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AbstractWe developed a multi-level model of the French COVID-19 epidemic at the regional level. We rely on a global extended Susceptible-Exposed-Infectious-Recovered (SEIR) mechanistic model as a simplified representation of the average epidemic process, with the addition of region specific random effects. Combining several French public datasets on the early dynamics of the epidemic, we estimate region-specific key parameters conditionally on this mechanistic model through Stochastic Approximation Expectation Maximization (SAEM) optimization usingMonolixsoftware. We thus estimate the basic reproductive numbers by region before lockdown (with a national average at 2.81 with 95% Confidence Interval [2.58; 3.07]), attack rates (i.e. percentages of infected people) over time per region which range between 1.9% and 9.9% as of May 11th, 2020, and the impact of nationwide lockdown on the infection rate which decreased the transmission rate by 76% towards reproductive numbers ranging from 0.63 to 0.73 at the end of lockdown across regions. These results confirm the low population immunity, the strong effect of the lockdown on the dynamics of the epidemics and the need for further intervention when lifting the lockdown to avoid an epidemic rebound.

t is not so easy to assess the relative efficacy of the various restrictive measures that were adopted to curb the spread of SARS-CoV-19. An approach is presented here that attempts to infer changes in contact rates from publicly available data. Though the method is very crude (essentially a ‘hack’) it seems to allow us to link national Covid-19-lockdowns and other measures (relaxations) to temporary changes in transmission rates. Events that are supposed to have increased transmission do not always show up so clearly. However, whatever its limitations, I argue that this method represents an encouraging way backward (in time). More importantly, we can correlate these measures with a published ’stringency index’ of public health measures to curb Covid-19, to assess whether there is an effect on transmission. Despite the obvious shortcomings of both measures there is a clear negative correlation: the more stringent national measures are, the lower the estimated transmission rate.

This article offers an original case study of the failed sale of Victoria’s Secret’s business to examine the deep consequences of the COVID-19 pandemic in the context of M&A transactions. After presenting the case, it draws two lessons from it by: (1) proposing a multi-layered analysis of the effects of covid-19 depending on the type of event considered and on the contract language, and (2) exploring the possible reshaping of the termination right in connection with the dramatic variations of the target’s value during the interim period caused by COVID-19.

In this paper, we consider two-component mixture distributions having one known component. This type of model is of particular interest when a known random phenomenon is contaminated by an unknown random effect. We propose in this setup to compare the unknown random sources involved in two separate samples. For this purpose, we introduce the so-called IBM (Inversion-Best Matching) approach resulting in a relaxed semiparametric Cramér-von Mises type two-sample test requiring very minimal assumptions (shape constraint free) about the unknown distributions. The accomplishment of our work lies in the fact that we establish a functional central limit theorem on the proportion parameters along with the unknown cumulative distribution functions of the model when Patra and Sen [22] prove that the √ n-rate cannot be achieved on these quantities in the basic one-sample case. An intensive numerical study is carried out from a large range of simulation setups to illustrate the asymptotic properties of our test. Finally, our testing procedure is applied to a real-life application through pairwise post-covid mortality effect testing across a panel of European countries. Nous considérons dans cet article des mélanges à deux composantes dont une composante est connue. Ces modèles sont d'un intérêt particulier lorsqu'un phénomène bien connu est affecté par une perturbation inconnue. Nous proposons de comparer les sources de cet impact inconnu entre deux échantillons distincts. Pour ce faire nous introduisons une nouvelle approche, appelée IBM (Inversion-Best Matching), qui permet d'effectuer le test à la manière d'un test de type Cramer-Von Mises sous très peu d'hypothèses concernant les aléas perturbateurs inconnus. Nous établissons un théorème central limite (TCL) fonctionnel sur les estimateurs que sont les proportions de chacun des modèles de contamination, ainsi que la fonction de répartition inconnue, là où Patra et Sen [22] n'obtenait pas de résultat asymptotique de type TCL. Des simulations permettent de valider la performance de la procédure de test et d'en valider ses propriétés asymptotiques. Enfin, nous appliquons notre stratégie de test à un problème concret de comparaison de déformation de la mortalité suite à la pandémie de Covid-19 à travers plusieurs pays européens.

In this paper we investigate cross-commodity futures markets connectedness over different nearest-to-maturities. We thus implement time and time-frequency estimations for two constructed baskets of commodities, classified based on common delivery months. Using daily data spanning the period 1995-2020, we provide a set of stylized facts on the extent to which commodity markets are integrated or segmented. More specifically, our results show that the total connectedness is broadly insensitive to maturity. However, after 2008 financial crisis, the connectedness among commodity futures prices increases when the maturity increases. Furthermore, the overall connectedness amplifies during crises periods compared to tranquil periods. Moreover, certain pairwise markets are comparatively highly linked such as crude oil and heating oil, wheat and corn, corn and soybean, and soybean and soybean oil. The results also demonstrate that crude oil and heating oil are net transmitters all the time and across maturities, while natural gas, gold, and wheat are net receivers all the time and across maturities. More interestingly, the frequency decomposition reveals that most of periods of high total connectedness are driven mostly by high frequency components, which may indicate that commodity markets process information rapidly, except for the COVID-19 crisis period where total connectedness has been driven by lower frequency components.

The SARS-CoV-2 pandemic has entered an uncertain race between the emergence of variants that are more transmissible and vaccine roll-out. Here, we developed a mathematical model to evaluate how the interplay of variants, vaccines and non-pharmaceutical interventions might shape the pandemic dynamics, using the rise of the B.1.1.7 variant in metropolitan France as a case study. Our analysis highlights the challenges ahead for the management of the SARS-CoV-2 pandemic and shows how the quick roll-out of vaccines to at-risk individuals and non-pharmaceutical interventions are needed to mitigate the impact of the emerging variants.

Following the COVID-19 outbreak, governments all around the world have implemented public health and economic measures to contain the spread of the virus and to support the economy. Public health measures include domestic lockdown, school closures and bans on mass gatherings among others. Economic measures cover wage support, cash transfers, interest rates cuts, tax cuts and delays, and support to exporters or importers. This paper presents a unique living dataset of governments' responses to COVID-19. The dataset codes the various policy interventions with their dates at the country-level for more than 200 countries from January 1 to May 27, 2020. The generation of detailed data on the measures taken by governments can help generate robust evidence to support public health and economic decision making.

BackgroundThe roll-out of COVID-19 vaccines is a multi-faceted challenge whose performance depends on pace of vaccination, vaccine characteristics and heterogeneities in individual risks.MethodsWe developed a mathematical model accounting for the risk of severe disease by age and comorbidity and transmission dynamics. We compared vaccine prioritisation strategies in the early roll-out stage and quantified the extent to which measures could be relaxed as a function of the vaccine coverage achieved in France.FindingsPrioritizing at-risk individuals reduces morbi-mortality the most if vaccines only reduce severity, but is of less importance if vaccines also substantially reduce infectivity or susceptibility. Age is the most important factor to consider for prioritization; additionally accounting for comorbidities increases the performance of the campaign in a context of scarce resources. Vaccinating 90% of ≥65 y.o. and 70% of 18-64 y.o. before the autumn 2021 with a vaccine that reduces severity by 90% and susceptibility by 80%, we find that control measures reducing transmission rates by 15-27% should be maintained to remain below 1,000 daily hospital admissions in France with a highly transmissible variant (basic reproduction numberR0=4). Assuming 90% of ≥65 y.o. are vaccinated, full relaxation of control measures might be achieved with a vaccine coverage of 89-100% in 18-64 y.o or 60-69% of 0-64 y.o.InterpretationEven in optimistic scenarios, current vaccination intentions in the French populations might not allow a complete relaxation of control measures. Vaccination of children, if possible, could help achieve this objective.

Most countries have started vaccinating people against COVID-19. However, due to limited production capacities and logistical challenges it will take months/years until herd immunity is achieved. Therefore, vaccination and social distancing have to be coordinated. In this paper, we provide some insight on this topic using optimization-based control on an age-differentiated compartmental model. For real-life decision making, we investigate the impact of the planning horizon on the optimal vaccination/social distancing strategy. We find that in order to reduce social distancing in the long run, without overburdening the healthcare system, it is essential to vaccinate the people with the highest contact rates first. That is also the case if the objective is to minimize fatalities provided that the social distancing measures are sufficiently strict. However, for short-term planning it is optimal to focus on the high-risk group. Comment: 25 pages, 16 figures