Statistical Inference for Propagation Processes on Complex Networks

Die Methoden der Netzwerktheorie erfreuen sich wachsender Beliebtheit, da sie die Darstellung von komplexen Systemen durch Netzwerke erlauben. Diese werden nur mit einer Menge von Knoten erfasst, die durch Kanten verbunden werden. Derzeit verfügbare Methoden beschränken sich hauptsächlich auf die deskriptive Analyse der Netzwerkstruktur. In der hier vorliegenden Arbeit werden verschiedene Ansätze für die Inferenz über Prozessen in komplexen Netzwerken vorgestellt. Diese Prozesse beeinflussen messbare Größen in Netzwerkknoten und werden durch eine Menge von Zufallszahlen beschrieben. Alle vorgestellten Methoden sind durch praktische Anwendungen motiviert, wie die Übertragung von Lebensmittelinfektionen, die Verbreitung von Zugverspätungen, oder auch die Regulierung von genetischen Effekten. Zunächst wird ein allgemeines dynamisches Metapopulationsmodell für die Verbreitung von Lebensmittelinfektionen vorgestellt, welches die lokalen Infektionsdynamiken mit den netzwerkbasierten Transportwegen von kontaminierten Lebensmitteln zusammenführt. Dieses Modell ermöglicht die effiziente Simulationen verschiedener realistischer Lebensmittelinfektionsepidemien. Zweitens wird ein explorativer Ansatz zur Ursprungsbestimmung von Verbreitungsprozessen entwickelt. Auf Grundlage einer netzwerkbasierten Redefinition der geodätischen Distanz können komplexe Verbreitungsmuster in ein systematisches, kreisrundes Ausbreitungsschema projiziert werden. Dies gilt genau dann, wenn der Ursprungsnetzwerkknoten als Bezugspunkt gewählt wird. Die Methode wird erfolgreich auf den EHEC/HUS Epidemie 2011 in Deutschland angewandt. Die Ergebnisse legen nahe, dass die Methode die aufwändigen Standarduntersuchungen bei Lebensmittelinfektionsepidemien sinnvoll ergänzen kann. Zudem kann dieser explorative Ansatz zur Identifikation von Ursprungsverspätungen in Transportnetzwerken angewandt werden. Die Ergebnisse von umfangreichen Simulationsstudien mit verschiedenstensten Übertragungsmechanismen lassen auf eine allgemeine Anwendbarkeit des Ansatzes bei der Ursprungsbestimmung von Verbreitungsprozessen in vielfältigen Bereichen hoffen. Schließlich wird gezeigt, dass kernelbasierte Methoden eine Alternative für die statistische Analyse von Prozessen in Netzwerken darstellen können. Es wurde ein netzwerkbasierter Kern für den logistischen Kernel Machine Test entwickelt, welcher die nahtlose Integration von biologischem Wissen in die Analyse von Daten aus genomweiten Assoziationsstudien erlaubt. Die Methode wird erfolgreich bei der Analyse genetischer Ursachen für rheumatische Arthritis und Lungenkrebs getestet. Zusammenfassend machen die Ergebnisse der vorgestellten Methoden deutlich, dass die Netzwerk-theoretische Analyse von Verbreitungsprozessen einen wesentlichen Beitrag zur Beantwortung verschiedenster Fragestellungen in unterschiedlichen Anwendungen liefern kann

Sensor-based localization of epidemic sources on human mobility networks

We investigate the source detection problem in epidemiology, which is one of the most important issues for control of epidemics. Mathematically, we reformulate the problem as one of identifying the relevant component in a multivariate Gaussian mixture model. Focusing on the study of cholera and diseases with similar modes of transmission, we calibrate the parameters of our mixture model using human mobility networks within a stochastic, spatially explicit epidemiological model for waterborne disease. Furthermore, we adopt a Bayesian perspective, so that prior information on source location can be incorporated (e.g., reflecting the impact of local conditions). Posterior-based inference is performed, which permits estimates in the form of either individual locations or regions. Importantly, our estimator only requires first-arrival times of the epidemic by putative observers, typically located only at a small proportion of nodes. The proposed method is demonstrated within the context of the 2000-2002 cholera outbreak in the KwaZulu-Natal province of South Africa

Integration des 'free cash flow' dans une analyse multifactorielle de la rentabilite des actions

Available from INIST (FR), Document Supply Service, under shelf-number : DO 6351 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc

Source estimation for propagation processes on complex networks with an application to delays in public transportation systems

textabstractThe correct identification of the source of a propagation process is crucial in many research fields. As a specific application, we consider source estimation of delays in public transportation networks. We propose two approaches: an effective distance median and a backtracking method. The former is based on a structurally generic effective distance-based approach for the identification of infectious disease origins, and the latter is specifically designed for delay propagation. We examine the performance of both methods in simulation studies and in an application to the German railway system, and we compare the results with those of a centrality-based approach for source detection.

Epidemiological and Ecological Characterization of the EHEC O104:H4 Outbreak in Hamburg, Germany, 2011

In 2011, a large outbreak of entero-hemorrhagic E. coli (EHEC) and hemolytic uremic syndrome (HUS) occurred in Germany. The City of Hamburg was the first focus of the epidemic and had the highest incidences among all 16 Federal States of Germany. In this article, we present epidemiological characteristics of the Hamburg notification data. Evaluating the epicurves retrospectively, we found that the first epidemiological signal of the outbreak, which was in form of a HUS case cluster, was received by local health authorities when already 99 EHEC and 48 HUS patients had experienced their first symptoms. However, only two EHEC and seven HUS patients had been notified. Middle-aged women had the highest risk for contracting the infection in Hamburg. Furthermore, we studied timeliness of case notification in the course of the outbreak. To analyze the spatial distribution of EHEC/HUS incidences in 100 districts of Hamburg, we mapped cases' residential addresses using geographic information software. We then conducted an ecological study in order to find a statistical model identifying associations between local socio-economic factors and EHEC/HUS incidences in the epidemic. We employed a Bayesian Poisson model with covariates characterizing the Hamburg districts as well as incorporating structured and unstructured spatial effects. The Deviance Information Criterion was used for stepwise variable selection. We applied different modeling approaches by using primary data, transformed data, and preselected subsets of transformed data in order to identify socio-economic factors characterizing districts where EHEC/HUS outbreak cases had their residence

Efficacy and immune-related adverse event associations in avelumab-treated patients

Background Adverse events (AEs) of special interest that arise during treatment with immune checkpoint inhibitors, including immune-related AEs (irAEs), have been reported to be associated with improved clinical outcomes. We analyzed patients treated with avelumab from the JAVELIN Solid Tumor and Merkel 200 trials, examining the association between AEs and efficacy while adjusting for confounding factors such as treatment duration and event order.Methods We analyzed efficacy and safety data from 1783 patients treated with the programmed death ligand 1 inhibitor avelumab who were enrolled in expansion cohorts of the JAVELIN Solid Tumor and Merkel 200 trials. To analyze the association between irAEs and efficacy with regard to survival, we used a time-dependent Cox model with time-varying indicators for irAEs, as well as multistate models that accounted for competing risks and time inhomogeneity.Results 295 patients (16.5%) experienced irAEs and 454 patients (25.5%) experienced infusion-related reactions. There was a reduced risk of death in patients who experienced irAEs compared with those who did not (HR 0.71, 95% CI 0.59 to 0.85) using the time-dependent Cox model. The multistate model did not suggest that the occurrence of irAEs could predict response; however, it predicted a higher chance of irAEs occurring after a response. No association was observed between response and infusion-related reactions.Conclusions Patients who experience irAEs showed improved survival. Although irAEs are not predictors for response to immune checkpoint inhibitors, increased vigilance for irAEs is needed after treatment with avelumab.Trial registration numbers NCT01772004 and NCT02155647

Epicurve of EHEC O104:H4 Outbreak in Hamburg, Germany.

<p>Shown are EHEC and HUS cases according to date of symptom onset (boxes, n = 656) and notification date (line, n = 554). The last outbreak-associated case fell ill on July 4, 2011. The arrow indicates the day when RKI was informed of a cluster of three pediatric HUS cases admitted to the Hamburg University Hospital.</p

Two different sides of &apos;chemobrain&apos;: determinants and nondeterminants of self-perceived cognitive dysfunction in a prospective, randomized, multicenter study

Abstract Objective: Complaints of cognitive dysfunction are frequent among cancer patients. Many studies have identified neuropsychological compromise associated with cancer and cancer therapy; however, the neuropsychological compromise was not related to self-reported cognitive dysfunction. In this prospective study, the authors examined if confounding factors masked an underlying association of self-perceived cognitive function with actual cognitive performance. Determinants of self-perceived cognitive dysfunction were investigated. Methods: Self-perceived cognitive function and cognitive performance were assessed before treatment, at the end of treatment, and 1 year after baseline in 101 breast cancer patients randomized to standard versus intensified chemotherapy. Linear mixed-effects models were applied to test the relationships of performance on neuropsychological tests, patient characteristics, and treatment variables to self-reported cognitive function. Change of cognitive performance was tested as a predictor of change in self-reports. Results: Self-perceived cognitive function deteriorated during chemotherapy and had partially recovered 1 year after diagnosis. The personality trait negative affectivity, current depression, and chemotherapy regimen were consistently related to cognitive self-reports. No significant associations with performance in any of the 12 cognitive tests emerged. Change of cognitive performance was not reflected in self-reports of cognitive function. Conclusions: Neuropsychological compromise and self-perceived cognitive dysfunction are independent phenomena in cancer patients. Generally, cancer-associated neuropsychological compromise is not noticed by affected patients, but negative affectivity and treatment burden induce pessimistic self-appraisals of cognitive functioning regardless of the presence of neuropsychological compromise. Clinicians should consider this when determining adequate therapy for patients who complain of &apos;chemobrain&apos;. Copyright r 2010 John Wiley &amp; Sons, Ltd. Keywords: oncology; cancer; cognition disorders; adverse effects; neuropsychological tests; antineoplastic combined chemotherapy protocols Objective After cytostatic treatment, many cancer patients complain about cognitive side effects such as attention and memory problems. As a result of these reports, cognitive function in cancer patients has been investigated in a growing number of studies and the existence of chemotherapy-associated cognitive compromise has indeed been confirmed by many of them. However, with few exceptions, cognitive compromise as assessed by neuropsychological testing appeared to be unrelated to self-reported cognitive dysfunction. The divergence of objectively assessed cognitive function and subjective reports has been found pretreatmen