3,504 research outputs found

    RANK-BASED TEMPO-SPATIAL CLUSTERING: A FRAMEWORK FOR RAPID OUTBREAK DETECTION USING SINGLE OR MULTIPLE DATA STREAMS

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    In the recent decades, algorithms for disease outbreak detection have become one of the main interests of public health practitioners to identify and localize an outbreak as early as possible in order to warrant further public health response before a pandemic develops. Today’s increased threat of biological warfare and terrorism provide an even stronger impetus to develop methods for outbreak detection based on symptoms as well as definitive laboratory diagnoses. In this dissertation work, I explore the problems of rapid disease outbreak detection using both spatial and temporal information. I develop a framework of non-parameterized algorithms which search for patterns of disease outbreak in spatial sub-regions of the monitored region within a certain period. Compared to the current existing spatial or tempo-spatial algorithm, the algorithms in this framework provide a methodology for fast searching of either univariate data set or multivariate data set. It first measures which study area is more likely to have an outbreak occurring given the baseline data and currently observed data. Then it applies a greedy searching mechanism to look for clusters with high posterior probabilities given the risk measurement for each unit area as heuristic. I also explore the performance of the proposed algorithms. From the perspective of predictive modeling, I adopt a Gamma-Poisson (GP) model to compute the probability of having an outbreak in each cluster when analyzing univariate data. I build a multinomial generalized Dirichlet (MGD) model to identify outbreak clusters from multivariate data which include the OTC data streams collected by the national retail data monitor (NRDM) and the ED data streams collected by the RODS system. Key contributions of this dissertation include 1) it introduces a rank-based tempo-spatial clustering algorithm, RSC, by utilizing greedy searching and Bayesian GP model for disease outbreak detection with comparable detection timeliness, cluster positive prediction value (PPV) and improved running time; 2) it proposes a multivariate extension of RSC (MRSC) which applies MGD model. The evaluation demonstrated the advantage that MGD model can effectively suppress the false alarms caused by elevated signals that are non-disease relevant and occur in all the monitored data streams

    A systematic review of cluster detection mechanisms in syndromic surveillance: Towards developing a framework of cluster detection mechanisms for EDMON system

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    Source at http://www.ep.liu.se/ecp/151/011/ecp18151011.pdf.Time lag in detecting disease outbreaks remains a threat to global health security. Currently, our research team is working towards a system called EDMON, which uses blood glucose level and other supporting parameters from people with type 1 diabetes, as indicator variables for outbreak detection. Therefore, this paper aims to pinpoint the state of the art cluster detection mechanism towards developing an efficient framework to be used in EDMON and other similar syndromic surveillance systems. Various challenges such as user mobility, privacy and confidentiality, geographical location estimation and other factors have been considered. To this end, we conducted a systematic review exploring different online scholarly databases. Considering peer reviewed journals and articles, literatures search was conducted between January and March 2018. Relevant literatures were identified using the title, keywords, and abstracts as a preliminary filter with the inclusion criteria and a full text review were done for literatures that were found to be relevant. A total of 28 articles were included in the study. The result indicates that various clustering and aberration detection algorithms have been developed and tested up to the task. In this regard, privacy preserving policies and high computational power requirement were found challenging since it restrict usage of specific locations for syndromic surveillance

    Prediction of the kiwifruit decline syndrome in diseased orchards by remote sensing

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    Eight years after the first record in Italy, Kiwifruit Decline (KD), a destructive disease causing root rot, has already affected more than 25% of the area under kiwifruit cultivation in Italy. Diseased plants are characterised by severe decay of the fine roots and sudden wilting of the canopy, which is only visible after the season's first period of heat (July-August). The swiftness of symptom appearance prevents correct timing and positioning for sampling of the disease, and is therefore a barrier to aetiological studies. The aim of this study is to test the feasibility of thermal and multispectral imaging for the detection of KD using an unsupervised classifier. Thus, RGB, multispectral and thermal data from a kiwifruit orchard, with healthy and diseased plants, were acquired simultaneously during two consecutive growing seasons (2017-2018) using an Unmanned Aerial Vehicle (UAV) platform. Data reduction was applied to the clipped areas of the multispectral and thermal data from the 2017 survey. Reduced data were then classified with two unsupervised algorithms, a K-means and a hierarchical method. The plant vigour (canopy size and presence/absence of wilted leaves) and the health shifts exhibited by asymptomatic plants between 2017 and 2018 were evaluated from RGB data via expert assessment and used as the ground truth for cluster interpretation. Multispectral data showed a high correlation with plant vigour, while temperature data demonstrated a good potential use in predicting health shifts, especially in highly vigorous plants that were asymptomatic in 2017 and became symptomatic in 2018. The accuracy of plant vigour assessment was above 73% when using multispectral data, while clustering of the temperature data allowed the prediction of disease outbreak one year in advance, with an accuracy of 71%. Based on our results, the unsupervised clustering of remote sensing data could be a reliable tool for the identification of sampling areas, and can greatly improve aetiological studies of this new disease in kiwifruit

    Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review

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    Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a combination of climatic, environmental, and human mobility factors. Arboviruses prediction models can be a support tool for decision-making by public health agents. In this study, we propose a systematic literature review to identify arboviruses prediction models, as well as models for their transmitter vector dynamics. To carry out this review, we searched reputable scientific bases such as IEE Xplore, PubMed, Science Direct, Springer Link, and Scopus. We search for studies published between the years 2015 and 2020, using a search string. A total of 429 articles were returned, however, after filtering by exclusion and inclusion criteria, 139 were included. Through this systematic review, it was possible to identify the challenges present in the construction of arboviruses prediction models, as well as the existing gap in the construction of spatiotemporal models

    Graph Theory and Networks in Biology

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    In this paper, we present a survey of the use of graph theoretical techniques in Biology. In particular, we discuss recent work on identifying and modelling the structure of bio-molecular networks, as well as the application of centrality measures to interaction networks and research on the hierarchical structure of such networks and network motifs. Work on the link between structural network properties and dynamics is also described, with emphasis on synchronization and disease propagation.Comment: 52 pages, 5 figures, Survey Pape

    Machine learning and big data techniques for satellite-based rice phenology

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    New sources of information are required to support rice production decisions. To cope with this challenge, studies have found practical applications on mapping rice using remote sensing techniques. This study attempts to implement a methodology aimed at monitoring rice phenology using optical satellite data. The relationship between rice phenology and reflectance metrics was explored at two levels: growth stages and biophysical modifications caused by diseases. Two optical moderate-resolution missions were combined to detect growth phases. Three machine-learning approaches (random forest, support vector machine, and gradient boosting trees) were trained with multitemporal NDVI data. Analytics from validation showed that the algorithms were able to estimate rice phases with performances above 0.94 in f-1 score. Tested models yielded an overall accuracy of 71.8%, 71.2%, 60.9% and 94.7% for vegetative, reproductive, ripening and harvested categories. A second exploration was carried out by combining Sentinel-2 data and ground-based information about rice disease incidence. K-means clustering was used to map rice biophysical changes across reproductive and ripening phases. The findings ascertained the remote sensing capabilities to create new information about rice for Colombia’s conditions

    MACHINE LEARNING AND BIG DATA TECHNIQUES FOR SATELLITE-BASED RICE PHENOLOGY MONITORING

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    New sources of information are required to support rice production decisions. To cope with this challenge, studies have found practical applications on mapping rice using remote sensing techniques. This study attempts to implement a methodology aimed at monitoring rice phenology using optical satellite data. The relationship between rice phenology and reflectance metrics was explored at two levels: growth stages and biophysical modifications caused by diseases. Two optical moderate-resolution missions were combined to detect growth phases. Three machine-learning approaches (random forest, support vector machine, and gradient boosting trees) were trained with multitemporal NDVI data. Analytics from validation showed that the algorithms were able to estimate rice phases with performances above 0.94 in f-1 score. Tested models yielded an overall accuracy of 71.8%, 71.2%, 60.9% and 94.7% for vegetative, reproductive, ripening and harvested categories. A second exploration was carried out by combining Sentinel-2 data and ground-based information about rice disease incidence. K-means clustering was used to map rice biophysical changes across reproductive and ripening phases. The findings ascertained the remote sensing capabilities to create new information about rice for Colombia’s conditions

    Statistical Inference for Propagation Processes on Complex Networks

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    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
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