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By implementing Principal Component Analysis (PCA) of multitemporal satellite data, this paper presents modeling solutions for air pollutant variation in three scenarios related to COVID-19 lockdown: pre, during, and after lockdown. Tropospheric NO 2 satellite data from Sentinel-5P was used. Two novel PCA-models were developed: Weighted Principal Component Analysis (WPCA) and Rescaled Principal Component Analysis (RPCA). Model results were tested for goodness-of-fit to empirical NO 2 data. The models were used to predict actual near-surface NO 2 concentrations. Model-predicted NO 2 concentrations were validated with NO 2 data acquired at ground monitoring stations. Besides, meteorological bias affecting NO 2 was assessed. It was found that the weather component had substantial impact on NO 2 built-ups, propitiating air pollutant decrease during lockdown and increase after. WPCA and RPCA models well fitted to observed NO 2. Both models accurately estimated near-surface NO 2 concentrations. Modeled NO 2 variation results evidenced the prolongated effect of the total lockdown (up to half a year). Model-predicted NO 2 concentrations were found to highly correlate with monitoring station NO 2 data collected on the ground. It is concluded that PCA is reliable in identifying and predicting air pollution variation patterns. The implementation of PCA is recommended when analyzing other pollutant gases. [Display omitted] • PCA is reliable for identifying and predicting air pollution variation. • WPCA and RPCA models well fitted observed NO 2. • Both models accurately estimated near-surface NO 2 concentrations. • Total lockdown had prolongated effect on NO 2 concentrations. • Weather conditions propitiated air pollutant decrease during lockdown and increase after. [ABSTRACT FROM AUTHOR]

• The French people are among the most reluctant to be vaccinated against Covid_19. • The crisis management is partly responsible for this hesitancy. • We identified the mechanisms allowing to overcome this hesitancy. • Four strategies triggering these mechanisms are presented in a model. France is one of the most vaccine-hesitant countries in the world, including for coronavirus disease 2019 (COVID-19). After 10 months of restrictive measures and media coverage of the dangers of COVID-19, French attitudes towards a vaccine continue to deteriorate. The communication strategies of the government have not helped; in fact, they have made the situation worse. Empirical studies on the national strategy for management of the COVID-19 pandemic in France have shed light on the reasons for vaccine hesitancy. These studies have identified four pillars for the vaccination strategy: i) Communication regarding the importance of herd immunity, ii) making healthcare workers the focus of the vaccination campaign, iii) citizen mobilization and guaranteed consultations, and iv) access to free vaccines without delay. This paper discusses the evidence supporting this strategy. [ABSTRACT FROM AUTHOR]

Testing is a crucial control mechanism in the beginning phase of an epidemic when the vaccines are not yet available. It enables the public health authority to detect and isolate the infected cases from the population, thereby limiting the disease transmission to susceptible people. However, despite the significance of testing in epidemic control, the recent literature on the subject lacks a control-theoretic perspective. In this paper, an epidemic model is proposed that incorporates the testing rate as a control input and differentiates the undetected infected from the detected infected cases, who are assumed to be removed from the disease spreading process in the population. After estimating the model on the data corresponding to the beginning phase of COVID-19 in France, two testing policies are proposed: the so-called best-effort strategy for testing (BEST) and constant optimal strategy for testing (COST). The BEST policy is a suppression strategy that provides a minimum testing rate that stops the growth of the epidemic when implemented. The COST policy, on the other hand, is a mitigation strategy that provides an optimal value of testing rate minimizing the peak value of the infected population when the total stockpile of tests is limited. Both testing policies are evaluated by their impact on the number of active intensive care unit (ICU) cases and the cumulative number of deaths for the COVID-19 case of France. [ABSTRACT FROM AUTHOR]

This paper presents a data-based simple model for fitting the available data of the Covid-19 pandemic evolution in France. The time series concerning the 13 regions of mainland France have been considered for fitting and validating the model. An extremely simple, two-dimensional model with only two parameters demonstrated to be able to reproduce the time series concerning the number of daily demises caused by Covid-19, the hospitalizations, intensive care and emergency accesses, the daily number of positive tests and other indicators, for the different French regions. These results might contribute to stimulate a debate on the suitability of much more complex models for reproducing and forecasting the pandemic evolution since, although relevant from a mechanistic point of view, they could lead to nonidentifiability issues. [ABSTRACT FROM AUTHOR]

In this paper, we present a spatialized extension of a SIR model that accounts for undetected infections and recoveries as well as the load on hospital services. The spatialized compartmental model we introduce is governed by a set of partial differential equations (PDEs) defined on a spatial domain with complex boundary. We propose to solve the set of PDEs defining our model by using a meshless numerical method based on a finite difference scheme in which the spatial operators are approximated by using radial basis functions. Such an approach is reputed as flexible for solving problems on complex domains. Then we calibrate our model on the French department of Isère during the first period of lockdown, using daily reports of hospital occupancy in France. Our methodology allows to simulate the spread of Covid-19 pandemic at a departmental level, and for each compartment. However, the simulation cost prevents from online short-term forecast. Therefore, we propose to rely on reduced order modeling to compute short-term forecasts of infection number. The strategy consists in learning a time-dependent reduced order model with few compartments from a collection of evaluations of our spatialized detailed model, varying initial conditions and parameter values. A set of reduced bases is learnt in an offline phase while the projection on each reduced basis and the selection of the best projection is performed online, allowing short-term forecast of the global number of infected individuals in the department. The original approach proposed in this paper is generic and could be adapted to model and simulate other dynamics described by a model with spatially distributed parameters of the type diffusion–reaction on complex domains. Also, the time-dependent model reduction techniques we introduced could be leveraged to compute control strategies related to such dynamics. • We introduced a spatialized SIR model with diffusion operators to better take local transmission of the infection into account. • We developed a meshless RBF-FD numerical method for its simulation. • We calibrated our model over the first wave of Covid-19 in France. • We built a metamodel from a set of evaluations of the detailed model. • We aggregated models obtained by changing the number of elements kept in the reduced basis, the aggregated model has a more robust behavior with respect to changes in the sanitary rules. [ABSTRACT FROM AUTHOR]

When ambulances' turnaround time (TT) in emergency departments is prolonged, it not only affects the victim severely but also causes unavailability of resources in emergency medical services (EMSs) and, consequently, leaves a locality unprotected. This problem may worsen with abnormal situations, e.g., the current coronavirus disease 2019 (COVID-19) pandemic. Taking this into consideration, this paper presents a first study on the COVID-19 impact on ambulances' TT by analyzing historical data from the Departmental Fire and Rescue Service of the Doubs (SDIS 25), in France, for three hospitals. Because the TTs of SDIS 25 ambulances increased, this paper also calculated and analyzed the number of breakdowns in services, which augmented due to shortage of ambulances that return on service in time. It is, therefore, vital to have a decision-support tool to better reallocate resources by knowing the time EMSs ambulances and personnel will be in use. Thus, this paper proposes a novel two-stage methodology based on machine learning (ML) models to forecast the TT of each ambulance in a given time and hospital. The first stage uses a multivariate model of regularly spaced time series to predict the average TT (AvTT) per hour, which considers temporal variables and external ones (e.g., COVID-19 statistics, weather data). The second stage utilizes a multivariate irregularly spaced time series model, which considers temporal variables of each ambulance departure, type of intervention, external variables, and the previously predicted AvTT as inputs. Four state-of-the-art ML models were considered in this paper, namely, Light Gradient Boosted Machine, Multilayer Perceptron, Long Short-Term Memory, and Prophet. As shown in the results, the proposed methodology provided remarkable results for practical purposes. The AvTT accuracies obtained for the three hospitals were 90.16%, 97.02%, and 93.09%. And the TT accuracies were 74.42%, 86.63%, and 76.67%, all with an error margin of ± 10 min. • Analysis of the ambulances' turnaround time for three hospitals from 2015–2020. • Breakage calculation in the fire department ambulance service during COVID-19. • Predicting the ambulances' average turnaround time in a given hour and hospital. • Predicting the turnaround time for each intervention and its ambulances. [ABSTRACT FROM AUTHOR]

Depuis le 16 mars 2020, date de l'annonce du premier confinement en France dans le contexte de pandémie liée à la COVID-19 et de crise sanitaire, les Français ont connu un bouleversement brutal et durable de leurs rythmes et habitudes. La crise sanitaire a eu un impact fort sur la population générale et sur les patients souffrant de troubles psychiatriques. La demande de prise en charge en santé mentale est particulièrement accrue chez les jeunes, qui semblent les plus impactés par cette crise. Dans la première partie de cet article, nous décrirons l'impact de la crise sanitaire sur la santé mentale des jeunes et sur la fréquentation des urgences psychiatriques, avec une revue exhaustive de la littérature française et internationale. La littérature traitant de la santé mentale des enfants et des jeunes durant la crise sanitaire est vaste et régulièrement actualisée. Au 31 décembre 2021, seules quelques données concernant l'état de santé mentale des jeunes sont chiffrées et publiées. Dans la seconde partie, nous présenterons une étude épidémiologique descriptive réalisée dans le plus grand centre d'urgences psychiatriques français, afin de mettre en évidence les changements des flux de consultations des patients âgés de 15 à 25 ans durant la première année de la crise sanitaire, comparativement aux trois années précédentes. En conclusion, nous évoquerons les mesures et adaptations du système de soins français mises en place pour prendre en charge les besoins accrus en santé mentale de cette population spécifique des 15–25 ans, particulièrement malmenée par la crise sanitaire, véritable enjeu national de santé publique. Since March 16, 2020, the date of the announcement of the first confinement in France in the context of the COVID-19 pandemic and health crisis, French residents have experienced a brutal and lasting disruption in their rhythms and habits. The health crisis has had a strong impact on the general population and especially on patients suffering from psychiatric disorders. The demand for mental health care has particularly heightened among young people, who seem to be the most affected by this crisis. In the first part of this article, we will describe the impact of the health crisis on the mental health of young people and on the attendance of psychiatric emergencies, with a review of French and international literature. In the second part, we will present a descriptive epidemiological study carried out in the largest French psychiatric emergency center. Our sample included 9,771 young patients from 15 to 25 years old. The main objective of our study is to highlight the changes in the flow of consultations of patients aged 15 to 25 years during the first year of the health crisis, compared to the three previous years. We calculated and compared the proportion of the target population consulting between the period of the first year of the health crisis (COVID period) and the period encompassing the previous three years (pre-COVID period). We also compared different variables, between the 2,179 young people consulting during the first year of COVID-19 health crisis and the 7,592 young people during the three last years: age, sex, marital status, symptoms, environmental context and CIM-10 diagnoses. We observed a significant increase in the proportion of patients aged 15 to 25 during the COVID period (n = 29.4% vs. 27.6%; P < 0,001). The patients during the COVID period were predominantly female. We have seen a significant increase in patients coming for consultations for anxiety (+4.3%; P < 0.001), sleep disorders (+2.8%; P < 0.001) and suicidal thoughts (+2.7%; P = 0.006) during the COVID period. During the first year of the health crisis, the target population consulted less for claustration, withdrawal or odd behavior. Family conflicts, traumatic events and other life events were more frequent. Schizophrenia, schizotypal and delusional disorders were less frequently diagnosed. The health crisis has had a considerable impact on the mental health of the youth population. The mental health of young people is a current subject of concern and a major public health issue. Data from the literature warns of the impact of the health crisis on the mental health of this population and the need for early treatment, even as the supply of psychiatric care is decreasing. [ABSTRACT FROM AUTHOR]

In mid-march in France following the announcement of containment of persons to their home, a number of city dwellers left to the country side. Countrymen were not enthusiastic in fear of contamination and this exodus was first strongly criticised in the media. Numerical simulations presented in the present paper show that the increase in infected persons in the countryside is over compensated by the decrease of the infection in cities. At the Nation level the effects of this urban exodus were then beneficial. • In France after the announcement of containment a number of city dwellers left to the country side. • The increase of infections in the country side was over compensated by their decrease in the city. • This effect is due to the smaller population density in the country side. • At the Nation level the urban exodus was beneficial. [ABSTRACT FROM AUTHOR]

The COVID-19 virus outbreak has affected most of the world in 2020. This paper deals with artificial intelligence (AI) methods that can address the problem of predicting scale, dynamics and sensitivity of the outbreak to preventive actions undertaken with a view to combatting the epidemic. In our study, we developed a cellular automata (CA) model for simulating the COVID-19 disease spreading. The enhanced infectious disease dynamics S E I R (Susceptible, Exposed, Infectious, and Recovered) model was applied to estimate the epidemic trends in Poland, France, and Spain. We introduced new parameters into the simulation framework which reflect the statistically confirmed dependencies such as age-dependent death probability, a different definition of the contact rate and enhanced parameters reflecting population mobility. To estimate key epidemiological measures and to predict possible dynamics of the disease, we juxtaposed crucial CA framework parameters to the reported COVID-19 values, e.g. length of infection, mortality rates and the reproduction number. Moreover, we used real population density and age structures of the studied epidemic populations. The model presented allows for the examination of the effectiveness of preventive actions and their impact on the spreading rate and the duration of the disease. It also shows the influence of structure and behavior of the populations studied on key epidemic parameters, such as mortality and infection rates. Although our results are critically dependent on the assumptions underpinning our model and there is considerable uncertainty associated with the outbreaks at such an early epidemic stage, the obtained simulation results seem to be in general agreement with the observed behavior of the real COVID-19 disease, and our numerical framework can be effectively used to analyze the dynamics and efficacy of epidemic containment methods. • Cellular Automata include density population of Poland, France, Spain in SEIR model. • We analyze influence of parametric adjustment for modeling pandemic prevention. • The earlier reduction of personal contacts the faster reduction of infections number. • Age dependent mortality rates are explored to reflect the real features of COVID-19. • Our approach allows for modeling of COVID-19 presumable dynamics. [ABSTRACT FROM AUTHOR]

As Covid-19 spreads across the world, governments turn a hopeful eye towards research and development of a vaccine against this new disease. But it is one thing to make a vaccine available, and it is quite another to convince the public to take the shot, as the precedent of the 2009 H1N1 influenza illustrated. In this paper, we present the results of four online surveys conducted in April 2020 in representative samples of the French population 18 years of age and over (N = 5018). These surveys were conducted during a period when the French population was on lockdown and the daily number of deaths attributed to the virus reached its peak. We found that if a vaccine against the new coronavirus became available, almost a quarter of respondents would not use it. We also found that attitudes to this vaccine were correlated significantly with political partisanship and engagement with the political system. Attitudes towards this future vaccine did not follow the traditional mapping of political attitudes along a Left-Right axis. The rift seems to be between people who feel close to governing parties (Centre, Left and Right) on the one hand, and, on the other, people who feel close to Far-Left and Far-Right parties as well as people who do not feel close to any party. We draw on the French sociological literature on ordinary attitudes to politics to discuss our results as well as the cultural pathways via which political beliefs can affect perceptions of vaccines during the COVID-19 pandemic. • Almost a quarter of the French population would not get vaccinated against COVID-19. • Attitudes are correlated with political partisanship and engagement with politics. • Attitudes do not follow the traditional separation between Left-wing and Right-wing. • Refusal is associated with proximity radical parties and to abstention. [ABSTRACT FROM AUTHOR]