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COVID-19 is characterized by respiratory symptoms of various severities, ranging from mild upper respiratory signs to acute respiratory failure/acute respiratory distress syndrome associated with a high mortality rate. However, the pathophysiology of the disease is largely unknown. Shotgun metagenomics from nasopharyngeal swabs were used to characterize the genomic, metagenomic and transcriptomic features of patients from the first pandemic wave with various forms of COVID-19, including outpatients, patients hospitalized not requiring intensive care, and patients in the intensive care unit, to identify viral and/or host factors associated with the most severe forms of the disease. Neither the genetic characteristics of SARS-CoV-2, nor the detection of bacteria, viruses, fungi or parasites were associated with the severity of pulmonary disease. Severe pneumonia was associated with overexpression of cytokine transcripts activating the CXCR2 pathway, whereas patients with benign disease presented with a T helper “Th1-Th17” profile. The latter profile was associated with female gender and a lower mortality rate. Our findings indicate that the most severe cases of COVID-19 are characterized by the presence of overactive immune cells resulting in neutrophil pulmonary infiltration which, in turn, could enhance the inflammatory response and prolong tissue damage. These findings make CXCR2 antagonists, in particular IL-8 antagonists, promising candidates for the treatment of patients with severe COVID-19.

Background In France, the lifting of the lockdown implemented to control the COVID-19 first wave in 2020 was followed by a reinforced contact-tracing (CT) strategy for the early detection of cases and transmission chains. We developed a reporting system of clusters defined as at least three COVID-19 cases, within seven days and belonging to the same community or having participated in the same gathering, whether they know each other or not. The aim of this study was to describe the typology and criticality of clusters reported between the two lockdowns in France to guide future action prioritisation. Methods In this study we describe the typology and criticality of COVID-19 clusters between the two lockdowns implemented in France (between May and end of October 2020). Clusters were registered in a national database named “MONIC” (MONItoring des Clusters), established in May 2020. This surveillance system identified the most affected communities in a timely manner. A level of criticality was defined for each cluster to take into consideration the risk of spreading within and outside the community of occurrence, and the health impact within the community. We compared the level of criticality according to the type of community in which the cluster occurred using Pearson’s chi-square tests. Results A total of 7236 clusters were reported over the study period, particularly in occupational environment (25.1%, n  = 1813), elderly care structures (21.9%, n  = 1586), and educational establishments (15.9%, n  = 1154). We show a shift over time of the most affected communities in terms of number of clusters. Clusters reported in occupational environment and the personal sphere had increased during summer while clusters reported in educational environment increased after the start of the school year. This trend mirrors change of transmission pattern overtime according to social contacts. Among all reported clusters, 43.1% had a high level of criticality with significant differences between communities ( p  < 0.0001). A majority of clusters had a high level of criticality in elderly care structures (82.2%), in disability care centres (56.6%), and health care facilities (51.7%). Conclusion These results highlight the importance of targeting public health action based on timely sustained investigations, testing capacity and targeted awareness campaigns. The emergence of new SARS-CoV-2 variants strengthen these public health recommendations and the need for rapid and prioritise vaccination campaigns.