped Frequent Sequential patterns can be extracted in an unsupervised way from Image Time Series (ITS). Plotting the occurrence maps of these patterns allows to describe the dataset spatially and temporally while discarding random uncertainties. However these maps can be too numerous and a swap randomization ranking approach has been proposed recently to select the most promising patterns. This previous work experimented the technique on Satellite ITS, giving credit to the maps that are least likely to appear on a randomized ITS. In this paper, extraction and ranking of GFS patterns is performed on a motion field time series obtained by terrestrial photogrammetry over the Argentière glacier. The focus is extended to the maps that are most likely to occur on the randomized time series and the experiment is repeated thousand times to assess the stability of the ranking.

Cet article presente une nouvelle approche pour l'analyse de series d'images satellite InSAR (Interferometric Synthetic Aperture Radar) et son application au monitoring de fluage le long d'une faille sismique active majeure. Les donnees InSAR permettent de mesurer les deformations du sol entre deux dates sur de grandes zones geographiques, mais la precision des mesures reste limitee par le bruit du aux variations en temps et en espace des conditions atmospheriques. L'approche proposee combine des techniques d'analyse d'images satellite et des techniques de fouille de donnees. Elle permet de traiter des series d'images satellite InSAR de facon non supervisee, meme avec des conditions atmospheriques variables, et fournit aux experts des cartes d'evolutions decrivant les deformations du sol. Des resultats experimentaux sur une serie d'images ENVISAT de la faille de Haiyuan (zone Nord-Est du plateau tibetain) sont presentes. Les cartes obtenues montrent un glissement asismique continu superficiel le long d'une portion de la faille, ce qui est consistant avec les modeles geophysiques actuels.

In this paper we present a method to summarize a satellite image time series. This summary is a small set of maps depicting salient phenomena occurring in the series over space and time. The approach is composed of a first step of extraction of spatiotemporal patterns, followed by an iterative ranking of these patterns using a swap randomization technique and a ranking based on a normalized mutual information measure. The best ranked patterns in the earliest iterations are in some sense the most informative and are used to build the summary. We present results showing that the approach is effective on both optical and radar data.

Satellite image time series (SITS) can be described in an unsupervised way by means of spatio-temporal localization maps. These maps are extracted using data mining techniques that spatially and temporally locate pixel evolutions affecting a minimum number of pixels with sufficiently high connectivity. Depending on the parameter settings and on the original data, large numbers of maps may be produced. In order to focus on the most interesting ones, we propose a method to rank them by computing the normalized mutual information between the spatio-temporal localization maps extracted from the SITS and the ones extracted from the same but randomized SITS. The latter is obtained using a swap-randomization technique. Experimental results on a Landsat 7 SITS covering New Caledonia are presented.