scholarly journals Spatial and spatio-temporal analysis of Salmonella infection in dairy herds in England and Wales

2008 ◽  
Vol 137 (6) ◽  
pp. 847-857 ◽  
Author(s):  
S. E. FENTON ◽  
H. E. CLOUGH ◽  
P. J. DIGGLE ◽  
S. J. EVANS ◽  
H. C. DAVISON ◽  
...  
Keyword(s):  
Transition Probabilities ◽  
Spatial Clustering ◽  
Markov Chain Model ◽  
Temporal Analysis ◽  
Dairy Herds ◽  
Chain Model ◽  
Time Period ◽  
Spatio Temporal ◽  
Using Data ◽  
Residual Deviance

SUMMARYUsing data from a cohort study conducted by the Veterinary Laboratories Agency (VLA), evidence of spatial clustering at distances up to 30 km was found for S. Agama and S. Dublin (P values of 0·001) and borderline evidence was found for spatial clustering of S. Typhimurium (P=0·077). The evolution of infection status of study farms over time was modelled using a Markov Chain model with transition probabilities describing changes in status at each of four visits, allowing for the effect of sampling visit. The degree of geographical clustering of infection, having allowed for temporal effects, was assessed by comparing the residual deviance from a model including a measure of recent neighbourhood infection levels with one excluding this variable. The number of cases arising within a defined distance and time period of an index case was higher than expected. This provides evidence for spatial and spatio-temporal clustering, which suggests either a contagious process (e.g. through direct or indirect farm-to-farm transmission) or geographically localized environmental and/or farm factors which increase the risk of infection. The results emphasize the different epidemiology of the three Salmonella serovars investigated.

Data Mining Technique Applied To DNA Sequencing

2015 ◽  
Vol 2 (7) ◽  
pp. 18
Author(s):  
R. Jamuna
Keyword(s):  
Data Mining ◽  
Dna Methylation ◽  
Markov Chain ◽  
Human Genome ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Effective Means ◽  
Cpg Islands ◽  
Chain Model ◽  
The Given

CpG islands (CGIs) play a vital role in genome analysis as genomic markers.  Identification of the CpG pair has contributed not only to the prediction of promoters but also to the understanding of the epigenetic causes of cancer. In the human genome [1] wherever the dinucleotides CG occurs the C nucleotide (cytosine) undergoes chemical modifications. There is a relatively high probability of this modification that mutates C into a T. For biologically important reasons the mutation modification process is suppressed in short stretches of the genome, such as ‘start’ regions. In these regions [2] predominant CpG dinucleotides are found than elsewhere. Such regions are called CpG islands. DNA methylation is an effective means by which gene expression is silenced. In normal cells, DNA methylation functions to prevent the expression of imprinted and inactive X chromosome genes. In cancerous cells, DNA methylation inactivates tumor-suppressor genes, as well as DNA repair genes, can disrupt cell-cycle regulation. The most current methods for identifying CGIs suffered from various limitations and involved a lot of human interventions. This paper gives an easy searching technique with data mining of Markov Chain in genes. Markov chain model has been applied to study the probability of occurrence of C-G pair in the given   gene sequence. Maximum Likelihood estimators for the transition probabilities for each model and analgously for the  model has been developed and log odds ratio that is calculated estimates the presence or absence of CpG is lands in the given gene which brings in many  facts for the cancer detection in human genome.

scholarly journals Analysis and Prediction of Pedestrians’ Violation Behavior at the Intersection Based on a Markov Chain

2021 ◽  
Vol 13 (10) ◽  
pp. 5690
Author(s):  
Chengyuan Mao ◽  
Lewen Bao ◽  
Shengde Yang ◽  
Wenjiao Xu ◽  
Qin Wang
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Warning System ◽  
Demographic Characteristics ◽  
Chain Model ◽  
Behavior Prediction ◽  
Road Geometry ◽  
Using Data ◽  
Pedestrian Crossing ◽  
Prediction Approach

Pedestrian violations pose a danger to themselves and other road users. Most previous studies predict pedestrian violation behaviors based only on pedestrians’ demographic characteristics. In practice, in addition to demographic characteristics, other factors may also impact pedestrian violation behaviors. Therefore, this study aims to predict pedestrian crossing violations based on pedestrian attributes, traffic conditions, road geometry, and environmental conditions. Data on the pedestrian crossing, both in compliance and in violation, were collected from 10 signalized intersections in the city of Jinhua, China. We propose an illegal pedestrian crossing behavior prediction approach that consists of a logistic regression model and a Markov Chain model. The former calculates the likelihood that the first pedestrian who decides to cross the intersection illegally within each signal cycle, while the latter computes the probability that the subsequent pedestrians who decides to follow the violation. The proposed approach was validated using data gathered from an additional signalized intersection in Jinhua city. The results show that the proposed approach has a robust ability in pedestrian violation behavior prediction. The findings can provide theoretical references for pedestrian signal timing, crossing facility optimization, and warning system design.

scholarly journals Calibration of Transition Intensities for a Multistate Model: Application to Long-Term Care

Risks ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 37
Author(s):  
Manuel L. Esquível ◽  
Gracinda R. Guerreiro ◽  
Matilde C. Oliveira ◽  
Pedro Corte Real
Keyword(s):  
Markov Chain ◽  
Continuous Time ◽  
Long Term Care ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Chain Model ◽  
Chain Process ◽  
Term Care ◽  
National Network

We consider a non-homogeneous continuous time Markov chain model for Long-Term Care with five states: the autonomous state, three dependent states of light, moderate and severe dependence levels and the death state. For a general approach, we allow for non null intensities for all the returns from higher dependence levels to all lesser dependencies in the multi-state model. Using data from the 2015 Portuguese National Network of Continuous Care database, as the main research contribution of this paper, we propose a method to calibrate transition intensities with the one step transition probabilities estimated from data. This allows us to use non-homogeneous continuous time Markov chains for modeling Long-Term Care. We solve numerically the Kolmogorov forward differential equations in order to obtain continuous time transition probabilities. We assess the quality of the calibration using the Portuguese life expectancies. Based on reasonable monthly costs for each dependence state we compute, by Monte Carlo simulation, trajectories of the Markov chain process and derive relevant information for model validation and premium calculation.

scholarly journals Spatial and Temporal Analysis of Dry and Wet Spells in Upper Awash River Basin, Ethiopia

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3051
Author(s):  
Girma Berhe Adane ◽  
Birtukan Abebe Hirpa ◽  
Chul-Hee Lim ◽  
Woo-Kyun Lee
Keyword(s):  
River Basin ◽  
Management Practices ◽  
Markov Chain Model ◽  
Temporal Analysis ◽  
Chain Model ◽  
Rainfall Analysis ◽  
Dry Spells ◽  
Dry Spell ◽  
Awash River Basin ◽  
Wet Spells

This study aimed to analyze the probability of the occurrence of dry/wet spell rainfall using the Markov chain model in the Upper Awash River Basin, Ethiopia. The rainfall analysis was conducted in the short rainy (Belg) and long rainy (Kiremt) seasons on a dekadal (10–day) scale over a 30-year period. In the Belg season, continuous, three-dekad dry spells were prevalent at all stations. Persistent dry spells might result in meteorological, hydrological, and socio-economic drought (in that order) and merge with the Kiremt season. The consecutive wet dekads of the Kiremt season indicate a higher probability of wet dekads at all stations, except Metehara. This station experienced a short duration (dekads 20–23) of wet spells, in which precipitation is more than 50% likely. Nevertheless, surplus rainwater may be recorded at Debrezeit and Wonji only in the Kiremt season because of a higher probability of wet spells in most dekads (dekads 19–24). At these stations, rainfall can be harvested for better water management practices to supply irrigation during the dry season, to conserve moisture, and to reduce erosion. This reduces the vulnerability of the farmers around the river basin, particularly in areas where dry spell dekads are dominant.

scholarly journals Will this search end up with booking? Modeling airline booking conversion of anonymous visitors

2020 ◽  
Vol 27 (2) ◽  
pp. 237-250
Author(s):  
Misuk Lee
Keyword(s):  
Markov Chain ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Distribution Channel ◽  
Stochastic Approach ◽  
Tourism Industry ◽  
Chain Model ◽  
Online Search ◽  
Clickstream Data ◽  
Content Type

Purpose Over the past two decades, online booking has become a predominant distribution channel of tourism products. As online sales have become more important, understanding booking conversion behavior remains a critical topic in the tourism industry. The purpose of this study is to model airline search and booking activities of anonymous visitors. Design/methodology/approach This study proposes a stochastic approach to explicitly model dynamics of airline customers’ search, revisit and booking activities. A Markov chain model simultaneously captures transition probabilities and the timing of search, revisit and booking decisions. The suggested model is demonstrated on clickstream data from an airline booking website. Findings Empirical results show that low prices (captured as discount rates) lead to not only booking propensities but also overall stickiness to a website, increasing search and revisit probabilities. From the decision timing of search and revisit activities, the author observes customers’ learning effect on browsing time and heterogeneous intentions of website visits. Originality/value This study presents both theoretical and managerial implications of online search and booking behavior for airline and tourism marketing. The dynamic Markov chain model provides a systematic framework to predict online search, revisit and booking conversion and the time of the online activities.

scholarly journals The Spatio-temporal Analysis of b-value in the Banda Arc, Indonesia

2021 ◽  
Vol 873 (1) ◽  
pp. 012010
Author(s):  
Muhammad Bani Al-Rasyid ◽  
Mira Nailufar Rusman ◽  
Daniel Hamonangan ◽  
Pepen Supendi ◽  
Kartika Hajar Kirana
Keyword(s):  
Temporal Variation ◽  
Temporal Analysis ◽  
B Value ◽  
Spatial And Temporal Variation ◽  
Tectonic Structure ◽  
Time Period ◽  
Banda Arc ◽  
Spatio Temporal ◽  
The Impact ◽  
Large Earthquakes

Abstract Banda arc is a complex tectonic structure manifests by high seismicity due to the collision of a continent and an intra-oceanic island arc. Using the relocated earthquakes data from ISC-EHB and BMKG catalogues from the time period of 1960 to 2018, we have conducted a spatial and temporal variation of b-value using the Guttenberg-Richter formula in the area. Our results show that the spatial distribution of low b-values located in the south of Ambon Island and southeast of Buru Island. On the other hand, the temporal variation of b-value shows a decrease in the northern part of the Banda sea probably high potential to produce large earthquakes in the future. Therefore, further mitigation is needed to minimize the impact of earthquakes in the area.

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