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Faverjon Céline - One of the best experts on this subject based on the ideXlab platform.
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Risk based surveillance for vector-borne diseases in horses : combining multiple sources of evidence to improve decision making
Utrecht University, 2017Co-Authors: Faverjon CélineAbstract:Emerging vector-borne diseases are a growing concern, especially for horse populations, which are at particular risk for disease spread. In general, horses travel widely and frequently and, despite the health and economic impacts of equine diseases, effective health regulations and biosecurity systems to ensure safe equine movements are not always in place. The present work proposes to improve the surveillance of vector-borne diseases in horses through the use of different approaches that assess the probability of occurrence of a newly introduced epidemic. First, we developed a spatiotemporal quantitative model which combined various probabilities in order to estimate the risk of introduction of African horse sickness and equine encephalosis in The Netherlands and in France. Such combinations of risk provided more a detailed picture of the true risk posed by these pathogens. Second, we assessed syndromic surveillance systems using two approaches: a classical approach with the Alarm Threshold based on the standard error of prediction, and a Bayesian approach based on a likelihood ratio. We focused particularly on the early detection of West Nile virus using reports of nervous symptoms in horses. Both approaches provided interesting results but Bayes’ rule was especially useful as it provided a quantitative output and was able to combine different epidemiological information. Finally, a Bayesian approach was also used to quantitatively combine various sources of risk estimation in a multivariate syndromic surveillance system (applied to West Nile virus in South of France). Combining evidence provided promising results. This work, based on risk estimations, strengthens the surveillance of VBDs in horses and can support public health decision making. It also, however, highlights the need to improve data collection and data sharing, to implement full performance assessments of complex surveillance systems, and to use effective communication and training to promote the adoption of these approaches
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La surveillance basée sur le risque des maladies à transmission vectorielle chez les chevaux : combinaison de plusieurs sources de données pour améliorer la prise de décision
HAL CCSD, 2015Co-Authors: Faverjon CélineAbstract:Emerging vector-borne diseases are a growing concern, especially for horse populations, which are at particular risk for disease spread. In general, horses travel widely and frequently and, despite the health and economic impacts of equine diseases, effective health regulations and biosecurity systems to ensure safe equine movements are not always in place. The present work proposes to improve the surveillance of vector-borne diseases in horses through the use of different approaches that assess the probability of occurrence of a newly introduced epidemic. First, we developed a spatiotemporal quantitative model which combined various probabilities in order to estimate the risk of introduction of African horse sickness and equine encephalosis. Such combinations of risk provided more a detailed picture of the true risk posed by these pathogens. Second, we assessed syndromic surveillance systems using two approaches: a classical approach with the Alarm Threshold based on the standard error of prediction, and a Bayesian approach based on a likelihood ratio. We focused particularly on the early detection of West Nile virus using reports of nervous symptoms in horses. Both approaches provided interesting results but Bayes’ rule was especially useful as it provided a quantitative output and was able to combine different epidemiological information. Finally, a Bayesian approach was also used to quantitatively combine various sources of risk estimation in a multivariate syndromic surveillance system, as well as a combination of quantitative risk assessment with syndromic surveillance (applied to West Nile virus and equine encephalosis, respectively). Combining evidence provided promising results. This work, based on risk estimations, strengthens the surveillance of VBDs in horses and can support public health decision making. It also, however, highlights the need to improve data collection and data sharing, to implement full performance assessments of complex surveillance systems, and to use effective communication and training to promote the adoption of these approaches.Les maladies émergentes à transmission vectorielle sont une préoccupation croissante et particulièrement lorsqu’elles affectent les chevaux, une population spécifiquement à risque vis-à-vis de la propagation de maladies. En effet, les chevaux voyagent fréquemment et, malgré l’impact sanitaire et économique des maladies équines, les règlementations sanitaires et les principes de biosécurité et de traçabilité censés assurer la sécurité des mouvements d'équidés ne sont pas toujours en place. Notre travail propose d'améliorer la surveillance des maladies à transmission vectorielle chez les chevaux en utilisant différentes méthodes pour estimer la probabilité d'émergence d'une maladie. Tout d'abord, nous avons développé un modèle quantitatif et spatio-temporel combinant différentes probabilités pour estimer les risques d'introduction de la peste équine et de l’encéphalose équine. Ces combinaisons permettent d’obtenir une image plus détaillée du risque posé par ces agents pathogènes. Nous avons ensuite évalué des systèmes de surveillance syndromique par deux approches méthodologiques: l'approche classique avec un seuil d'Alarme basé sur un multiple de l'erreur standard de prédiction, et l'approche bayésienne basée sur le rapport de vraisemblance. Nous avons travaillé ici principalement sur la détection précoce du virus West Nile en utilisant les symptômes nerveux des chevaux. Les deux approches ont fourni des résultats prometteurs, mais l’approche bayésienne était particulièrement intéressante pour obtenir un résultat quantitatif et pour combiner différentes informations épidémiologiques. Pour finir, l'approche bayésienne a été utilisée pour combiner quantitativement différentes sources d'estimation du risque : surveillance syndromique multivariée, et combinaison de la surveillance syndromique avec les résultats d’analyses de risques. Ces combinaisons ont données des résultats prometteurs. Ce travail, basé sur des estimations de risque, contribue à améliorer la surveillance des maladies à transmission vectorielle chez les chevaux et facilite la prise de décision. Les principales perspectives de ce travail sont d'améliorer la collecte et le partage de données, de mettre en oeuvre une évaluation complète des performances des systèmes de surveillance multivariés, et de favoriser l'adoption de ce genre d’approche par les décideurs en utilisant une interface conviviale et en mettant en place un transfert de connaissance
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La surveillance basée sur le risque des maladies à transmission vectorielle chez les chevaux : combinaison de plusieurs sources de données pour améliorer la prise de decision
HAL CCSD, 2015Co-Authors: Faverjon CélineAbstract:Emerging vector-borne diseases are a growing concern, especially for horse populations, which are at particular risk for disease spread. In general, horses travel widely and frequently and, despite the health and economic impacts of equine diseases, effective health regulations and biosecurity systems to ensure safe equine movements are not always in place. The present work proposes to improve the surveillance of vector-borne diseases in horses through the use of different approaches that assess the probability of occurrence of a newly introduced epidemic. First, we developed a spatiotemporal quantitative model which combined various probabilities in order to estimate the risk of introduction of African horse sickness and equine encephalosis. Such combinations of risk provided more a detailed picture of the true risk posed by these pathogens. Second, we assessed syndromic surveillance systems using two approaches: a classical approach with the Alarm Threshold based on the standard error of prediction, and a Bayesian approach based on a likelihood ratio. We focused particularly on the early detection of West Nile virus using reports of nervous symptoms in horses. Both approaches provided interesting results but Bayes’ rule was especially useful as it provided a quantitative output and was able to combine different epidemiological information. Finally, a Bayesian approach was also used to quantitatively combine various sources of risk estimation in a multivariate syndromic surveillance system, as well as a combination of quantitative risk assessment with syndromic surveillance (applied to West Nile virus and equine encephalosis, respectively). Combining evidence provided promising results. This work, based on risk estimations, strengthens the surveillance of VBDs in horses and can support public health decision making. It also, however, highlights the need to improve data collection and data sharing, to implement full performance assessments of complex surveillance systems, and to use effective communication and training to promote the adoption of these approaches.Les maladies émergentes à transmission vectorielle sont une préoccupation croissante et particulièrement lorsqu’elles affectent les chevaux, une population spécirfiquement à risque vis-à-vis de la propagation de maladies. En effet, les chevaux voyagent fréquemment et, malgré l’impact sanitaire et économique des maladies équines, les règlementations sanitaires et les principes de biosécurité et de traçabilité censés assurer la sécurité des mouvements d'équidés ne sont pas toujours en place. Notre travail propose d'améliorer la surveillance des maladies à transmission vectorielle chez les chevaux en utilisant différentes méthodes pour estimer la probabilité d'émergence d'une maladie. Tout d'abord, nous avons développé un modèle quantitatif et spatio-temporel combinant différentes probabilités pour estimer les risques d'introduction de la peste équine et de l’encéphalose équine. Ces combinaisons permettent d’obtenir une image plus détaillée du risque posé par ces agents pathogènes. Nous avons ensuite évalué des systèmes de surveillance syndromique par deux approches méthodologiques: l'approche classique avec un seuil d'Alarme basé sur un multiple de l'erreur standard de prédiction, et l'approche bayésienne basée sur le rapport de vraisemblance. Nous avons travaillé ici principalement sur la détection précoce du virus West Nile en utilisant les symptômes nerveux des chevaux. Les deux approches ont fourni des résultats prometteurs, mais l’approche bayésienne était particulièrement intéressante pour obtenir un résultat quantitatif et pour combiner différentes informations épidémiologiques. Pour finir, l'approche bayésienne a été utilisée pour combiner quantitativement différentes sources d'estimation du risque : surveillance syndromique multivariée, et combinaison de la surveillance syndromique avec les résultats d’analyses de risques. Ces combinaisons ont données des résultats prometteurs. Ce travail, basé sur des estimations de risque, contribue à améliorer la surveillance des maladies à transmission vectorielle chez les chevaux et facilite la prise de décision. Les principales perspectives de ce travail sont d'améliorer la collecte et le partage de données, de mettre en oeuvre une évaluation complète des performances des systèmes de surveillance multivariés, et de favoriser l'adoption de ce genre d’approche par les décideurs en utilisant une interface conviviale et en mettant en place un transfert de connaissance
Antoine Grall - One of the best experts on this subject based on the ideXlab platform.
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Online maintenance policy for a deteriorating system with random change of mode
Reliability Engineering and System Safety, 2007Co-Authors: Bassem Saassouh, Laurence Dieulle, Antoine GrallAbstract:Most of maintenance policies proposed in the literature for gradually deteriorating systems, consider a stationary deterioration process. This paper is an attempt to take into account stochastically deteriorating systems which are subject to a sudden change in their degradation process. A technical device subject to gradual degradation is considered. It is assumed that the level of degradation can be resumed by a single scalar variable. An online maintenance decision rule is proposed, which makes it possible to take into account in real time the online information available on the operating mode of the system as well as its actual deterioration level. We show the efficiency of considering online decision rules for maintenance with respect to traditional maintenance policies based on a static Alarm Threshold. Numerical simulations are given, to assess and optimize the performance of the maintained system from its asymptotic unavailability point of view. It is compared to the results obtained with classical control-limit maintenance policies.
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Asymptotic failure rate of a continuously monitored system
Reliability Engineering and System Safety, 2006Co-Authors: Antoine Grall, Laurence Dieulle, Christophe Berenguer, Michel RoussignolAbstract:This paper deals with a perfectly continuously monitored system which gradually and stochastically deteriorates. The system is renewed by a delayed maintenance operation, which is triggered when the measured deterioration level exceeds an Alarm Threshold. A mathematical model is developed to study the asymptotic behavior of the reliability function. A procedure is proposed which allows us to identify the asymptotic failure rate of the maintained system. Numerical experiments illustrate the efficiency of the proposed procedure and emphasize the relevance of the asymptotic failure rate as an interesting indicator for the evaluation of the control-limit preventive replacement policy.
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Maintenance policy for a continuously monitored deteriorating system
Probability in the Engineering and Informational Sciences, 2003Co-Authors: Christophe Berenguer, Laurence Dieulle, Antoine Grall, Michel RoussignolAbstract:We consider a continuously monitored system that gradually and stochastically deteriorates. An Alarm Threshold is set on the system deterioration level for triggering a delayed preventive maintenance operation. A mathematical model is developed to find the value of the Alarm Threshold that minimizes the asymptotic unavailability. Approximations are derived to improve the numerical optimization.
Gary W. Small - One of the best experts on this subject based on the ideXlab platform.
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Nocturnal Hypoglycemic Alarm Based on Near-Infrared Spectroscopy: In Vivo Studies with a Rat Animal Model
Analytical chemistry, 2019Co-Authors: Sanjeewa R. Karunathilaka, Mark A. Arnold, Gary W. SmallAbstract:A noninvasive method for detecting episodes of nocturnal hypoglycemia is demonstrated with in vivo measurements made with a rat animal model. Employing spectra collected from the near-infrared combination region of 4000–5000 cm–1, piecewise linear discriminant analysis (PLDA) is used to classify spectra into Alarm and nonAlarm data classes on the basis of whether or not they correspond to glucose concentrations below a user-defined hypoglycemic Threshold. A reference spectrum and corresponding glucose concentration are acquired at the start of the monitoring period, and spectra are then collected continuously and converted to absorbance units relative to the initial reference spectrum. The resulting differential spectra correspond to differential glucose concentrations that reflect the differences in concentration between each spectrum and the reference. Given an Alarm Threshold (e.g., 3.0 mM), a database of calibration differential spectra can be partitioned into two groups containing spectra above and b...
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Nocturnal Hypoglycemic Alarm Based on Near-Infrared Spectroscopy: In Vivo Studies with a Rat Animal Model
2019Co-Authors: Sanjeewa R. Karunathilaka, Mark A. Arnold, Gary W. SmallAbstract:A noninvasive method for detecting episodes of nocturnal hypoglycemia is demonstrated with in vivo measurements made with a rat animal model. Employing spectra collected from the near-infrared combination region of 4000–5000 cm–1, piecewise linear discriminant analysis (PLDA) is used to classify spectra into Alarm and nonAlarm data classes on the basis of whether or not they correspond to glucose concentrations below a user-defined hypoglycemic Threshold. A reference spectrum and corresponding glucose concentration are acquired at the start of the monitoring period, and spectra are then collected continuously and converted to absorbance units relative to the initial reference spectrum. The resulting differential spectra correspond to differential glucose concentrations that reflect the differences in concentration between each spectrum and the reference. Given an Alarm Threshold (e.g., 3.0 mM), a database of calibration differential spectra can be partitioned into two groups containing spectra above and below the Threshold. A classification model is then computed with PLDA. The resulting model can be applied to the differential spectra collected during the monitoring period in order to identify spectra whose corresponding glucose concentrations lie in the hypoglycemic range. In this work, the Alarm algorithm was tested in two single-day studies performed with anesthetized rats. Glucose concentrations spanned the range of 1.6 to 13.5 mM (29 to 244 mg/dL). For both rats, the Alarm algorithm performed well. On average, 87.5% of Alarm events were correctly detected, and the occurrence of false Alarms was 7.2%. False Alarms were restricted to times when the glucose concentrations were very close to the Alarm Threshold rather than at random times, thus demonstrating the potential of the approach for practical use
Sanjeewa R. Karunathilaka - One of the best experts on this subject based on the ideXlab platform.
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Nocturnal Hypoglycemic Alarm Based on Near-Infrared Spectroscopy: In Vivo Studies with a Rat Animal Model
Analytical chemistry, 2019Co-Authors: Sanjeewa R. Karunathilaka, Mark A. Arnold, Gary W. SmallAbstract:A noninvasive method for detecting episodes of nocturnal hypoglycemia is demonstrated with in vivo measurements made with a rat animal model. Employing spectra collected from the near-infrared combination region of 4000–5000 cm–1, piecewise linear discriminant analysis (PLDA) is used to classify spectra into Alarm and nonAlarm data classes on the basis of whether or not they correspond to glucose concentrations below a user-defined hypoglycemic Threshold. A reference spectrum and corresponding glucose concentration are acquired at the start of the monitoring period, and spectra are then collected continuously and converted to absorbance units relative to the initial reference spectrum. The resulting differential spectra correspond to differential glucose concentrations that reflect the differences in concentration between each spectrum and the reference. Given an Alarm Threshold (e.g., 3.0 mM), a database of calibration differential spectra can be partitioned into two groups containing spectra above and b...
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Nocturnal Hypoglycemic Alarm Based on Near-Infrared Spectroscopy: In Vivo Studies with a Rat Animal Model
2019Co-Authors: Sanjeewa R. Karunathilaka, Mark A. Arnold, Gary W. SmallAbstract:A noninvasive method for detecting episodes of nocturnal hypoglycemia is demonstrated with in vivo measurements made with a rat animal model. Employing spectra collected from the near-infrared combination region of 4000–5000 cm–1, piecewise linear discriminant analysis (PLDA) is used to classify spectra into Alarm and nonAlarm data classes on the basis of whether or not they correspond to glucose concentrations below a user-defined hypoglycemic Threshold. A reference spectrum and corresponding glucose concentration are acquired at the start of the monitoring period, and spectra are then collected continuously and converted to absorbance units relative to the initial reference spectrum. The resulting differential spectra correspond to differential glucose concentrations that reflect the differences in concentration between each spectrum and the reference. Given an Alarm Threshold (e.g., 3.0 mM), a database of calibration differential spectra can be partitioned into two groups containing spectra above and below the Threshold. A classification model is then computed with PLDA. The resulting model can be applied to the differential spectra collected during the monitoring period in order to identify spectra whose corresponding glucose concentrations lie in the hypoglycemic range. In this work, the Alarm algorithm was tested in two single-day studies performed with anesthetized rats. Glucose concentrations spanned the range of 1.6 to 13.5 mM (29 to 244 mg/dL). For both rats, the Alarm algorithm performed well. On average, 87.5% of Alarm events were correctly detected, and the occurrence of false Alarms was 7.2%. False Alarms were restricted to times when the glucose concentrations were very close to the Alarm Threshold rather than at random times, thus demonstrating the potential of the approach for practical use
Laurence Dieulle - One of the best experts on this subject based on the ideXlab platform.
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Online maintenance policy for a deteriorating system with random change of mode
Reliability Engineering and System Safety, 2007Co-Authors: Bassem Saassouh, Laurence Dieulle, Antoine GrallAbstract:Most of maintenance policies proposed in the literature for gradually deteriorating systems, consider a stationary deterioration process. This paper is an attempt to take into account stochastically deteriorating systems which are subject to a sudden change in their degradation process. A technical device subject to gradual degradation is considered. It is assumed that the level of degradation can be resumed by a single scalar variable. An online maintenance decision rule is proposed, which makes it possible to take into account in real time the online information available on the operating mode of the system as well as its actual deterioration level. We show the efficiency of considering online decision rules for maintenance with respect to traditional maintenance policies based on a static Alarm Threshold. Numerical simulations are given, to assess and optimize the performance of the maintained system from its asymptotic unavailability point of view. It is compared to the results obtained with classical control-limit maintenance policies.
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Asymptotic failure rate of a continuously monitored system
Reliability Engineering and System Safety, 2006Co-Authors: Antoine Grall, Laurence Dieulle, Christophe Berenguer, Michel RoussignolAbstract:This paper deals with a perfectly continuously monitored system which gradually and stochastically deteriorates. The system is renewed by a delayed maintenance operation, which is triggered when the measured deterioration level exceeds an Alarm Threshold. A mathematical model is developed to study the asymptotic behavior of the reliability function. A procedure is proposed which allows us to identify the asymptotic failure rate of the maintained system. Numerical experiments illustrate the efficiency of the proposed procedure and emphasize the relevance of the asymptotic failure rate as an interesting indicator for the evaluation of the control-limit preventive replacement policy.
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Maintenance policy for a continuously monitored deteriorating system
Probability in the Engineering and Informational Sciences, 2003Co-Authors: Christophe Berenguer, Laurence Dieulle, Antoine Grall, Michel RoussignolAbstract:We consider a continuously monitored system that gradually and stochastically deteriorates. An Alarm Threshold is set on the system deterioration level for triggering a delayed preventive maintenance operation. A mathematical model is developed to find the value of the Alarm Threshold that minimizes the asymptotic unavailability. Approximations are derived to improve the numerical optimization.