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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.

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.

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.

On both sides of the Atlantic, in Anglo-Saxon countries, the issue of excess mortality due to Covid-19 among members of minorities has emerged as a central social justice issue. Outside the Anglo-Saxon countries, where race and ethnicity are generally recorded, it is difficult to address this issue. However, in France, data for the period up to the end of confinement, mentioning country of birth and place of death, from "état-civil" files, allow comparisons to be made on the determinants of the severity of Covid-19 integrating ethnicity. Regression analyses based on the difference in death counts between the spring of 2020 and the same period of previous years, show that the interweaving of health status, household size and ethnicity accurately reflects the disparities between departmental mortality rates due to Covid-19. People born in Black Africa clearly appear to be in a worse position than those born in the Maghreb, in Asian and European countries, not to mention the natives, in terms of risk of death.

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.

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.

International audience; Estimation of dynamical systems - in particular, identification of their parameters - is fundamental in computational biology, e.g., pharmacology, virology, or epidemiology, to reconcile model runs with available measurements. Unfortunately, the mean and variance priorities of the parameters must be chosen very appropriately to balance our distrust of the measurements when the data are sparse or corrupted by noise. Otherwise, the identification procedure fails. One option is to use repeated measurements collected in configurations with common priorities - for example, with multiple subjects in a clinical trial or clusters in an epidemiological investigation. This shared information is beneficial and is typically modeled in statistics using nonlinear mixed-effects models. In this paper, we present a data assimilation method that is compatible with such a mixed-effects strategy without being compromised by the potential curse of dimensionality. We define population-based estimators through maximum likelihood estimation. We then develop an equivalent robust sequential estimator for large populations based on filtering theory that sequentially integrates data. Finally, we limit the computational complexity by defining a reduced-order version of this population-based Kalman filter that clusters subpopulations with common observational backgrounds. The performance of the resulting algorithm is evaluated against classical pharmacokinetics benchmarks. Finally, the versatility of the proposed method is tested in an epidemiological study using real data on the hospitalisation of COVID-19 patients in the regions and departments of France.

Author summary Type I interferons are major antiviral effectors produced by the host in response to viral infections. Importantly, delayed or impaired type I IFN signalling response has been shown to correlate with severe COVID-19. These observations provided further impetus to test the administration of exogenous type I IFN as a treatment against SARS-CoV-2 infection in patients. However, studies using MERS-CoV or SARS-CoV infected mice demonstrated that type I interferon treatment was beneficial when administered early, but was ineffective and even caused deleterious immunopathology when administered at later stages of infection. It is therefore crucial to understand how the timing of the type I IFN treatments modulates their efficacy and safety against SARS-CoV-2. In this preclinical study using the SARS-CoV-2-infected Syrian hamster model, we showed that intranasal type I IFN treatment was beneficial only when administered before the onset of symptoms. Importantly, late treatment was ineffective but was not associated with deleterious effects. This study provides important information to interpret clinical trials showing no to modest effects of type I IFNs in COVID-19 patients. Impaired type I interferons (IFNs) production or signaling have been associated with severe COVID-19, further promoting the evaluation of recombinant type I IFNs as therapeutics against SARS-CoV-2 infection. In the Syrian hamster model, we show that intranasal administration of IFN-α starting one day pre-infection or one day post-infection limited weight loss and decreased viral lung titers. By contrast, intranasal administration of IFN-α starting at the onset of symptoms three days post-infection had no impact on the clinical course of SARS-CoV-2 infection. Our results provide evidence that early type I IFN treatment is beneficial, while late interventions are ineffective, although not associated with signs of enhanced disease.

Invasive pulmonary aspergillosis (IPA) in intensive care unit patients is a major concern. Influenza-associated acute respiratory distress syndrome (ARDS) and severe COVID-19 patients are both at risk of developing invasive fungal diseases. We used the new international definitions of influenza-associated pulmonary aspergillosis (IAPA) and COVID-19-associated pulmonary aspergillosis (CAPA) to compare the demographic, clinical, biological, and radiological aspects of IAPA and CAPA in a monocentric retrospective study. A total of 120 patients were included, 71 with influenza and 49 with COVID-19-associated ARDS. Among them, 27 fulfilled the newly published criteria of IPA: 17/71 IAPA (23.9%) and 10/49 CAPA (20.4%). Kaplan–Meier curves showed significantly higher 90-day mortality for IPA patients overall (p = 0.032), whereas mortality did not differ between CAPA and IAPA patients. Radiological findings showed differences between IAPA and CAPA, with a higher proportion of features suggestive of IPA during IAPA. Lastly, a wide proportion of IPA patients had low plasma voriconazole concentrations with a higher delay to reach concentrations > 2 mg/L in CAPA vs. IAPA patients (p = 0.045). Severe COVID-19 and influenza patients appeared very similar in terms of prevalence of IPA and outcome. The dramatic consequences on the patients’ prognosis emphasize the need for a better awareness in these particular populations.