scholarly journals Airline transportation and arrival time of international disease spread: A case study of Covid-19

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256398
Author(s):  
Pei-Fen Kuo ◽  
Chui-Sheng Chiu
Keyword(s):  
Random Walk ◽  
Shortest Path ◽  
Cox Model ◽  
Predictive Performance ◽  
Survival Model ◽  
Disease Spread ◽  
Geographical Distance ◽  
Effective Distance ◽  
Arrival Times ◽  
Distance Model

In this era of globalization, airline transportation has greatly increased international trade and travel within the World Airport Network (WAN). Unfortunately, this convenience has expanded the scope of infectious disease spread from a local to a worldwide occurrence. Thus, scholars have proposed several methods to measure the distances between airports and define the relationship between the distances and arrival times of infectious diseases in various countries. However, such studies suffer from the following limitations. (1) Only traditional statistical methods or graphical representations were utilized to show that the effective distance performed better than the geographical distance technique. Researchers seldom use the survival model to quantify the actual differences among arrival times via various distance methods. (2) Although scholars have found that most diseases tend to spread via the random walk rather than the shortest path method, this hypothesis may no longer be true because the network has been severally altered due to recent COVID-related travel reductions. Therefore, we used 2017 IATA (International Air Transport Association) to establish an airline network via various chosen path strategies (random walk and shortest path). Then, we employed these two networks to quantify each model’s predictive performance in order to estimate the importation probability function of COVID-19 into various countries. The effective distance model was found to more accurately predict arrival dates of COVID-19 than the geographical distance model. However, if pre-Covid airline data is included, the path of disease spread might not follow the random walk theory due to recent flight suspensions and travel restrictions during the epidemic. Lastly, when testing effective distance, the inverse distance survival model and the Cox model yielded very similar importation risk estimates. The results can help authorities design more effective international epidemic prevention and control strategies.

scholarly journals Static and dynamic properties of selected stochastic processes on complex networks

2020 ◽  
Author(s):  
Jeremi Ochab
Keyword(s):  
Random Walk ◽  
Complex Networks ◽  
Random Walks ◽  
Dynamic Properties ◽  
Finite Size ◽  
Disease Spread ◽  
Maximal Entropy ◽  
Centrality Measures ◽  
Topological Features ◽  
The Subject

This thesis is concerned with the properties of a number of selected processes taking place on complex networks and the way they are affected by structure and evolution of the networks. What is meant here by 'complex networks' is the graph-theoretical representations and models of various empirical networks (e.g., the Internet network) which contain both random and deterministic structures, and are characterised among others by the small-world phenomenon, power-law vertex degree distributions, or modular and hierarchical structure. The mathematical models of the processes taking place on these networks include percolation and random walks we utilise.The results presented in the thesis are based on five thematically coherent papers. The subject of the first paper is calculating thresholds for epidemic outbreaks on dynamic networks, where the disease spread is modelled by percolation. In the paper, known analytical solutions for the epidemic thresholds were extended to a class of dynamically evolving networks; additionally, the effects of finite size of the network on the magnitude of the epidemic were studied numerically. The subject of the second and third paper is the static and dynamic properties of two diametrically opposed random walks on model highly symmetric deterministic graphs. Specifically, we analytically and numerically find the stationary states and relaxation times of the ordinary, diffusive random walk and the maximal-entropy random walk. The results provide insight into localisation of random walks or their trapping in isolated regions of networks. Finally, in the fourth and fifth paper, we examine the utility of random walks in detecting topological features of complex networks. In particular, we study properties of the centrality measures (roughly speaking, the ranking of vertices) based on random walks, as well as we conduct a systematic comparative study of random-walk based methods of detecting modular structure of networks.These studies thus aimed at specific problems in modelling and analysis of complex networks, including theoretical examination of the ways the behaviour of random processes intertwines with the structure of complex networks.

A Comparative Study of Graph Kernels and Clustering Algorithms

2021 ◽  
Vol 12 (1) ◽  
pp. 33-48
Author(s):  
Riju Bhattacharya ◽  
Naresh Kumar Nagwani ◽  
Sarsij Tripathi
Keyword(s):  
Random Walk ◽  
Comparative Study ◽  
Shortest Path ◽  
Kernel Method ◽  
Graph Clustering ◽  
Self Organizing Map ◽  
Clustering Methods ◽  
Clustering Techniques ◽  
Graph Kernels ◽  
Silhouette Index

Graph kernels have evolved as a promising and popular method for graph clustering over the last decade. In this work, comparative study on the five standard graph kernel techniques for graph clustering has been performed. The graph kernels, namely vertex histogram kernel, shortest path kernel, graphlet kernel, k-step random walk kernel, and Weisfeiler-Lehman kernel have been compared for graph clustering. The clustering methods considered for the kernel comparison are hierarchical, k-means, model-based, fuzzy-based, and self-organizing map clustering techniques. The comparative study of kernel methods over the clustering techniques is performed on MUTAG benchmark dataset. Clustering performance is assessed with internal validation performance parameters such as connectivity, Dunn, and the silhouette index. Finally, the comparative analysis is done to facilitate researchers for selecting the appropriate kernel method for effective graph clustering. The proposed methodology elicits k-step random walk and shortest path kernel have performed best among all graph clustering approaches.

Using Radiomics-based Modeling to Predict Individual Progression From Mild Cognitive Impairment to Alzheimer’s Disease

2021 ◽  
Author(s):  
Jiehui Jiang ◽  
Xiaoming Sun ◽  
Ian Alberts ◽  
Min Wang ◽  
Axel Rominger ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Fdg Pet ◽  
Cox Model ◽  
Standardized Uptake Value ◽  
Predictive Performance ◽  
Clinical Model ◽  
Neuropsychological Assessments

Abstract Background: Predicting the risk of disease progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) has important clinical significance. This study aims at providing a personalized MCI-to-AD conversion prediction via radiomics-based predictive modeling (RPM) with multicentre 18F-Fluorodeoxyglucose positron emission tomography (FDG PET) data. Method: Three cohorts of 18F-FDG PET data and neuropsychological assessments were gathered from patients examined at Huashan Hospital (n=22), Xuanwu Hospital (n=80), and from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (n=355). Of these, amyloid images were selected for the ADNI and Xuanwu cohorts. First, 430 radiomic features were extracted from the 80 regions of interest (ROIs) for all PET images. These features were then concatenated for feature selection and an RPM model was constructed on the ADNI dataset. In addition, we used clinical scale data to establish a clinical Cox model, and a combined model for comparison. Afterwards, the images from Huashan Hospital were used to validate the stability and reliability of RPM, and the images from Xuanwu Hospital were used to explore the differences of biomarkers at different cognitive stages. Finally, correlation analysis was conducted between the radiomic biomarkers, neuropsychological assessments, and amyloid burden. Results: Experimental results show that the predictive performance of the PET-modal cox model was better than clinical cox model. In the two test data sets, the C index of PET model is 0.75 and 0.73, respectively; The C index of clinical model is 0.68. Moreover, most crucial image biomarkers had significant differences at different cognitive stages, and were significantly correlated with cognitive ability and the amyloid global level standardized uptake value ratio.Conclusion: The preliminary results demonstrated that the developed RPM approach has the potential to monitor the progress in high-risk populations with AD.

scholarly journals Prediction of conversion to Alzheimer’s disease using deep survival analysis of MRI images

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Tomonori Nakagawa ◽  
Manabu Ishida ◽  
Junpei Naito ◽  
Atsushi Nagai ◽  
Shuhei Yamaguchi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Survival Analysis ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cox Model ◽  
Brain Magnetic Resonance Imaging ◽  
Brain Regions ◽  
Survival Model ◽  
Imaging Data

Abstract The prediction of the conversion of healthy individuals and those with mild cognitive impairment to the status of active Alzheimer’s disease is a challenging task. Recently, a survival analysis based upon deep learning was developed to enable predictions regarding the timing of an event in a dataset containing censored data. Here, we investigated whether a deep survival analysis could similarly predict the conversion to Alzheimer’s disease. We selected individuals with mild cognitive impairment and cognitively normal subjects and used the grey matter volumes of brain regions in these subjects as predictive features. We then compared the prediction performances of the traditional standard Cox proportional-hazard model, the DeepHit model and our deep survival model based on a Weibull distribution. Our model achieved a maximum concordance index of 0.835, which was higher than that yielded by the Cox model and comparable to that of the DeepHit model. To our best knowledge, this is the first report to describe the application of a deep survival model to brain magnetic resonance imaging data. Our results demonstrate that this type of analysis could successfully predict the time of an individual’s conversion to Alzheimer’s disease.

Estimating and Forecasting a Term Structure of Interest Rates with State-Space Nelson-Siegel Model

2011 ◽  
Vol 19 (3) ◽  
pp. 309-334
Author(s):  
Joonhyuk Song
Keyword(s):  
Random Walk ◽  
Interest Rates ◽  
State Space ◽  
Term Structure ◽  
Yield Curve ◽  
Predictive Ability ◽  
Predictive Performance ◽  
The State ◽  
Space Representation ◽  
Forecast Horizon

This paper estimates a Nelson-Siegel model under the state-space representation in order to circumvent the shortcomings of the conventional Nelson-Siegel model and evaluates the predictive ability of the estimated model. The results indicate that the estimated Nelson-Siegel time-varying three factors have close relations to their counterparts : level, slope and curvature and the inflection of the Korean yield curve is located around the maturity of 55-month. Meanwhile, each factor is found to have unit-root but differenced-factors do not show signs of unit-roots, hence proved I (1) series. In order to assess the efficacy of the estimated model, we compare the yield prediction from our model with several natural competitors : random walk, Fama-Bliss, and Cochrane-Piazzesi. With respect to out-of-sample performance, Fama-Bliss model proves to be the worst in term structure forecasts in Korea. The predictive performance differs between the random walk and the state-space Nelson-Siegel model depending on the forecast horizon lengths. At the shorter horizon, the state-space Nelson-Siegel model outperforms the random walk, but the table is turned in the longer horizon

scholarly journals A Review of Data Analytic Applications in Road Traffic Safety. Part 2: Prescriptive Modeling

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1096 ◽  
Author(s):  
Qiong Hu ◽  
Miao Cai ◽  
Nasrin Mohabbati-Kalejahi ◽  
Amir Mehdizadeh ◽  
Mohammad Ali Alamdar Yazdi ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Shortest Path ◽  
Traffic Safety ◽  
Road Traffic ◽  
Predictive Performance ◽  
Risk Indicators ◽  
Crash Risk ◽  
Future Research ◽  
Road Traffic Safety ◽  
Prescriptive Model

In the first part of the review, we observed that there exists a significant gap between the predictive and prescriptive models pertaining to crash risk prediction and minimization, respectively. In this part, we review and categorize the optimization/ prescriptive analytic models that focus on minimizing crash risk. Although the majority of works in this segment of the literature are related to the hazardous materials (hazmat) trucking problems, we show that (with some exceptions) many can also be utilized in non-hazmat scenarios. In an effort to highlight the effect of crash risk prediction model on the accumulated risk obtained from the prescriptive model, we present a simulated example where we utilize four risk indicators (obtained from logistic regression, Poisson regression, XGBoost, and neural network) in the k-shortest path algorithm. From our example, we demonstrate two major designed takeaways: (a) the shortest path may not always result in the lowest crash risk, and (b) a similarity in overall predictive performance may not always translate to similar outcomes from the prescriptive models. Based on the review and example, we highlight several avenues for future research.

scholarly journals PRYNT: a tool for prioritization of disease candidates from proteomics data using a combination of shortest-path and random walk algorithms

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Franck Boizard ◽  
Bénédicte Buffin-Meyer ◽  
Julien Aligon ◽  
Olivier Teste ◽  
Joost P. Schanstra ◽  
...  
Keyword(s):  
Random Walk ◽  
Kidney Disease ◽  
Shortest Path ◽  
Kidney Tissue ◽  
Proteomics Data ◽  
Protein Protein Interaction ◽  
Urinary Proteome ◽  
Link Type ◽  
Upstream Regulator ◽  
Better Than

AbstractThe urinary proteome is a promising pool of biomarkers of kidney disease. However, the protein changes observed in urine only partially reflect the deregulated mechanisms within kidney tissue. In order to improve on the mechanistic insight based on the urinary protein changes, we developed a new prioritization strategy called PRYNT (PRioritization bY protein NeTwork) that employs a combination of two closeness-based algorithms, shortest-path and random walk, and a contextualized protein–protein interaction (PPI) network, mainly based on clique consolidation of STRING network. To assess the performance of our approach, we evaluated both precision and specificity of PRYNT in prioritizing kidney disease candidates. Using four urinary proteome datasets, PRYNT prioritization performed better than other prioritization methods and tools available in the literature. Moreover, PRYNT performed to a similar, but complementary, extent compared to the upstream regulator analysis from the commercial Ingenuity Pathway Analysis software. In conclusion, PRYNT appears to be a valuable freely accessible tool to predict key proteins indirectly from urinary proteome data. In the future, PRYNT approach could be applied to other biofluids, molecular traits and diseases. The source code is freely available on GitHub at: https://github.com/Boizard/PRYNT and has been integrated as an interactive web apps to improved accessibility (https://github.com/Boizard/PRYNT/tree/master/AppPRYNT).

scholarly journals Epithelial-to-Mesenchymal Transition and Neoangiogenesis in Laryngeal Squamous Cell Carcinoma

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3339
Author(s):  
Leonardo Franz ◽  
Lorenzo Nicolè ◽  
Anna Chiara Frigo ◽  
Giancarlo Ottaviano ◽  
Piergiorgio Gaudioso ◽  
...  
Keyword(s):  
Large Scale ◽  
Epithelial To Mesenchymal Transition ◽  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Cox Model ◽  
Disease Free Survival ◽  
Predictive Performance ◽  
Therapy Resistance ◽  
Prognostic Models ◽  
Mesenchymal Transition

The mechanism of epithelial–mesenchymal transition (EMT) is fundamental for carcinogenesis, tumor progression, cancer cell invasion, metastasis, recurrence, and therapy resistance, comprising important events, such as cellular junction degradation, downregulation of epithelial phenotype markers, overexpression of mesenchymal markers, and increase in cellular motility. The same factors that drive epithelial cells toward a mesenchymal phenotype may also drive endothelial cells toward a proangiogenic phenotype. The aim of this exploratory study was to investigate a potential interplay between EMT and angiogenesis (quantified through CD105 expression) in laryngeal carcinoma (LSCC). CD105-assessed microvessel density (MVD) and EMT markers (E-cadherin, N-cadherin, Snail, Slug, Zeb1, and Zeb2) were assessed on 37 consecutive LSCC cases. The univariate Cox regression model identified pN+ status (p = 0.0343) and Slug expression (p = 0.0268) as predictive of disease-free survival (DFS). A trend toward significance emerged for CD105-assessed MVD (p = 0.0869) and N-cadherin expression (p = 0.0911). In the multivariate Cox model, pN-status, Slug, and N-cadherin expressions retained their significant values in predicting DFS (p = 0.0346, p = 0.0430, and p = 0.0214, respectively). Our data support the hypothesis of a mutual concurrence of EMT and angiogenesis in driving LSCC cells toward an aggressive phenotype. To better characterize the predictive performance of prognostic models based on EMT and angiogenesis, further large-scale prospective studies are required.

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