Analysis of syntactic and semantic features for fine-grained event-spatial understanding in outbreak news reports

<p>Abstract</p> <p>Background</p> <p>Previous studies have suggested that epidemiological reasoning needs a fine-grained modelling of events, especially their spatial and temporal attributes. While the temporal analysis of events has been intensively studied, far less attention has been paid to their spatial analysis. This article aims at filling the gap concerning automatic event-spatial attribute analysis in order to support health surveillance and epidemiological reasoning.</p> <p>Results</p> <p>In this work, we propose a methodology that provides a detailed analysis on each event reported in news articles to recover the most specific locations where it occurs. Various features for recognizing spatial attributes of the events were studied and incorporated into the models which were trained by several machine learning techniques. The best performance for spatial attribute recognition is very promising; 85.9% F-score (86.75% precision/85.1% recall).</p> <p>Conclusions</p> <p>We extended our work on event-spatial attribute recognition by focusing on machine learning techniques, which are CRF, SVM, and Decision tree. Our approach avoided the costly development of an external knowledge base by employing the feature sources that can be acquired locally from the analyzed document. The results showed that the CRF model performed the best. Our study indicated that the nearest location and previous event location are the most important features for the CRF and SVM model, while the location extracted from the verb's subject is the most important to the Decision tree model.</p

Structuring an event ontology for disease outbreak detection

<p>Abstract</p> <p>Background</p> <p>This paper describes the design of an event ontology being developed for application in the machine understanding of infectious disease-related events reported in natural language text. This event ontology is designed to support timely detection of disease outbreaks and rapid judgment of their alerting status by 1) bridging a gap between layman's language used in disease outbreak reports and public health experts' deep knowledge, and 2) making multi-lingual information available.</p> <p>Construction and content</p> <p>This event ontology integrates a model of experts' knowledge for disease surveillance, and at the same time sets of linguistic expressions which denote disease-related events, and formal definitions of events. In this ontology, rather general event classes, which are suitable for application to language-oriented tasks such as recognition of event expressions, are placed on the upper-level, and more specific events of the experts' interest are in the lower level. Each class is related to other classes which represent participants of events, and linked with multi-lingual synonym sets and axioms.</p> <p>Conclusions</p> <p>We consider that the design of the event ontology and the methodology introduced in this paper are applicable to other domains which require integration of natural language information and machine support for experts to assess them. The first version of the ontology, with about 40 concepts, will be available in March 2008.</p

Modeling, analysis and defense strategies against Internet attacks.

Third, we have analyzed the tradeoff between delay caused by filtering of worms at routers, and the delay due to worms' excessive amount of network traffic. We have used the optimal control problem, to determine the appropriate tradeoffs between these two delays for a given rate of a worm spreading. Using our technique we can minimize the overall network delay by finding the number of routers that should perform filtering and the time at which they should start the filtering process.Many early Internet protocols were designed without a fundamentally secure infrastructure and hence vulnerable to attacks such as denial of service (DoS) attacks and worms. DoS attacks attempt to consume the resources of a remote host or network, thereby denying or degrading service to legitimate users. Network forensics is an emerging area wherein the source or the cause of the attacker is determined using IDS tools. The problem of finding the source(s) of attack(s) is called the "trace back problem". Lately, Internet worms have become a major problem for the security of computer networks, causing considerable amount of resources and time to be spent recovering from the disruption of systems. In addition to breaking down victims, these worms create large amounts of unnecessary network data traffic that results in network congestion, thereby affecting the entire network.In this dissertation, first we solve the trace back problem more efficiently in terms of the number of routers needed to complete the track back. We provide an efficient algorithm to decompose a network into connected components and construct a terminal network. We show that for a terminal network with n routers, the trace back can be completed in O(log n) steps.Second, we apply two classical epidemic SIS and SIR models to study the spread of Internet Worm. The analytical models that we provide are useful in determining the rate of spread and time required to infect a majority of the nodes in the network. Our simulation results on large Internet like topologies show that in a fairly small amount of time, 80% of the network nodes is infected

Modeling the Spread of Alfalfa Stem Nematodes: Insights into their Dynamics and Control

Alfalfa is a major cash crop in the western United States, where fields that are infested with the alfalfa stem nematode (Ditylenchus dipsaci) can be found. With no nematicides available to control alfalfa stem nematode spread, growers can use nematode resistant varieties of alfalfa to manage nematode populations in a field. A deterministic, discrete-time, host-parasite model is presented that describes the spread of alfalfa stem nematodes on resistant hosts that was fit to experimental data obtained in Weber County, Utah. Numerical results obtained from simulations with the model are used to compare how varying levels of resistance can affect harvest yield. Resistant varieties can also affect the invasion speeds of epidemics in crops. A continuous time, spatial model is presented that describes how these resistant varieties affect invasion speeds in general crop systems. Speeds of traveling wave fronts are determined for simple epidemics in crops that contain a mixture of resistant and non-resistant hosts. For the model, it was found that the wave speeds will slow down as highly nematode resistant varieties of alfalfa are used. The speed of invasion for the alfalfa stem nematode can be determined by using a mathematical relationship that is know as the contact distribution. We present a spatial model for the spread of alfalfa stem nematodes that uses a Gaussian distribution as the contact distribution of the alfalfa stem nematodes, which was determined by experimental data. Using this contact distribution we are able to approximate the speed of nematode invasive fronts in absence of advection, i.e. without nematode trans-port through flood irrigation. The contact distribution is then used to calculate front speeds when resistant varieties of alfalfa are introduced. We found that, unsurprisingly, invasive speeds are relatively low and cannot support the rapid dispersal of the disease among fields as seen in practice. However, this result leads to conjecture that changing current irrigation practices, from flood to sprinkle irrigation, could effectively contribute to control the spread of alfalfa stem nematodes. Resistant varieties of alfalfa can be used to effectively control the spread of the alfalfa stem nematode. In this work we have shown that using resistant varieties of alfalfa can increase yield up to 83%, they can slow down invasion speeds of nematodes, and switching from flood to sprinkler irrigation could effectively contribute to the control of the alfalfa stem nematode

Tensor product approach to modelling epidemics on networks

To improve mathematical models of epidemics it is essential to move beyond the traditional assumption of homogeneous well--mixed population and involve more precise information on the network of contacts and transport links by which a stochastic process of the epidemics spreads. In general, the number of states of the network grows exponentially with its size, and a master equation description suffers from the curse of dimensionality. Almost all methods widely used in practice are versions of the stochastic simulation algorithm (SSA), which is notoriously known for its slow convergence. In this paper we numerically solve the chemical master equation for an SIR model on a general network using recently proposed tensor product algorithms. In numerical experiments we show that tensor product algorithms converge much faster than SSA and deliver more accurate results, which becomes particularly important for uncovering the probabilities of rare events, e.g. for number of infected people to exceed a (high) threshold

Estimation of time-space-varying parameters in dengue epidemic models

There are nowadays a huge load of publications about dengue epidemic models, which mostly employ deterministic differential equations. The analytical properties of deterministic models are always of particular interest by many experts, but their validity "“ if they can indeed track some empirical data "“ is an increasing demand by many practitioners. In this view, the data can tell to which figure the solutions yielded from the models should be; they drift all the involving parameters towards the most appropriate values. By prior understanding of the population dynamics, some parameters with inherently constant values can be estimated forthwith; some others can sensibly be guessed. However, solutions from such models using sets of constant parameters most likely exhibit, if not smoothness, at least noise-free behavior; whereas the data appear very random in nature. Therefore, some parameters cannot be constant as the solutions to seemingly appear in a high correlation with the data. We were aware of impracticality to solve a deterministic model many times that exhaust all trials of the parameters, or to run its stochastic version with Monte Carlo strategy that also appeals for a high number of solving processes. We were also aware that those aforementioned non-constant parameters can potentially have particular relationships with several extrinsic factors, such as meteorology and socioeconomics of the human population. We then study an estimation of time-space-varying parameters within the framework of variational calculus and investigate how some parameters are related to some extrinsic factors. Here, a metric between the aggregated solution of the model and the empirical data serves as the objective function, where all the involving state variables are kept satisfying the physical constraint described by the model. Numerical results for some examples with real data are shown and discussed in details

Haemorrhagic Fevers in Africa: Narratives, Politics and Pathways of Disease and Response

Haemorrhagic fevers have, par excellence, captured popular and media imagination as deadly diseases to come ‘out of Africa’. Associated with wildlife vectors in forested environments, viral haemorrhagic fevers such as Ebola, Marburg and lassa fever figure high in current concern about so-called ‘emerging infectious diseases’, their hotspots of origin and threat of global spread. Outbreak narratives have justified rapid and sometimes draconian international policy responses and control measures. Yet there is a variety of other ways of framing haemorrhagic fevers. There present different views concerning who is at risk, and how? Is the ‘system’ of interacting social-disease ecological processes a local or a global one, and how do scales intersect? Should haemorrhagic fevers be understood in terms of short-term outbreaks, or as part of more ‘structural’, long-term social-disease-ecological interactions? What of the perspectives of people living with the diseases in African settings? And what of uncertainties about disease dynamics, over longer as well as short time scales? This paper contrasts global outbreak narratives with three others which consider haemorrhagic fevers as deadly local disease events, in terms of culture and context, and in terms of long-term social and environmental dynamics. It considers the pathways of disease response associated with each, and how they might be better integrated to deal with haemorrhagic fevers in more effective, Sustainable and socially just waysESR