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Even though much has been learned about the new pathogen SARS-CoV-2 since the beginning of the COVID-19 pandemic, a lot of uncertainty remains. In this paper we argue that what is important to know under uncertainty is whether harm accelerates and whether health policies achieve deceleration of harm. For this, we need to see cases in relation to diagnostic effort and not to look at indicators based on cases only, such as a number of widely used epidemiological indicators, including the reproduction number, do. To do so overlooks a crucial dimension, namely the fact that the best we can know about cases will depend on some welldefined strategy of diagnostic effort, such as testing in the case of COVID-19. We will present a newly developed indicator to observe harm, the acceleration index, which is essentially an elasticity of cases in relation to tests. We will discuss what efficiency of testing means and propose that the corresponding health policy goal should be to find ever fewer cases with an ever-greater diagnostic effort. Easy and low-threshold testing will also be a means to give back people’s sovereignty to lead their life in an “open” as opposed to “locked-down” society.

1st lines: In the upcoming European Council on July 17 and 18, EU member states will fight for a compromise on the European Commission’s main project to tackle the economic fallout of the Covid-19 crisis across Europe: a new 7-year EU budget propped up with a temporary Recovery Instrument (Next Generation EU) amounting to EUR 750 bn of jointly issued debt and to be passed on to EU countries as grants and loans. It is one of the most ambitious in a long line of proposals for European debt mutualisation. While joint borrowing can carry a lot of advantages, debt mutualisation has always been very controversial. Confrontations between those countries supposedly benefiting and losing from mutualising debt have repeatedly centered on the legitimate balance of solidarity and responsibility that such debt implies. Democratic legitimacy in solidarity-responsibility arrangements can be achieved when they can deliver in terms of output legitimacy (being effective in economic terms), input legitimacy (ensuring sufficient room for domestic politics in deciding national policy trajectories) and throughput legitimacy (being run in a transparent and accountable manner).

Uprising in China, the global COVID-19 epidemic soon started to spread out in Europe. As no medical treatment was available, it became urgent to design optimal non-pharmaceutical policies. With the help of a SIR model, we contrast two policies, one based on herd immunity (adopted by Sweden and the Netherlands), the other based on ICU capacity shortage. Both policies led to the danger of a second wave. Policy efficiency corresponds to the absence or limitation of a second wave. The aim of the paper is to measure the efficiency of these policies using statistical models and data. As a measure of efficiency, we propose the ratio of the size of two observed waves using a double sigmoid model coming from the biological growth literature. The Oxford data set provides a policy severity index together with observed number of cases and deaths. This severity index is used to illustrate the key features of national policies for ten European countries and to help for statistical inference. We estimate basic reproduction numbers, identify key moments of the epidemic and provide an instrument for comparing the two reported waves between January and October 2020. We reached the following conclusions. With a soft but long lasting policy, Sweden managed to master the first wave for cases thanks to a low R 0 , but at the cost of a large number of deaths compared to other Nordic countries and Denmark is taken as an example. We predict the failure of herd immunity policy for the Netherlands. We could not identify a clear sanitary policy for large European countries. What we observed was a lack of control for observed cases, but not for deaths.

Group testing is a screening strategy that involves dividing a population into several disjointed groups of subjects. In its simplest implementation, each group is tested with a single test in the first phase, while in the second phase only subjects in positive groups, if any, need to be tested again individually. In this paper, we address the problem of group testing design, which aims to determine a partition into groups of a finite population in such a way that cardinality constraints on the size of each group and a constraint on the expected total number of tests are satisfied while minimizing a linear combination of the expected number of false negative and false positive classifications. First, we show that the properties and model introduced by Aprahmian et al. can be extended to the group test design problem, which is then modeled as a constrained shortest path problem on a specific graph. We design and implement an ad hoc algorithm to solve this problem. On instances based on Sant\'e Publique France data on Covid-19 screening tests, the results of the computational experiments are very promising.

Les perturbations économiques dues à la pandémie mondiale de Covid-19 ont déclenché d'innombrables litiges entre les entreprises sur la manière de faire respecter leurs relations contractuelles. Dans ce contexte, la survie des entreprises devient un réel problème lorsque les partenaires dans les relations verticales ou les chaînes de valeur mondiales se comportent de manière opportuniste. Ce comportement d'exploitation devrait-il être examiné comme des pratiques anticoncurrentielles par les autorités de la concurrence, en toute séparation des tribunaux ordinaires?Dans cet article, nous soutenons que la règle bien connue de l'abus de l'état de dépendance économique (ASED) pourrait dissuader l'opportunisme qui survient également lors de la pandémie de Covid-19 ou de toute autre crise mondiale. En fait, rejetés par la Chicago School de l'antitrust, non reconnus par la doctrine du contrat, à l'exception de sa facette de conduite négligente, les abus d'exploitation pourraient réintégrer la liste des pratiques néfastes pour les consommateurs, bien qu'après une analyse complexe. L’article examine en outre dans quelle mesure l’ASED, dissuadant un large éventail de comportements d’exploitation, comble le vide laissé par les dispositions réprimant les pratiques déloyales, même s’il confère une latitude considérable au pouvoir discrétionnaire des juges spécialisés. L'article soutien que la mise en œuvre de l'ASED assurerait une plus grande dissuasion de l'opportunisme légal dans la mesure où elle rétrécit les fissures du corpus juridique entre les abus d'exclusion et d'exploitation. Economic disruptions due to the global Covid-19 pandemic have unleashed uncountable litigation between firms on how to enforce their contractual relationships. In this context, the survival of undertakings becomes a real issue when partners in vertical relationships or global value chains behave opportunistically. Should this exploitative conduct be scrutinized as anti-competitive practices by the competition authorities in full separation from ordinary courts? In this paper, we argue that the well-known rule of abuse of the state of economic dependence (ASED) could deter opportunism that arises likewise during the Covid-19 pandemic or any other global crises. Actually, rejected by the Chicago School of Antitrust, and not acknowledged by the contract doctrine except its facet of negligent conduct, exploitative abuses could reintegrate the list of harmful practices to the consumers albeit after an intricate analysis. The article further investigates the extent to which the ASED, deterring a wide spectrum of exploitative conduct, fills the gap left by the provisions punishing unfair practices albeit it confers a considerable room for the specialist judges' discretion. Such ASED implementation would ensure more deterrence of the lawful opportunism inasmuch it narrows cracks of the legal corpus between exclusionary and exploitative abuses.

Le modèle évolutif SIR prédit une diminution trop forte des fractions de personnes infectées par COVID-19 en France après le début du verrouillage national, par rapport à ce qui est observé. J'ajuste avec des modèles SEIR étendus les données quotidiennes des hôpitaux français : arrivées dans les unités de soins intensifs et critiques, sorties et décès. Cette analyse implique des ratios de durée entre phases sur des fractions de branchement, supposés uniformes dans tout un pays, et le nombre de reproduction de base, R 0, avant et durant le confinement national, pour chaque région de France. L'analyse bayésienne jointe des régions permet des évaluations précises des rapports durée / fraction et des fractions des phases pré-hospitalisées. Les données hospitalières sont bien ajustées par les modèles, à l'exception des arrivées en soins intensifs, qui diminuent plus rapidement que prévu, indiquant un meilleur traitement au fil du temps. En moyenne sur la France, l'analyse donne R 0 = 3,4 ± 0,1 avant et 0,65 ± 0,04 (90% de confiance) durant le confinement, avec des petites variations régionales. Le 11 mai 2020, le taux de létalité par COVID-19 en France était de 4 ± 1% (90% de confiance), tandis que les Fiévreux sont largement plus nombreux que les Asymptomatiques, contrairement aux premières phases. Sans confinement ni de distanciation sociale, la France aurait subi plus de 2 millions de décès dus au COVID-19, tandis qu'un confinement appliqué 10 jours plus tôt aurait fait moins de 1000 morts. La fraction des personnes immunisées a atteint un plateau inférieur à 1% dans toute la France (3% à Paris) fin avril 2020 (95% de confiance), suggérant un manque d'immunité collective. La disponibilité généralisée des masques faciaux le 11 mai, lors de la levée partielle due confinement, devrait maintenir R 0 en dessous de l'unité si au moins 46% de la population les porte en dehors de leur domicile. Sinon, sans autre distanciation sociale, une deuxième vague est inévitable et fera tripler le nombre de décès entre début mai et octobre (si R 0 = 1,2) ou même fin juin (si R 0 = 2). The SIR evolutionary model predicts too sharp a decrease of the fractions of people infected with COVID-19 in France after the start of the national lockdown, compared to what is observed. I fit the daily hospital data: arrivals in regular and critical care units, releases and deaths, using extended SEIR models. These involve ratios of evolutionary timescales to branching fractions, assumed uniform throughout a country, and the basic reproduction number, R 0 , before and during the national lockdown, for each region of France. The joint-region Bayesian analysis allows precise evaluations of the time/fraction ratios and pre-hospitalized fractions. The hospital data are well fit by the models, except the arrivals in critical care, which decrease faster than predicted, indicating better treatment over time. Averaged over France, the analysis yields R 0 = 3.4 ± 0.1 before the lockdown and 0.65 ± 0.04 (90% c.l.) during the lockdown, with small regional variations. On 11 May 2020, the Infection Fatality Rate in France was 4 ± 1% (90% c.l.), while the Feverish vastly outnumber the Asymptomatic, contrary to the early phases. Without the lockdown nor social dis-tancing, over 2 million deaths from COVID-19 would have occurred throughout y France, while a lockdown that would have been enforced 10 days earlier would have led to less than 1000 deaths. The fraction of immunized people reached a plateau below 1% throughout France (3% in Paris) by late April 2020 (95% c.l.), suggesting a lack of herd immunity. The widespread availability of face masks on 11 May, when the lockdown was partially lifted, should keep R 0 below unity if at least 46% of the population wear them outside their home. Otherwise, without enhanced other social distancing, a second wave is inevitable and cause the number of deaths to triple between early May and October (if R 0 = 1.2) or even late June (if R 0 = 2).

ABSTRACTIn this short report, we bring some data-driven analyses of COVID-19 mortality in Brazil. The impact of COVID-19 is evaluated by comparing the 2019 and 2020 civilian death records. There is evidence of a considerable excess of deaths since the pandemic started with respect to the previous year. In some states, it is clear that not all excess of deaths in 2020 is due to COVID-19, but to other respiratory causes that did not present the same prevalence in the previous year. Because of this unusual behavior of respiratory deaths, we may infer the evidence of a huge amount of under-reporting deaths due to the COVID-19. The data also shows that COVID-19 has produced an excess death in all ages besides people above 90 and below 10 years. In addition, when separates by sex, data indicate a larger increase in the deaths among males than females.

Abstract Background Achieving universal health coverage (UHC) is a global priority and a keystone element of the 2030 Sustainable Development Goals. However, COVID-19 is causing serious impacts on tax revenue and many countries are facing constraints to new investment in health. To advance UHC progress, countries can also focus on improving health system technical efficiency to maximize the service outputs given the current health financing levels. Methods This study assesses Cambodia’s public health services technical efficiency, unit costs, and utilization rates to quantify the extent to which current health financing can accommodate the expansion of social health protection coverage. This study employs Data Envelopment Analysis (DEA), truncated regression, and pioneers the application of DEA Aumann-Shapley applied cost allocation to the health sector, enabling unit cost estimation for the major social health insurance payment categories. Results Overall, for the public health system to be fully efficient output would need to increase by 34 and 73% for hospitals and health centers, respectively. We find public sector service quality, private sector providers, and non-discretionary financing to be statistically significant factors affecting technical efficiency. We estimate there is potential supply-side ‘service space’ to expand population coverage to an additional 4.69 million social health insurance beneficiaries with existing financing if the public health system were fully efficient. Conclusions Public health service efficiency in Cambodia can be improved by increasing utilization of cost-effective services. This can be achieved by enrolling more beneficiaries into the social health insurance schemes with current supply-side financing levels. Other factors that can lead to increased efficiency are improving health service quality, regulating private sector providers, focusing on discretionary health financing, and incentivizing a referral system.

The COVID-19 pandemic has prompted numerous fluid dynamical simulations of the propagation of potentially virus-laden respiratory droplets. While these studies have highlighted the apparent sensitivity of the numerical results to the sizes of the emitted droplet and the local humidity, many of them are still performed in stagnant air, i.e., without any external air flow. This very short note demonstrates, on the basis of coarse-grained fluid dynamical simulations in a simple generic setting, that even modest winds or air draughts strongly impact the risks of short-ranged transmission via droplets. The induced dispersion of droplets may contribute to explaining the lower risks of viral transmission experienced outdoors, even at short range.

Our human community face a pandemic, which has lead us to lockdown half of world inhabitants. We are so disarmed to deal with the coronavirus (SARS-CoV-2) that lockdown seems us the only countermeasure suitable to stop the pandemic, whatever the resulting economic cost could be. First publications focus on disease characteristics, on disease infectivity, on available treatments, on ICU medical care, on lockdown but not on medical countermeasure simulation. In order to fill this gap, we propose a flow model, which calculates by period the people in different disease stages and under medical treatments and which propagates flow of people between periods. We use a discrete representation of the SIR model over a horizon of six months. Cplex solver has been chosen as a simulator. Notre communauté fait face à une pandémie, qui nous a conduit à confiner la moitié des habitants de notre planète. Nous sommes désarmés face au coronavirus (SRAS-CoV-2) et le confinement semble être la seule contre-mesure apte à stopper la pandémie, quel que soit le coût économique qui en résulte. Les premières publications portent sur les caractéristiques de la maladie, sur la contagiosité de la maladie, sur les traitements de première intention disponibles, sur les soins médicaux en unités de soins intensifs et sur le confinement. Nous n'avons pas trouvé d'articles traitant de la simulation de la pandémie de COVID-19 afin d'évaluer et de dimensionner les contre-mesures médicales.