Disease spread through animal movements: a static and temporal network analysis of pig trade in Germany

Background: Animal trade plays an important role for the spread of infectious diseases in livestock populations. As a case study, we consider pig trade in Germany, where trade actors (agricultural premises) form a complex network. The central question is how infectious diseases can potentially spread within the system of trade contacts. We address this question by analyzing the underlying network of animal movements. Methodology/Findings: The considered pig trade dataset spans several years and is analyzed with respect to its potential to spread infectious diseases. Focusing on measurements of network-topological properties, we avoid the usage of external parameters, since these properties are independent of specific pathogens. They are on the contrary of great importance for understanding any general spreading process on this particular network. We analyze the system using different network models, which include varying amounts of information: (i) static network, (ii) network as a time series of uncorrelated snapshots, (iii) temporal network, where causality is explicitly taken into account. Findings: Our approach provides a general framework for a topological-temporal characterization of livestock trade networks. We find that a static network view captures many relevant aspects of the trade system, and premises can be classified into two clearly defined risk classes. Moreover, our results allow for an efficient allocation strategy for intervention measures using centrality measures. Data on trade volume does barely alter the results and is therefore of secondary importance. Although a static network description yields useful results, the temporal resolution of data plays an outstanding role for an in-depth understanding of spreading processes. This applies in particular for an accurate calculation of the maximum outbreak size.Comment: main text 33 pages, 17 figures, supporting information 7 pages, 7 figure

Constrained tGAP for generalisation between scales: the case of Dutch topographic data

This article presents the results of integrating large- and medium-scale data into a unified data structure. This structure can be used as a single non-redundant representation for the input data, which can be queried at any arbitrary scale between the source scales. The solution is based on the constrained topological Generalized Area Partition (tGAP), which stores the results of a generalization process applied to the large-scale dataset, and is controlled by the objects of the medium-scale dataset, which act as constraints on the large-scale objects. The result contains the accurate geometry of the large-scale objects enriched with the generalization knowledge of the medium-scale data, stored as references in the constraint tGAP structure. The advantage of this constrained approach over the original tGAP is the higher quality of the aggregated maps. The idea was implemented with real topographic datasets from The Netherlands for the large- (1:1000) and medium-scale (1:10,000) data. The approach is expected to be equally valid for any categorical map and for other scales as well

Measuring relative opinion from location-based social media: A case study of the 2016 U.S. presidential election

Social media has become an emerging alternative to opinion polls for public opinion collection, while it is still posing many challenges as a passive data source, such as structurelessness, quantifiability, and representativeness. Social media data with geotags provide new opportunities to unveil the geographic locations of users expressing their opinions. This paper aims to answer two questions: 1) whether quantifiable measurement of public opinion can be obtained from social media and 2) whether it can produce better or complementary measures compared to opinion polls. This research proposes a novel approach to measure the relative opinion of Twitter users towards public issues in order to accommodate more complex opinion structures and take advantage of the geography pertaining to the public issues. To ensure that this new measure is technically feasible, a modeling framework is developed including building a training dataset by adopting a state-of-the-art approach and devising a new deep learning method called Opinion-Oriented Word Embedding. With a case study of the tweets selected for the 2016 U.S. presidential election, we demonstrate the predictive superiority of our relative opinion approach and we show how it can aid visual analytics and support opinion predictions. Although the relative opinion measure is proved to be more robust compared to polling, our study also suggests that the former can advantageously complement the later in opinion prediction

Mortality rates of the Alpine Chamois : the influence of snow-meteorological factors

Especially for animals inhabiting alpine areas, winter environmental conditions can be limiting. Cold temperatures, hampered food availability and natural perils are just three of many potential threats that mountain ungulates face in winter. Understanding their sensitivity to climate variability is essential for game management. Here we focus on analyzing the influence of snow and weather conditions on the mortality pattern of Alpine chamois. Our mortality data are derived from a systematic assessment of 6,500 chamois that died of natural causes over the course of 13 years. We use population- and habitat-specific data on snow, climate and avalanche danger to identify the key environmental factors that essentially determine the spatio-temporal variations in chamois mortality. Initially, we show that most fatalities occurred in winter, with a peak around March, when typically snow depths were highest. Death causes related to poor general conditions were the major component of seasonal variations. As for the interannual variations in mortality, snow depth and avalanche risk best explained the occurrence of winters with increased numbers of fatalities. Finally, analyzing differences in mortality rates between populations, we identified sun-exposed winter habitats with little snow accumulation as favourable for alpine chamois

Corporate governance ratings as a means to reduce asymmetric information

Can corporate governance ratings reduce problems of asymmetric information between companies and investors? To answer this question, we set out to examine the information basis for providing such ratings by reviewing corporate governance attributes that are required or recommended in laws, accounting standards and codes, respectively. After that, we scrutinize and organize the publicly available information on the methodologies actually used by rating providers. However, important details of these methodologies are treated as confidential property, thus we approach the evaluation of corporate governance ratings as a means to reduce asymmetric information in a more general manner. We propose that the rating process may be seen as consisting of two general activities, namely a data reduction phase, and a data weighting, aggregation and classification phase. Findings based on a Danish data set suggest that rating providers by selecting relevant attributes in an intelligent way can improve the screening of companies according to governance quality. In contrast, it seems questionable that weighting, aggregation and classification of corporate governance attributes considerably improve discrimination according to governance qualityNo; keywords

XWeB: the XML Warehouse Benchmark

With the emergence of XML as a standard for representing business data, new decision support applications are being developed. These XML data warehouses aim at supporting On-Line Analytical Processing (OLAP) operations that manipulate irregular XML data. To ensure feasibility of these new tools, important performance issues must be addressed. Performance is customarily assessed with the help of benchmarks. However, decision support benchmarks do not currently support XML features. In this paper, we introduce the XML Warehouse Benchmark (XWeB), which aims at filling this gap. XWeB derives from the relational decision support benchmark TPC-H. It is mainly composed of a test data warehouse that is based on a unified reference model for XML warehouses and that features XML-specific structures, and its associate XQuery decision support workload. XWeB's usage is illustrated by experiments on several XML database management systems

Recruitment Facilitation and Spatial Pattern Formation in Soft-Bottom Mussel Beds

Mussels (Mytilus edulis) build massive, spatially complex, biogenic structures that alter the biotic and abiotic environment and provide a variety of ecosystem services. Unlike rocky shores, where mussels can attach to the primary substrate, soft sediments are unsuitable for mussel attachment. We used a simple lattice model, field sampling, and field and laboratory experiments to examine facilitation of recruitment (i.e., preferential larval, juvenile, and adult attachment to mussel biogenic structure) and its role in the development of power-law spatial patterns observed in Maine, USA, soft-bottom mussel beds. The model demonstrated that recruitment facilitation produces power-law spatial structure similar to that in natural beds. Field results provided strong evidence for facilitation of recruitment to other mussels—they do not simply map onto a hard-substrate template of gravel and shell hash. Mussels were spatially decoupled from non-mussel hard substrates to which they can potentially recruit. Recent larval recruits were positively correlated with adult mussels, but not with other hard substrates. Mussels made byssal thread attachments to other mussels in much higher proportions than to other hard substrates. In a field experiment, mussel recruitment was highest to live mussels, followed by mussel shell hash and gravel, with almost no recruitment to muddy sand. In a laboratory experiment, evenly dispersed mussels rapidly self-organized into power-law clusters similar to those observed in nature. Collectively, the results indicate that facilitation of recruitment to existing mussels plays a major role in soft-bottom spatial pattern development. The interaction between large-scale resource availability (hard substrate) and local-scale recruitment facilitation may be responsible for creating complex power-law spatial structure in soft-bottom mussel beds