Proposal of a framework for evaluating military surveillance systems for early detection of outbreaks on duty areas

<p>Abstract</p> <p>Background</p> <p>In recent years a wide variety of epidemiological surveillance systems have been developed to provide early identification of outbreaks of infectious disease. Each system has had its own strengths and weaknesses. In 2002 a Working Group of the Centers for Disease Control and Prevention (CDC) produced a framework for evaluation, which proved suitable for many public health surveillance systems. However this did not easily adapt to the military setting, where by necessity a variety of different parameters are assessed, different constraints placed on the systems, and different objectives required. This paper describes a proposed framework for evaluation of military syndromic surveillance systems designed to detect outbreaks of disease on operational deployments.</p> <p>Methods</p> <p>The new framework described in this paper was developed from the cumulative experience of British and French military syndromic surveillance systems. The methods included a general assessment framework (CDC), followed by more specific methods of conducting evaluation. These included Knowledge/Attitude/Practice surveys (KAP surveys), technical audits, ergonomic studies, simulations and multi-national exercises. A variety of military constraints required integration into the evaluation. Examples of these include the variability of geographical conditions in the field, deployment to areas without prior knowledge of naturally-occurring disease patterns, the differences in field sanitation between locations and over the length of deployment, the mobility of military forces, turnover of personnel, continuity of surveillance across different locations, integration with surveillance systems from other nations working alongside each other, compatibility with non-medical information systems, and security.</p> <p>Results</p> <p>A framework for evaluation has been developed that can be used for military surveillance systems in a staged manner consisting of initial, intermediate and final evaluations. For each stage of the process parameters for assessment have been defined and methods identified.</p> <p>Conclusion</p> <p>The combined experiences of French and British syndromic surveillance systems developed for use in deployed military forces has allowed the development of a specific evaluation framework. The tool is suitable for use by all nations who wish to evaluate syndromic surveillance in their own military forces. It could also be useful for civilian mobile systems or for national security surveillance systems.</p

STEEL: A Spatio-Temporal Extended Event Language for Tracking Epidemic Spread from Outbreak Reports

We propose a Spatio-Temporal Extended Event Language (STEEL) for representing and reasoning about events that are described in outbreak reports. This language is an extension of the Event Calculus based on mereotopological relationships and structured conglomeration of events, in which time is replaced with spatiotemporal location. It allows representing and building aggregates of events according to the spatiotemporal location of their occurrence. In a proof a concept study, we aimed at comparing the performances of an experimental implementation in Prolog of this language with 3 human experts during a question-answering task on a trial corpus of 35 outbreak reports. This experiment showed experts&apos; agreement with the system&apos;s responses

Hydroxychloroquine Failure: The End Does Not Justify the Means

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

International audienceIntroduction: Describing the human hut gut microbiota is one the most exciting challenges of the 21(st) 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

Bacterial Cocktail to Treat Clostridium difficile Infection: Primum Non Nocere

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Insights Into Subspecies Discrimination Potentiality From Bacteria MALDI-TOF Mass Spectra by Using Data Mining and Diversity Studies

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

Building test data from real outbreaks for evaluating detection algorithms.

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

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

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

International audienceGiven the proliferation of cataclysmic predictions about antibiotic resistance, cases of which are estimated to amount to 12 500 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

Tobacco-smoking-related prevalence of methanogens in the oral fluid microbiota

International audienceThe oral fluid microbiome comprises an important bacterial diversity, yet the presence of archaea has not been reported so far. In order to quest for the presence of methanogenic archaea (methanogens) in oral fluid, we used a polyphasic approach including PCR-sequencing detection, microscopic observation by fluorescence in-situ hybridization, isolation and culture, molecular identification and genotyping of methanogens in 200 oral fluid specimens. In the presence of negative controls, 64/200 (32%) prospectively analysed oral fluid specimens were PCR-positive for methanogens, all identified as Methanobrevibacter oralis by sequencing. Further, fluorescence in-situ hybridization detected methanogens in 19/48 (39.6%) investigated specimens; with morphology suggesting M. oralis in 10 cases and co-infecting Methanobrevibacter smithii in nine cases. M. oralis was cultured from 46/64 (71.8%) PCR-positive specimens and none of PCR-negative specimens; and one M. smithii isolate was co-cultured with M. oralis in one specimen. Multispacer Sequence Typing found one M. oralis genotype per specimen and a total of five different genotypes with 19/46 (41%) of isolates all belonging to spacer-type four. Statistical analyses showed a significant correlation between the PCR-detection of methanogens in oral fluid and tobacco smoking. These data indicate that M. oralis and M. smithii are oral fluid-borne methanogens in tobacco smokers. Both methanogens could be transmitted during intimate contacts such as mother-to-child contacts and kissing