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The ongoing COVID-19 outbreak has expanded rapidly throughout China. Major behavioral, clinical, and state interventions are underway currently to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, have affected COVID-19 spread in China. We use real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation on transmission in cities across China and ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was well explained by human mobility data. Following the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases are still indicative of local chains of transmission outside Wuhan. This study shows that the drastic control measures implemented in China have substantially mitigated the spread of COVID-19. One sentence summary: The spread of COVID-19 in China was driven by human mobility early on and mitigated substantially by drastic control measures implemented since the end of January.

The first case of coronavirus disease 2019 (COVID-19) in Algeria was reported on 25 February 2020. Since then, it has progressed rapidly and the number of cases grow exponentially each day. In this article, we utilize SEIR modelling to forecast COVID-19 outbreak in Algeria under two scenarios by using the real-time data from March 01 to April 10, 2020. In the first scenario: no control measures are put into place, we estimate that the basic reproduction number for the epidemic in Algeria is 2.1, the number of new cases in Algeria will peak from around late May to early June and up to 82% of the Algerian population will likely contract the coronavirus. In the second scenario, at a certain date T, drastic control measures are taken, people are being advised to self-isolate or to quarantine and will be able to leave their homes only if necessary. We use SEIR model with fast change between fully protected and risky states. We prove that the final size of the epidemic depends strongly on the cumulative number of cases at the date when we implement intervention and on the fraction of the population in confinement. Our analysis shows that the longer we wait, the worse the situation will be and this very quickly produces.

Monitoring the Covid19 pandemic constitutes a critical societal stake that received considerable research efforts. The intensity of the pandemic on a given territory is efficiently measured by the reproduction number, quantifying the rate of growth of daily new infections. Recently, estimates for the time evolution of the reproduction number were produced using an inverse problem formulation with a nonsmooth functional minimization. While it was designed to be robust to the limited quality of the Covid19 data (outliers, missing counts), the procedure lacks the ability to output credibility interval based estimates. This remains a severe limitation for practical use in actual pandemic monitoring by epidemiologists that the present work aims to overcome by use of Monte Carlo sampling. After interpretation of the nonsmooth functional into a Bayesian framework, several sampling schemes are tailored to adjust the nonsmooth nature of the resulting posterior distribution. The originality of the devised algorithms stems from combining a Langevin Monte Carlo sampling scheme with Proximal operators. Performance of the new algorithms in producing relevant credibility intervals for the reproduction number estimates and denoised counts are compared. Assessment is conducted on real daily new infection counts made available by the Johns Hopkins University. The interest of the devised monitoring tools are illustrated on Covid19 data from several different countries.

Little is known about the general equilibrium impact COVID-19 induces on different gender groups. This paper addresses the problem of relatively few general equilibrium studies focusing on gender impacts of COVID-19. The analysis uses a gendered Computable General Equilibrium model linked to a microsimulation model that analyses a mild and severe scenario of the pandemic on economic and distributional outcomes for females. Irrespective of scenario, findings show that because women employment tend to have unskilled labour which is more concentrated in sectors that are hurt the most by COVID-19 response measures, they suffer disproportionately more from higher unemployment than their male counterparts. The poverty outcomes show worsened vulnerability for female-headed households given that, even prior to the pandemic, poverty was already higher amongst women. These simulated results are consistent with recently observed impacts and address research gaps important for well-designed public policies to reverse these trends.On connait peu les impacts d’équilibre général induits par la Covid-19 sur les groupes de genre différents. Cette étude adresse le problème de la pénurie d’études en équilibre général s’intéressant aux impacts de la COVID-19 sur le genre. L’analyse te combine un modèle d’équilibre général calculable sexo-spécifique avec un modèle de micro-simulation et évalue deux scenarios de la pandémie, l’un modéré et l’autre sévère, et leurs effets sur les résultats économiques et distributionnels des femmes. Quel que soit le scenario, les résultats démontrent que les femmes souffrent du chômage d’une manière disproportionée comparé aux hommes, puisque le travail des femmes tend à être du travail non qualifié, concentré dans les secteurs qui sont les plus frappés par les mesures de réponse à la COVID-19. En termes de pauvreté, les foyers dirigés par des femmes sont plus vulnérables, étant donné que même avant la pandémie, la pauvreté était déjà plus élevée chez les femmes. Les résultats simulés par cette étude concordent avec les impacts récemment observés, et abordent les lacunes de recherche nécessaires pour modéliser des politiques publiques bien conçues afin de renverser ces tendances.

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.

ABSTRACTClinical manifestations of COVID-19 caused by the novel coronavirus SARS-CoV-2 are associated with age. While children are largely spared from severe respiratory disease, they can present with a SARS-CoV-2-associated multisystem inflammatory syndrome (MIS-C) similar to Kawasaki’s disease. Here, we show distinct antibody (Ab) responses in children with MIS-C compared to adults with severe COVID-19 causing acute respiratory distress syndrome (ARDS), and those who recovered from mild disease. There was a reduced breadth and specificity of anti-SARS-CoV-2-specific antibodies in MIS-C patients compared to the COVID patient groups; MIS-C predominantly generated IgG Abs specific for the Spike (S) protein but not for the nucleocapsid (N) protein, while the COVID-19 cohorts had anti-S IgG, IgM and IgA Abs, as well as anti-N IgG Abs. Moreover, MIS-C patients had reduced neutralizing activity compared to both COVID-19 cohorts, indicating a reduced protective serological response. These results suggest a distinct infection course and immune response in children and adults who develop severe disease, with implications for optimizing treatments based on symptom and age.

As countries in Europe implement strategies to control the COVID-19 pandemic, different options are chosen regarding schools. Through a stochastic age-structured transmission model calibrated to the observed epidemic in Île-de-France in the first wave, we explored scenarios of partial, progressive, or full school reopening. Given the uncertainty on children’s role, we found that reopening schools after lockdown may increase COVID-19 cases, yet protocols exist to keep the epidemic controlled. Under a scenario with stable epidemic activity if schools were closed, reopening pre-schools and primary schools would lead to up to 76% [67, 84]% occupation of ICU beds if no other school level reopened, or if middle and high schools reopened later. Immediately reopening all school levels may overwhelm the ICU system. Priority should be given to pre- and primary schools allowing younger children to resume learning and development, whereas full attendance in middle and high schools is not recommended for stable or increasing epidemic activity. Large-scale test and trace is required to keep the epidemic under control. Ex-post assessment shows that progressive reopening of schools, limited attendance, and strong adoption of preventive measures contributed to a decreasing epidemic after lifting the first lockdown. The role of children in the spread of COVID-19 is not fully understood, and the circumstances under which schools should be opened are therefore debated. Here, the authors demonstrate protocols by which schools in France can be safely opened without overwhelming the healthcare system.