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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.

Trichomonas tenax, an anaerobic protist difficult to cultivate with an unreliable molecular identification, has been suspected of involvement in periodontitis, a multifactorial inflammatory dental disease affecting the soft tissue and bone of periodontium. A cohort of 106 periodontitis patients classified by stages of severity and 85 healthy adult control patients was constituted. An efficient culture protocol, a new identification tool by real-time qPCR of T. tenax and a Multi-Locus Sequence Typing system (MLST) based on T. tenax NIH4 reference strain were created. Fifty-three strains of Trichomonas sp. were obtained from periodontal samples. 37/106 (34.90%) T. tenax from patients with periodontitis and 16/85 (18.80%°) T. tenax from control patients were detected by culture (p = 0.018). Sixty of the 191 samples were tested positive for T. tenax by qPCR, 24/85 (28%) controls and 36/106 (34%) periodontitis patients (p = 0.089). By combining both results, 45/106 (42.5%) patients were positive by culture and/or PCR, as compared to 24/85 (28.2%) controls (p = 0.042). A link was established between the carriage in patients of Trichomonas tenax and the severity of the disease. Genotyping demonstrates the presence of strain diversity with three major different clusters and a relation between disease strains and the periodontitis severity (p

Benchmarking surveillance systems requires realistic simulations of disease outbreaks. However, obtaining these data in sufficient quantity, with a realistic shape and covering a sufficient range of agents, size and duration, is known to be very difficult. The dataset of outbreak signals generated should reflect the likely distribution of authentic situations faced by the surveillance system, including very unlikely outbreak signals. We propose and evaluate a new approach based on the use of historical outbreak data to simulate tailored outbreak signals. The method relies on a homothetic transformation of the historical distribution followed by resampling processes (Binomial, Inverse Transform Sampling Method-ITSM, Metropolis-Hasting Random Walk, Metropolis-Hasting Independent, Gibbs Sampler, Hybrid Gibbs Sampler). We carried out an analysis to identify the most important input parameters for simulation quality and to evaluate performance for each of the resampling algorithms. Our analysis confirms the influence of the type of algorithm used and simulation parameters (i.e. days, number of cases, outbreak shape, overall scale factor) on the results. We show that, regardless of the outbreaks, algorithms and metrics chosen for the evaluation, simulation quality decreased with the increase in the number of days simulated and increased with the number of cases simulated. Simulating outbreaks with fewer cases than days of duration (i.e. overall scale factor less than 1) resulted in an important loss of information during the simulation. We found that Gibbs sampling with a shrinkage procedure provides a good balance between accuracy and data dependency. If dependency is of little importance, binomial and ITSM methods are accurate. Given the constraint of keeping the simulation within a range of plausible epidemiological curves faced by the surveillance system, our study confirms that our approach can be used to generate a large spectrum of outbreak signals.

Abstract 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.

Objectives: The SARS-CoV-2 epidemic presents a poorly understood epidemiological cycle. We aimed to compare the age and weekly distributions of the five human coronaviruses, including SARS-CoV-2, that circulated in southeastern France. Methods: We analyzed all available diagnoses of respiratory viruses, including SARS-CoV-2, performed between 09/2013 and 05/2020 at the University Hospital Institute Méditerranée Infection in Marseille, southeastern France. Results: For SARS-CoV-2, positive children

Objectives: Epidemiological surveillance is one critical approach to estimate and fight the burden of antibiotic resistance (AR). Here we summarise the characteristics of surveillance systems devoted to the surveillance of AR worldwide and published in the literature. Methods: We performed a systematic review of the literature available on PubMed from January 2007 to July 2019 (12.5 years). The keywords (‘surveillance system’ OR ‘laboratory-based surveillance’ OR ‘syndromic surveillance’ OR ‘sentinel surveillance’ OR ‘integrated surveillance’ OR ‘population-based surveillance’) AND (‘antibiotic resistance’ OR ‘antimicrobial resistance’) were used. This research was completed with AR monitoring systems available on websites. Results: We identified 71 AR surveillance systems described by 90 publications from 35 countries, including 64 (90.1%) national and 7 (9.9%) multinational surveillance systems. Two regions accounted for ∼72% of systems: European region (37; 52.1%) and Region of the Americas (14; 19.7%). Fifty-three focused on AR surveillance in humans, 12 studied both humans and animals, and 6 focused only on animals. The two most common bacterial species reported were Staphylococcus aureus (42; 59.2%) and Escherichia coli (39; 54.9%). Of the 71 AR surveillance systems, 20 (28.2%) used prevalence as an indicator, 3 (4.2%) used incidence and 7 (9.9%) used both. Methicillin-resistant S. aureus (MRSA), vancomycin-resistant Enterococcus spp., S. aureus and Streptococcus pneumoniae, penicillin-resistant S. pneumoniae, and extended-spectrum β-lactamase (ESBL)-producing and carbapenem-resistant E. coli and Klebsiella pneumoniae were monitored. Conclusions: Our results showed heterogeneous surveillance systems. A ‘One Health’ approach is needed to monitor AR, with reference to the WHO Global Action Plan.

Objectives: We investigated possible COVID-19 epidemic clusters and their common sources of exposure that led to a sudden increase in the incidence of COVID-19 in the Jewish community of Marseille between March 15 and March 20, 2020. Methods: All data were generated as part of routine work at Marseille university hospitals. Biological diagnoses were made by RT-PCR testing. A telephone survey of families in which a laboratory confirmed case was diagnosed, was conducted to determine possible exposure events. Results: As of March 30, 2020, 63 patients were linked to 6 epidemic clusters. The clusters were linked to religious and social activities: a ski trip, organized meals for the Purim Jewish celebration in community and family settings on March 10, a religious service and a charity gala. Notably, 40% of the patients were infected by index patients during the presymptomatic period, which was 2.5 days before symptom onset. When considering household members, all 12 patients who tested negative and who did not develop any relevant clinical symptoms compatible with COVID-19 were 1 - 16 years of age. The clinical attack rate (symptoms compatible with COVID-19 and biologically confirmed by PCR) in adults was 85% compared to 26% in children. Conclusions: Family and community gatherings for the Purim celebration probably accelerated the spread of COVID-19 in the Marseille Jewish community, leading to multiple epidemic clusters. This investigation of family clusters suggested that all close contacts of patients with confirmed COVID-19 who were not infected were children.

Abstract The burden of antibiotic resistance is currently estimated by mathematical modeling, without real count of resistance to key antibiotics. Here we report the real rate of resistance to key antibiotics in bacteria isolated from humans during a 5 years period in a large area in southeast in France. We conducted a retrospective study on antibiotic susceptibility of 539,107 clinical strains isolated from hospital and private laboratories in south of France area from January 2014 to January 2019. The resistance rate to key antibiotics as well as the proportion of bacteria classified as Difficult-to-Treat (DTR) were determined and compared with the Mann–Whitney U test, the χ2 test or the Fisher’s exact test. Among 539,037 isolates, we did not observe any significant increase or decrease in resistance to key antibiotics for 5 years, (oxacillin resistance in Staphylococcus aureus, carbapenem resistance in enterobacteria and Pseudomonas aeruginosa and 3rd generation cephalosporin resistance in Escherichia coli and Klebsiella pneumoniae). However, we observed a significant decrease in imipenem resistance for Acinetobacter baumannii from 2014 to 2018 (24.19–12.27%; p = 0.005) and a significant increase of ceftriaxone resistance in Klebsiella pneumoniae (9.9–24.03%; p = 0.001) and Enterobacter cloacae (24.05–42.05%; p = 0.004). Of these 539,037 isolates, 1604 (0.3%) had a DTR phenotype. Over a 5-year period, we did not observe a burden of AR in our region despite a high rate of antibiotic consumption in our country. These results highlight the need for implementation of real-time AR surveillance systems which use factual data.

After the end of the first epidemic episode of SARS-CoV-2 infections, as cases began to rise again during the summer of 2020, we at IHU Méditerranée Infection in Marseille, France, intensified the genomic surveillance of SARS-CoV-2, and described the first viral variants. In this study, we compared the incidence curves of SARS-CoV-2-associated deaths in different countries and reported the classification of SARS-CoV-2 variants detected in our institute, as well as the kinetics and sources of the infections. We used mortality collected from a COVID-19 data repository for 221 countries. Viral variants were defined based on ≥5 hallmark mutations along the whole genome shared by ≥30 genomes. SARS-CoV-2 genotype was determined for 24,181 patients using next-generation genome and gene sequencing (in 47 and 11% of cases, respectively) or variant-specific qPCR (in 42% of cases). Sixteen variants were identified by analyzing viral genomes from 9,788 SARS-CoV-2-diagnosed patients. Our data show that since the first SARS-CoV-2 epidemic episode in Marseille, importation through travel from abroad was documented for seven of the new variants. In addition, for the B.1.160 variant of Pangolin classification (a.k.a. Marseille-4), we suspect transmission from farm minks. In conclusion, we observed that the successive epidemic peaks of SARS-CoV-2 infections are not linked to rebounds of viral genotypes that are already present but to newly introduced variants. We thus suggest that border control is the best mean of combating this type of introduction, and that intensive control of mink farms is also necessary to prevent the emergence of new variants generated in this animal reservoir.

From January 18th to August 13th, 2021, 13,804 unvaccinated and 1,156 patients who had received at least one COVID-19 vaccine dose were tested qPCR-positive for SARS-CoV-2 in our center. Among vaccinated patients, 949, 205 and 2 had received a single, two or three vaccine doses, respectively. Most patients (80.3%) had received the Pfizer-BioNTech vaccine. The SARS-CoV-2 variants infecting vaccinated patients varied over time, reflecting those circulating in the Marseille area, with a predominance of the Marseille-4/20A.EU2 variant from weeks 3 to 6, of the Alpha/20I variant from weeks 7 to 25, and of the Delta/21A variant from week 26. SARS-CoV-2 infection was significantly more likely to occur in the first 13 days post-vaccine injection in those who received a single dose (48.9%) than two doses (27.4%, p< 10–3). Among 161 patients considered as fully vaccinated, i.e., >14 days after the completion of the vaccinal scheme (one dose for Johnson and Johnson and two doses for Pfizer/BioNTech, Moderna and Sputnik vaccines), 10 (6.2%) required hospitalization and four (2.5%) died. Risks of complications increased with age in a nonlinear pattern, with a first breakpoint at 54, 33, and 53 years for death, transfer to ICU, and hospitalization, respectively. Among patients infected by the Delta/21A or Alpha/20I variants, partial or complete vaccination exhibited a protective effect with a risk divided by 3.1 for mortality in patients ≥ 55 years, by 2.8 for ICU transfer in patients ≥ 34 years, and by 1.8 for hospitalization in patients ≥ 54 years. Compared to partial vaccination, complete vaccination provided an even stronger protective effect, confirming effectiveness to prevent severe forms of COVID-19.

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.

Objective: To describe the characteristics of COVID-19 patients seen in March-April and June-August 2020 in Marseille, France with the aim to investigate possible changes in the disease between these two time periods. Methods: Demographics, hospitalization rate, transfer to intensive care unit (ICU), lethality, clinical and biological parameters were investigated. Results: Compared to those seen in March-April, COVID-19 patients seen in June-August were significantly younger (39.2 vs. 45.3 years), more likely to be male (52.9% vs. 45.6%), and less likely to be hospitalized (10.7 vs. 18.0%), to be transferred to ICU (0.9% vs. 1.8%) and to die (0.1% vs. 1.1%). Their mean fibrinogen and D-dimer blood levels were lower (1.0 vs. 1.5 g/L and 0.6 vs. 1.1 μg/mL, respectively). By contrast, their viral load was higher (cycle threshold ≤16 = 5.1% vs. 3.7%). Conclusions: Patients in the two periods did not present marked age and sex differences, but markers of severity were undoubtedly less prevalent in the summer period, associating with a 10 times decrease in the lethality rate.

Respiratory viruses are a major cause of mortality worldwide and in France, where they cause several thousands of deaths every year. University Hospital Institute-Méditerranée Infection performs real-time surveillance of all diagnoses of infections and associated deaths in public hospitals in Marseille, Southeastern France. This study compared mortality associated with diagnoses of respiratory viruses during the colder months of 2018–2019 and 2019–2020 (week 47–week 14). In 2018–2019, 73 patients (0.17% of 42,851 hospitalized patients) died after being diagnosed with a respiratory virus; 40 and 13 deaths occurred in patients diagnosed with influenza A virus and respiratory syncytial virus (RSV), respectively. In 2019–2020, 50 patients (0.10% of 49,043 patients hospitalized) died after being diagnosed with a common respiratory virus; seven and seven deaths occurred in patients diagnosed with influenza A virus and RSV, respectively. Additionally, 55 patients died after being diagnosed with SARS-CoV-2. The proportion of respiratory virus-associated deaths among hospitalized patients was thus significantly lower for common respiratory viruses in 2019–2020 than in 2018–2019 (102 versus 170 per 100,000 hospitalized patients; p = 0.003), primarily as a consequence of a decrease in influenza A virus (–83%) and RSV (–46%)-associated deaths. Overall, the proportion of respiratory virus-associated deaths among hospitalized patients was higher, but not significantly, in 2019–2020 than in 2018–2019 (214 versus 170 per 100,000 hospitalized patients; p = 0.08, Yates-corrected Chi-square test). These findings put into perspective the death burden of SARS-CoV-2 infections in this geographical area.

Abstract 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.

In recent years, and more specifically at the beginning of the COVID-19 crisis, wastewater surveillance has been proposed as a tool to monitor the epidemiology of human viral infections. In the present work, from July to December 2020, the number of copies of SARS-CoV-2 RNA in Marseille’s wastewater was correlated with the number of new positive cases diagnosed in our Institute of Infectious Disease, which tested about 20% of the city’s population. Number of positive cases and number of copies of SARS-CoV-2 RNA in wastewater were significantly correlated (p = 0.013). During the great epidemic peak, from October to December 2020, the curves of virus in the sewers and the curves of positive diagnoses were perfectly superposed. During the summer period, the superposition of curves was less evident as subject to many confounding factors that were discussed. We also tried to correlate the effect of viral circulation in wastewater with containment measures, probably the most unbiased correlation on their potential inflection effect of epidemic curves. Not only is this correlation not obvious, but it also clearly appears that the drop in cases as well as the drop in the viral load in the sewers occur before the containment measures. In fact, this suggests that there are factors that initiate the end of the epidemic peak independently of the containment measure. These factors will therefore need to be explored more deeply in the future.

Bacterial identification at subspecies level is critical in clinical care and epidemiological investigations due to the different epidemic potentialities of a species. For this purpose, matrix-assisted laser desorption ionization – time-of-flight mass spectrometry (MALDI-TOF MS) has been proposed in place of molecular genotyping, but with some result discrepancies. The aim of this work is to methodically mine the expression diversities of MALDI-TOF bacterial species spectra and their possible latent organization in order to evaluate their subspecies specific expression. Peak expression diversities of MALDI-TOF spectra coming from routine identifications have been analyzed using Hill numbers, rarefaction curves, and peak clustering. Some size effect critical thresholds were estimated using change point analyses. We included 167,528 spectra corresponding to 405 species. Species spectra diversities have a broad size-dependent variability, which may be influenced by the kind of sampling. Peak organization is characterized by the presence of a main cluster made of the most frequently co-occurring peaks and around 20 secondary clusters grouping less frequently co-occurring peaks. The 35 most represented species in our sample are distributed in two groups depending on the focusing of their protein synthesis activity on the main cluster or not. Our results may advocate some analogy with genomics studies of bacteria, with a main species-related cluster of co-occurring peaks and several secondary clusters, which may host peaks able to discriminate bacterial subgroups. This systematic study of the expression diversities of MALDI-TOF spectra shows that latent organization of co-occurring peaks supports subspecies discrimination and may explain why studies on MALDI-TOF-based typing exhibit some result divergences.

AIM:The aim of the present study was to assess the feasibility and diagnostic contribution of protein profiling using MALDI-TOF mass spectrometry applied to saliva, gingival crevicular fluid (GCF) and dental plaque from periodontitis and healthy subjects. We hypothesized that rapid routine and blinded MALDI-TOF analysis could accurately classify these three types of samples according to periodontal state. MATERIALS AND METHODS:Unstimulated saliva, GCF and dental plaque, collected from periodontitis subjects and healthy controls, were analyzed by MALDI-TOF MS. Based on the differentially expressed peaks between the two groups, diagnostic decision trees were built for each sample. RESULTS:Among 141 patients (67 periodontitis and 74 healthy controls), the decision trees diagnosed periodontitis with a sensitivity = 70.3% (± 0.211) and a specificity = 77.8% (± 0.165) for saliva, a sensitivity = 79.6% (± 0.188) and a specificity = 75.7% (± 0.195) for GCF, and a sensitivity = 72.1% (± 0.202) and a specificity = 72.2% (± 0.195) for dental plaque. The sensitivity and specificity of the tests were improved to 100% (CI 95% = [0.91;1]) and 100% (CI 95% = [0.92;1]), respectively, when two samples were tested. CONCLUSION:We developed, for the first time, diagnostic tests based on protein profiles of saliva, GCF and dental plaque between periodontitis patients and healthy subjects. When at least 2 of these samples were tested, the best results were obtained.

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.

Blood culture-negative endocarditis (BCNE) remains a diagnostic challenge. In our center, despite a systematic and exhaustive microbiological diagnostics strategy, 22% of patients with BCNE remain without an identified etiology. In an effort to determine the relevance of using Western blot (WB) for the etiological diagnosis of BCNE in patients with early antibiotic use, we developed specific assays for the major infective endocarditis (IE) causative agents, namely, Staphylococcus aureus, Enterococcus faecalis, Streptococcus anginosus, and Streptococcus gallolyticus. Our technique was effective to identify the antigenic profiles of the four tested agents, but cross-reactions with S. aureus and S. anginosus antigens were frequent. A scoring method was developed for the diagnosis of E. faecalis and S. gallolyticus IE using the presence of reactivity to at least two antigenic bands for each bacterium and the positivity to at least one of the Ef300, Ef72, or Ef36 proteic bands for E. faecalis, and positivity for the two Sg75 and Sg97 proteic bands for S. gallolyticus. We tested these diagnostic criteria in a prospective cohort of 363 patients with suspected IE. Immunoblotting for the diagnosis of E. faecalis IE showed a sensitivity of 100% and a specificity of 99%. The positive and negative predictive values were 73 and 100%, respectively. Regarding S. gallolyticus infection, immunoblot had a sensitivity of 100% and a specificity of 95%. However, the positive predictive value was 22%, whereas the predictive negative value was 100%. Using WB, we identified a potential etiological agent in 4 of 14 BCNE cases with no identified pathogen. In conclusion, WB constitutes a promising and helpful method to diagnose E. faecalis or S. gallolyticus IE in patients with early antibiotic uptake and negative blood cultures.

Background: Infectious diseases remain a major public health problem worldwide. Hence, their surveillance is critical. Currently, many surveillance strategies and systems are in use around the world. An inventory of the data, surveillance strategies, and surveillance systems developed worldwide for the surveillance of infectious diseases is presented herein, with emphasis on the role of the microbiology laboratory in surveillance. Methods: The data, strategies, and systems used around the world for the surveillance of infectious diseases and pathogens, along with current issues and trends, were reviewed. Results: Twelve major classes of data were identified on the basis of their timing relative to infection, resources available, and type of surveillance. Two primary strategies were compared: disease-specific surveillance and syndromic surveillance. Finally, 262 systems implemented worldwide for the surveillance of infections were registered and briefly described, with a focus on those based on microbiological data from laboratories. Conclusions: There is currently a wealth of available data on infections, which has been growing with the recent emergence of new technologies. Concurrently with the expansion of computer resources and networks, these data will allow the optimization of real-time detection and notification of infections.

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 β-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.

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

Infectious diseases still represent a major challenge for humanity. In this context, their surveillance is critical. From 2010 to 2016, two Point-Of-Care (POC) laboratories have been successfully implemented in the rural Saloum region of Senegal. In parallel, a homemade syndromic surveillance system called EPIMIC was implemented to monitor infectious diseases using data produced by the POC laboratory of the Timone hospital in Marseille, France. The aim of this study is to describe the steps necessary for implementing EPIMIC using data routinely produced by two POC laboratories (POC-L) established in rural Senegal villages. After improving EPIMIC, we started to monitor the 15 pathogens routinely diagnosed in the two POC-L using the same methodology we used in France. In 5 years, 2,577 deduplicated patients-samples couples from 775 different patients have been tested in the Dielmo and Ndiop POC-L. 739 deduplicated patients-samples couples were found to be positive to at least one of the tested pathogens. The retrospective analysis of the Dielmo and Ndiop POC data with EPIMIC allowed to generate 443 alarms. Since January 2016, 316 deduplicated patients-samples couples collected from 298 different patients were processed in the Niakhar POC laboratory. 56 deduplicated patients-samples couples were found to be positive to at least one of the tested pathogens. The retrospective analysis of the data of the Niakhar POC laboratory with EPIMIC allowed to generate 14 alarms. Although some improvements are still needed, EPIMIC has been successfully spread using data routinely produced by two rural POC-L in Senegal, West Africa.

This paper presents a study that investigated the management of epidemiological alerts. Alert management is a complex, distributed medical activity. It can be compared to the largest category of dynamic environment management. Two activities were examined. First, we analyzed traditional alert management activity with the MAD method (observations and interviews). Second, we analyzed and characterized alert management activities when supported by the computerized ASTER system (Alerte et Surveillance en TEmps Réel, alert and monitoring in real time). The second analysis was conducted with EORCA method, which is used to describe collective and complex activities. The results highlight various modifications to both the cognitive activity and team activities which were due to the use of the ASTER system.