Tracing Road Network Bottleneck by Data Driven Approach.
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ABSTRACT:
SUBMITTER: Qi H
PROVIDER: S-EPMC4881905 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundContact tracing has been globally adopted in the fight to control the infection rate of COVID-19. To this aim, several mobile apps have been developed. However, there are ever-growing concerns over the working mechanism and performance of these applications. The literature already provides some interesting exploratory studies on the community's response to the applications by analyzing information from different sources, such as news and users' reviews of the applications. However, to the best of our knowledge, there is no existing solution that automatically analyzes users' reviews and extracts the evoked sentiments. We believe such solutions combined with a user-friendly interface can be used as a rapid surveillance tool to monitor how effective an application is and to make immediate changes without going through an intense participatory design method.ObjectiveIn this paper, we aim to analyze the efficacy of AI and NLP techniques for automatically extracting and classifying the polarity of users' sentiments by proposing a sentiment analysis framework to automatically analyze users' reviews on COVID-19 contact tracing mobile apps. We also aim to provide a large-scale annotated benchmark data set to facilitate future research in the domain. As a proof of concept, we also developed a web application based on the proposed solutions, which is expected to help the community quickly analyze the potential of an application in the domain.MethodsWe propose a pipeline starting from manual annotation via a crowd-sourcing study and concluding with the development and training of artificial intelligence (AI) models for automatic sentiment analysis of users' reviews. In detail, we collected and annotated a large-scale data set of user reviews on COVID-19 contact tracing applications. We used both classical and deep learning methods for classification experiments.ResultsWe used 8 different methods on 3 different tasks, achieving up to an average F1 score of 94.8%, indicating the feasibility of the proposed solution. The crowd-sourcing activity resulted in a large-scale benchmark data set composed of 34,534 manually annotated reviews.ConclusionsThe existing literature mostly relies on the manual or exploratory analysis of users' reviews on applications, which is tedious and time-consuming. In existing studies, generally, data from fewer applications are analyzed. In this work, we showed that AI and natural language processing techniques provide good results for analyzing and classifying users' sentiments' polarity and that automatic sentiment analysis can help to analyze users' responses more accurately and quickly. We also provided a large-scale benchmark data set. We believe the presented analysis, data set, and proposed solutions combined with a user-friendly interface can be used as a rapid surveillance tool to analyze and monitor mobile apps deployed in emergency situations leading to rapid changes in the applications without going through an intense participatory design method.
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Project description:Various approaches have been proposed to address the delineation of built-up areas for a wide range of applications. Recently developed approaches are based on the increasing availability of road network data. However, most approaches have employed one or more parameters to divide built-up from non-built-up areas. Very few studies have discussed how to determine appropriate thresholds for such parameters. This study employed an empirical approach for threshold determination, and validated that the approach is applicable for the delineation of built-up areas using road network data. A series of experiments were designed to investigate the most-appropriate thresholds (determined using a similarity measure) for multiple parameters of three existing approaches (street blocks, grid-based, and kernel density) with regard to different administrative regions and cities/towns. The results show that in most cases, the most-appropriate thresholds or ranges for different subdivisions are either identical or overlap-thus validating the use of the most-appropriate thresholds to delineate built-up areas for one or multiple small subdivisions and, by inference, for a much larger region.
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Project description:Physarum Polycephalum is a single cell visible by unaided eye. This is a plasmodial, vegetative stage of acellular slime mould. This single cell has myriad of nuclei which contribute to a network of bio-chemical oscillators responsible for the slime mould's distributed sensing, concurrent information processing and decision making, and parallel actuation. When presented with a spatial configuration of sources of nutrients, the slime mould spans the sources with networks of its protoplasmic tube. These networks belong to a family of planar proximity graphs. The protoplasmic networks also show a degree of similarity to vehicular transport networks. Previously, we have shown that the foraging behaviour of the slime mould can be applied in archaeological research to complement and enhance conventional geographic information system tools. The results produced suffered from limitation of a flat substrate: transport routes imitated by the slime mould did not reflect patterns of elevations. To overcome the limitation of the 'flat world' we constructed a three-dimensional model of Balkans. In laboratory experiments and computer modelling we uncovered patterns of the foraging behaviour that might shed a light onto development of Roman roads in the Balkans during the imperial period (1st century BC - 4th century AD).
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Project description:Spatial analyses of pathogen occurrence in their natural surroundings entail unique opportunities for assessing in vivo drivers of disease epidemiology. Such studies are however confronted by the complexity of the landscape driving epidemic spread and disease persistence. Since relevant information on how the landscape influences epidemiological dynamics is rarely available, simple spatial models of spread are often used. In the current study we demonstrate both how more complex transmission pathways could be incorpoted to epidemiological analyses and how this can offer novel insights into understanding disease spread across the landscape. Our study is focused on Podosphaera plantaginis, a powdery mildew pathogen that transmits from one host plant to another by wind-dispersed spores. Its host populations often reside next to roads and thus we hypothesize that the road network influences the epidemiology of P. plantaginis. To analyse the impact of roads on the transmission dynamics, we consider a spatial dataset on the presence-absence records on the pathogen collected from a fragmented landscape of host populations. Using both mechanistic transmission modeling and statistical modeling with road-network summary statistics as predictors, we conclude the evident role of the road network in the progression of the epidemics: a phenomena which is manifested both in the enhanced transmission along the roads and in infections typically occurring at the central hub locations of the road network. We also demonstrate how the road network affects the spread of the pathogen using simulations. Jointly our results highlight how human alteration of natural landscapes may increase disease spread.
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Project description:Aiming at the dense urban road network vulnerability without structural negative consequences, this paper proposes a novel non-structural road network vulnerability analysis framework. Three aspects of the framework are mainly described: (i) the rationality of non-structural road network vulnerability, (ii) the metrics for negative consequences accounting for variant road conditions, and (iii) the introduction of a new vulnerability index based on user exposure. Based on the proposed methodology, a case study in the Sioux Falls network which was usually threatened by regular heavy snow during wintertime is detailedly discussed. The vulnerability ranking of links of Sioux Falls network with respect to heavy snow scenario is identified. As a result of non-structural consequences accompanied by conceivable degeneration of network, there are significant increases in generalized travel time costs which are measurements for "emotionally hurt" of topological road network.
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Project description:The chemical industry is of paramount importance to the world economy and this industrial sector represents a substantial income source for developing countries. However, the chemical plants producing inside an industrial district pose a great threat to the surrounding atmospheric environment and human health. Therefore, designing an appropriate and available air quality monitoring network (AQMN) is essential for assessing the effectiveness of deployed pollution-controlling strategies and facilities. As monitoring facilities located at inappropriate sites would affect data validity, a two-stage data-driven approach constituted of a spatio-temporal technique (i.e. Bayesian maximum entropy) and a multi-objective optimization model (i.e. maximum concentration detection capability and maximum dosage detection capability) is proposed in this paper. The approach aims at optimizing the design of an AQMN formed by gas sensor modules. Owing to the lack of long-term measurement data, our developed atmospheric dispersion simulation system was employed to generate simulated data for the above method. Finally, an illustrative case study was implemented to illustrate the feasibility of the proposed approach, and results imply that this work is able to design an appropriate AQMN with acceptable accuracy and efficiency.
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Project description:BackgroundFood-borne disease outbreaks constitute a large health burden on society. One of the challenges when investigating such outbreaks is to trace the origin of the outbreak. In this study, we consider a network model to determine the spatial origin of the contaminated food product that caused the outbreak.MethodsThe network model we use replaces the classic geographic distance of a network by an effective distance so that two nodes connected by a long-range link may be more strongly connected than their geographic distance would suggest. Furthermore, the effective distance transforms complex spatial patterns into regular topological patterns, creating a means for easier identification of the origin of the spreading phenomenon. Because detailed information on food distribution is generally not available, the model uses the gravity model from economics: the flow of goods from one node to another increases with population size and decreases with the geographical distance between them.ResultsThis effective distance network approach has been shown to perform well in a large Escherichia coli O104:H4 outbreak in Germany in 2011. In this article, we apply the same method to various food-borne disease outbreaks in the Netherlands. We found the effective distance network approach to fail in certain scenarios.ConclusionsGreat care should be taken as to whether the underlying network model correctly captures the spreading mechanism of the outbreak in terms of spatial scale and single or multiple source outbreak.
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