The emergence, spread and vanishing of a French SARS-CoV-2 variant exemplifies the fate of RNA virus epidemics and obeys the Mistigri rule.

The nature and dynamics of mutations associated with the emergence, spread, and vanishing of SARS-CoV-2 variants causing successive waves are complex. We determined the kinetics of the most common French variant ("Marseille-4") for 10 months since its onset in July 2020. Here, we analyzed and classified into subvariants and lineages 7453 genomes obtained by next-generation sequencing. We identified two subvariants, Marseille-4A, which contains 22 different lineages of at least 50 genomes, and Marseille-4B. Their average lifetime was 4.1 ± 1.4 months, during which 4.1 ± 2.6 mutations accumulated. Growth rate was 0.079 ± 0.045, varying from 0.010 to 0.173. Most of the lineages exhibited a bell-shaped distribution. Several beneficial mutations at unpredicted sites initiated a new outbreak, while the accumulation of other mutations resulted in more viral heterogenicity, increased diversity and vanishing of the lineages. Marseille-4B emerged when the other Marseille-4 lineages vanished. Its ORF8 gene was knocked out by a stop codon, as reported in SARS-CoV-2 of mink and in the Alpha variant. This subvariant was associated with increased hospitalization and death rates, suggesting that ORF8 is a nonvirulence gene. We speculate that the observed heterogenicity of a lineage may predict the end of the outbreak.

Investigation of a COVID-19 outbreak on the Charles de Gaulle aircraft carrier, March to April 2020: a retrospective cohort study.

BackgroundSARS-CoV-2 emergence was a threat for armed forces. A COVID-19 outbreak occurred on the French aircraft carrier Charles de Gaulle from mid-March to mid-April 2020.AimTo understand how the virus was introduced, circulated then stopped circulation, risk factors for infection and severity, and effectiveness of preventive measures.MethodsWe considered the entire crew as a cohort and collected personal, clinical, biological, and epidemiological data. We performed viral genome sequencing and searched for SARS-CoV-2 in the environment.ResultsThe attack rate was 65% (1,148/1,767); 1,568 (89%) were included. The male:female ratio was 6.9, and median age was 29 years (IQR: 24-36). We examined four clinical profiles: asymptomatic (13.0%), non-specific symptomatic (8.1%), specific symptomatic (76.3%), and severe (i.e. requiring oxygen therapy, 2.6%). Active smoking was not associated with severe COVID-19; age and obesity were risk factors. The instantaneous reproduction rate (Rt) and viral sequencing suggested several introductions of the virus with 4 of 5 introduced strains from within France, with an acceleration of Rt when lifting preventive measures. Physical distancing prevented infection (adjusted OR: 0.55; 95% CI: 0.40-0.76). Transmission may have stopped when the proportion of infected personnel was large enough to prevent circulation (65%; 95% CI: 62-68).ConclusionNon-specific clinical pictures of COVID-19 delayed detection of the outbreak. The lack of an isolation ward made it difficult to manage transmission; the outbreak spread until a protective threshold was reached. Physical distancing was effective when applied. Early surveillance with adapted prevention measures should prevent such an outbreak.

Early combination therapy with hydroxychloroquine and azithromycin reduces mortality in 10,429 COVID-19 outpatients.

We evaluated the age-specific mortality of unselected adult outpatients infected with SARS-CoV-2 treated early in a dedicated COVID-19 day hospital and we assessed whether the use of hydroxychloroquine (HCQ) + azithromycin (AZ) was associated with improved survival in this cohort. A retrospective monocentric cohort study was conducted in the day hospital of our center from March to December 2020 in adults with PCR-proven infection who were treated as outpatients with a standardized protocol. The primary endpoint was 6-week mortality, and secondary endpoints were transfer to the intensive care unit and hospitalization rate. Among 10,429 patients (median age, 45 [IQR 32-57] years; 5597 [53.7%] women), 16 died (0.15%). The infection fatality rate was 0.06% among the 8315 patients treated with HCQ+AZ. No deaths occurred among the 8414 patients younger than 60 years. Older age and male sex were associated with a higher risk of death, ICU transfer, and hospitalization. Treatment with HCQ+AZ (0.17 [0.06-0.48]) was associated with a lower risk of death, independently of age, sex and epidemic period. Meta-analysis evidenced consistency with 4 previous outpatient studies (32,124 patients-Odds ratio 0.31 [0.20-0.47], I2 = 0%). Early ambulatory treatment of COVID-19 with HCQ+AZ as a standard of care is associated with very low mortality, and HCQ+AZ improve COVID-19 survival compared to other regimens.

Using MALDI-TOF spectra in epidemiological surveillance for the detection of bacterial subgroups with a possible epidemic potential.

BACKGROUND: For the purpose of epidemiological surveillance, the Hospital University Institute Méditerranée infection has implemented since 2013 a system named MIDaS, based on the systematic collection of routine activity materials, including MALDI-TOF spectra, and results. The objective of this paper is to present the pipeline we use for processing MALDI-TOF spectra during epidemiological surveillance in order to disclose proteinic cues that may suggest the existence of epidemic processes in complement of incidence surveillance. It is illustrated by the analysis of an alarm observed for Streptococcus pneumoniae. METHODS: The MALDI-TOF spectra analysis process looks for the existence of clusters of spectra characterized by a double time and proteinic close proximity. This process relies on several specific methods aiming at contrasting and clustering the spectra, presenting graphically the results for an easy epidemiological interpretation, and for determining the discriminating spectra peaks with their possible identification using reference databases. RESULTS: The use of this pipeline in the case of an alarm issued for Streptococcus pneumoniae has made it possible to reveal a cluster of spectra with close proteinic and temporal distances, characterized by the presence of three discriminant peaks (5228.8, 5917.8, and 8974.3 m/z) and the absence of peak 4996.9 m/z. A further investigation on UniProt KB showed that peak 5228.8 is possibly an OxaA protein and that the absent peak may be a transposase. CONCLUSION: This example shows this pipeline may support a quasi-real time identification and characterization of clusters that provide essential information on a potentially epidemic situation. It brings valuable information for epidemiological sensemaking and for deciding on the continuation of the epidemiological investigation, in particular the involving of additional costly resources to confirm or invalidate the alarm. CLINICAL TRIALS REGISTRATION: NCT03626987.

Using decision fusion methods to improve outbreak detection in disease surveillance.

BACKGROUND: When outbreak detection algorithms (ODAs) are considered individually, the task of outbreak detection can be seen as a classification problem and the ODA as a sensor providing a binary decision (outbreak yes or no) for each day of surveillance. When they are considered jointly (in cases where several ODAs analyze the same surveillance signal), the outbreak detection problem should be treated as a decision fusion (DF) problem of multiple sensors. METHODS: This study evaluated the benefit for a decisions support system of using DF methods (fusing multiple ODA decisions) compared to using a single method of outbreak detection. For each day, we merged the decisions of six ODAs using 5 DF methods (two voting methods, logistic regression, CART and Bayesian network - BN). Classical metrics of accuracy, prediction and timelines were used during the evaluation steps. RESULTS: In our results, we observed the greatest gain (77%) in positive predictive value compared to the best ODA if we used DF methods with a learning step (BN, logistic regression, and CART). CONCLUSIONS: To identify disease outbreaks in systems using several ODAs to analyze surveillance data, we recommend using a DF method based on a Bayesian network. This method is at least equivalent to the best of the algorithms considered, regardless of the situation faced by the system. For those less familiar with this kind of technique, we propose that logistic regression be used when a training dataset is available.

Correction to: Using decision fusion methods to improve outbreak detection in disease surveillance.

Following publication of the original article [1], the authors reported that one of the authors' names is spelled incorrectly.

How mass spectrometric approaches applied to bacterial identification have revolutionized the study of human gut microbiota.

INTRODUCTION: Describing the human hut gut microbiota is one the most exciting challenges of the 21st century. Currently, high-throughput sequencing methods are considered as the gold standard for this purpose, however, they suffer from several drawbacks, including their inability to detect minority populations. The advent of mass-spectrometric (MS) approaches to identify cultured bacteria in clinical microbiology enabled the creation of the culturomics approach, which aims to establish a comprehensive repertoire of cultured prokaryotes from human specimens using extensive culture conditions. Areas covered: This review first underlines how mass spectrometric approaches have revolutionized clinical microbiology. It then highlights the contribution of MS-based methods to culturomics studies, paying particular attention to the extension of the human gut microbiota repertoire through the discovery of new bacterial species. Expert commentary: MS-based approaches have enabled cultivation methods to be resuscitated to study the human gut microbiota and thus to fill in the blanks left by high-throughput sequencing methods in terms of culturing minority populations. Continued efforts to recover new taxa using culture methods, combined with their rapid implementation in genomic databases, would allow for an exhaustive analysis of the gut microbiota through the use of a comprehensive approach.

Emergence of uncommon HIV-1 non-B subtypes and circulating recombinant forms and trends in transmission of antiretroviral drug resistance in patients with primary infection during the 2013-2015 period in Marseille, Southeastern France.

Primary HIV-1 infections (PHI) with non-B subtypes are increasing in developed countries while transmission of HIV-1 harboring antiretroviral resistance-associated mutations (RAMs) remains a concern. This study assessed non-B HIV-1 subtypes and RAMs prevalence among patients with PHI in university hospitals of Marseille, Southeastern France, in 2005-2015 (11 years). HIV-1 sequences were obtained by in-house protocols from 115 patients with PHI, including 38 for the 2013-2015 period. On the basis of the phylogenetic analysis of the reverse transcriptase region, non-B subtypes were identified in 31% of these patients. They included 3 different subtypes (3A, 1C, 4F), 23 circulating recombinant forms (CRFs) (CRF02_AG, best BLAST hits being CRF 36_cpx and CRF30 in 7 and 1 cases, respectively), and 5 unclassified sequences (U). Non-B subtypes proportion increased significantly, particularly in 2011-2013 vs in 2005-2010 (P = .03). CRF02_AG viruses largely predominated in 2005-2013 whereas atypical strains more difficult to classify and undetermined recombinants emerged recently (2014-2015). The prevalence of protease, nucleos(t)ide reverse transcriptase, and first-generation nonnucleoside reverse transcriptase inhibitors-associated RAMs were 1.7% (World Health Organization [WHO] list, 2009/2.6% International AIDS Society [IAS] list, 2017), 5.2%/4.3%, and 5.2%/5.2%, respectively. Etravirine/rilpivirine-associated RAM (IAS) prevalence was 4.3%. Men who have sex with men (MSM) were more frequently infected with drug-resistant viruses than other patients (26% vs 7%; P = .011). The recent increase of these rare HIV-1 strains and the spread of drug-resistant HIV-1 among MSM in Southeastern France might be considered when implementing prevention strategies and starting therapies.

Evaluating the Clinical Burden and Mortality Attributable to Antibiotic Resistance: The Disparity of Empirical Data and Simple Model Estimations.

Given the proliferation of cataclysmic predictions about antibiotic resistance, cases of which are estimated to amount to 12500 per year in France, we herein decided to compare the empirical clinical microbiology data from our institution with estimates and predictions from 10 major international scientific articles and reports. The analysis of 7 years of antibiotic resistance data from 10 bacterial species and genera of clinical interest from our institution identified no deaths that were directly attributable to extremely drug-resistant bacteria. By comparing our observations to the 10 articles and reports studied herein, we concluded that their results lack empirical data. Interventions are urgently needed to significantly reduce both mortality and the healthcare costs associated with bacterial infections, including the implementation of local and national laboratory data-based surveillance systems for the routine surveillance of antibiotic resistance that would be helpful for a better understanding of how to manage antibiotic-resistant bacteria in the future.

Implementation and Initial Analysis of a Laboratory-Based Weekly Biosurveillance System, Provence-Alpes-Côte d'Azur, France.

We describe the implementation of an automated infectious disease surveillance system that uses data collected from 210 microbiologic laboratories throughout the Provence-Alpes-Côte d'Azur region in France. Each week, these facilities report bacterial species that have been isolated from patients in their area. An alarm is triggered whenever the case count for a bacterial species infection exceeds 2 SDs of the historical mean for that species at the participating laboratory. At its inception in July 2013, the system monitored 611 bacterial species. During July 1, 2013-March 20, 2016, weekly analyses of incoming surveillance data generated 34 alarms signaling possible infectious disease outbreaks; after investigation, 14 (41%) of these alarms resulted in health alerts declared by the regional health authority. We are currently improving the system by developing an Internet-based surveillance platform and extending our surveillance to include more laboratories in the region.

Outbreak definition by change point analysis: a tool for public health decision?

BACKGROUND: Most studies of epidemic detection focus on their start and rarely on the whole signal or the end of the epidemic. In some cases, it may be necessary to retrospectively identify outbreak signals from surveillance data. Our study aims at evaluating the ability of change point analysis (CPA) methods to locate the whole disease outbreak signal. We will compare our approach with the results coming from experts' signal inspections, considered as the gold standard method. METHODS: We simulated 840 time series, each of which includes an epidemic-free baseline (7 options) and a type of epidemic (4 options). We tested the ability of 4 CPA methods (Max-likelihood, Kruskall-Wallis, Kernel, Bayesian) methods and expert inspection to identify the simulated outbreaks. We evaluated the performances using metrics including delay, accuracy, bias, sensitivity, specificity and Bayesian probability of correct classification (PCC). RESULTS: A minimum of 15 h was required for experts for analyzing the 840 curves and a maximum of 25 min for a CPA algorithm. The Kernel algorithm was the most effective overall in terms of accuracy, bias and global decision (PCC = 0.904), compared to PCC of 0.848 for human expert review. CONCLUSIONS: For the aim of retrospectively identifying the start and end of a disease outbreak, in the absence of human resources available to do this work, we recommend using the Kernel change point model. And in case of experts' availability, we also suggest to supplement the Human expertise with a CPA, especially when the signal noise difference is below 0.

Real-Time Microbiology Laboratory Surveillance System to Detect Abnormal Events and Emerging Infections, Marseille, France.

Infectious diseases are a major threat to humanity, and accurate surveillance is essential. We describe how to implement a laboratory data-based surveillance system in a clinical microbiology laboratory. Two historical Microsoft Excel databases were implemented. The data were then sorted and used to execute the following 2 surveillance systems in Excel: the Bacterial real-time Laboratory-based Surveillance System (BALYSES) for monitoring the number of patients infected with bacterial species isolated at least once in our laboratory during the study periodl and the Marseille Antibiotic Resistance Surveillance System (MARSS), which surveys the primary ß-lactam resistance phenotypes for 15 selected bacterial species. The first historical database contained 174,853 identifications of bacteria, and the second contained 12,062 results of antibiotic susceptibility testing. From May 21, 2013, through June 4, 2014, BALYSES and MARSS enabled the detection of 52 abnormal events for 24 bacterial species, leading to 19 official reports. This system is currently being refined and improved.

Role of SARS-CoV-2 mutations in the evolution of the COVID-19 pandemic.

OBJECTIVES: The SARS-CoV-2 pandemic and large-scale genomic surveillance provided an exceptional opportunity to analyze mutations that appeared over three years in viral genomes. Here we studied mutations and their epidemic consequences for SARS-CoV-2 genomes from our center. METHODS: We analyzed 61,397 SARS-CoV-2 genomes we sequenced from respiratory samples for genomic surveillance. Mutations frequencies were calculated using Nextclade, Microsoft Excel, and an in-house Python script. RESULTS: A total of 22,225 nucleotide mutations were identified, 220 (1.0%) being each at the root of ≥836 genomes, classifying mutations as 'hyperfertile'. Two seeded the European pandemic: P323L in RNA polymerase, associated with an increased mutation rate, and D614G in spike that improved fitness. Most 'hyperfertile' mutations occurred in areas not predicted with increased virulence. Their mean number was 8±6 (0-22) per 1000 nucleotides per gene. They were 3.7-times more frequent in accessory than informational genes (13.8 versus 3.7/1000 nucleotides). Particularly, they were 4.1-times more frequent in ORF8 than in the RNA polymerase gene. Interestingly, stop codons were present in 97 positions, almost only in accessory genes, including ORF8 (21/100 codons). CONCLUSIONS: most 'hyperfertile' mutations did not predict emergence of a new epidemic, and some were stop codons indicating the existence of so-named 'non-virulence' genes.

The 2009 A(H1N1) influenza pandemic in the French Armed Forces: evaluation of three surveillance systems.

BACKGROUND: The French military forces had to modify their epidemiological surveillance systems at the time of the 2009 A(H1N1) influenza pandemic. The aim of this article was to present an evaluation of the different systems used. METHODS: Two influenza surveillance systems are usually used in the French forces: one permanent (Surveillance épidémiologique des armées or SEA) and one seasonal (Système militaire d'observation de la grippe or SMOG). The pandemic required the implementation of a daily surveillance system (Surveillance quotidienne--SQ), which aimed to monitor disrupted activity owing to 2009 A(H1N1) influenza. The qualitative evaluation of these three systems during the period from September 2009 to February 2010 was performed using 11 criteria based on the list defined by Centers for Disease Control and Prevention of Atlanta. RESULTS: Although it included only 30 sentinel units vs. 320 for the other systems, the SMOG system was the best-performing system in terms of relevance, feasibility, efficacy, quality of data, usefulness, acceptability, efficiency and cost/benefits/costs ratio. The SQ proved very expensive in terms of logistics. CONCLUSION: The SQ did not bring any significant advantage compared with the weekly surveillance schemes. In the eventuality of another similar episode, influenza surveillance could be significantly improved by using the SMOG system extended to more units for better geographical coverage.

COVID-19 Is a Confounder of Increased Candida Airway Colonisation.

An increased incidence of invasive fungal infection was reported in SARS-CoV-2-infected patients hospitalised in the intensive care unit. However, the impact of COVID-19 on Candida airway colonisation has not yet been assessed. This study aimed to test the impact of several factors on Candida airway colonisation, including SARS-CoV-2 infection. We conducted a two-pronged monocentric retrospective study. First, we analysed the prevalence of positive yeast culture in respiratory samples obtained from 23 departments of the University Hospital of Marseille between 1 January 2018 and 31 March 2022. We then conducted a case-control study, comparing patients with documented Candida airway colonisation to two control groups. We observed an increase in the prevalence of yeast isolation over the study period. The case-control study included 300 patients. In the multivariate logistic regression, diabetes, mechanical ventilation, length of stay in the hospital, invasive fungal disease, and the use of antibacterials were independently associated with Candida airway colonisation. The association of SARS-CoV-2 infection with an increased risk of Candida airway colonisation is likely to be a consequence of confounding factors. Nevertheless, we found the length of stay in the hospital, mechanical ventilation, diabetes, and the use of antibacterials to be statistically significant independent risk factors of Candida airway colonisation.

Control of common viral epidemics but not of SARS-CoV-2 through the application of hygiene and distancing measures.

BACKGROUND: We systematically survey respiratory and gastrointestinal infections of viral origin in samples sent to our university hospital institute in Marseille, southern France. Here, we evaluated whether the measures implemented to fight COVID-19 had an effect on the dynamics of viral respiratory or gastrointestinal infections. METHODS: We analysed PCR performed and positive for the diagnoses of viral respiratory and gastrointestinal infections over five years (January 2017-February 2021). Data were collected from our epidemiological surveillance system (MIDaS). Dates and contents of French measures against SARS-CoV-2 were collected from: https://www.gouvernement.fr/info-coronavirus/les-actions-du-gouvernement. RESULTS: Over the 2017-2021 period, 990,364 analyses were carried out for respiratory infections not including SARS-CoV-2, 510,671 for SARS-CoV-2 and 27,719 for gastrointestinal infections. During winter 2020-2021, when the most restrictive lockdown measures were in place in France, a marked decrease of infections with influenza viruses (one case versus 1,839-1,850 cases during 2017-2020 cold seasons) and with the RSV (56 cases versus 988-1,196 cases during 2017-2020 cold seasons) was observed, demonstrating the relative effectiveness of these measures on their occurrence. SARS-CoV-2 incidence seemed far less affected. Rhinoviruses, parainfluenza 3 virus, and the coronavirus NL63 remained at comparable levels. Also, the norovirus winter season positivity rates decreased continuously and significantly over time from 9.3% in 2017-2018 to 2.0% in 2020-2021. CONCLUSION: The measures taken to control COVID-19 were effective against lower respiratory tract infections viruses and gastroenteritis agents, but not on the agents of the common winter cold and SARS-CoV-2. This suggests that more specific measures to prevent COVID-19 and upper respiratory tract infections need to be discovered to limit the spread of this epidemic.