Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Ramasamy, Jaisankar; Murugesan, Ranjani Title A name given to the resource BAYESIAN SPATIAL TEMPORAL TREND ANALYSIS FOR DECISION MAKING AND RISK ASSESSMENT IN DENGUE INCIDENCE STUDIES: A CASE OF TAMILNADU Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Reliability: Theory and Applications;Volume;20;Issue;1;pp.219-226 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.24412/1932-2321-2025-182-219-226" target="_blank" rel="noreferrer noopener">https://doi.org/10.24412/1932-2321-2025-182-219-226</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105001149183?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105001149183?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Ramasamy J., Department of Statistics, Bharathiar University, Tamil Nadu, Coimbatore, India; Murugesan R., Department of Statistics and Data Science, Christ University, Karnataka, Bengaluru, India Description An account of the resource This study presents a Bayesian spatial-temporal analysis for studying Dengue incidence in Tamil Nadu, aiming to provide insights into decision-making and risk assessment strategies. Statistical models that allow a more accurate depiction of true disease rates by borrowing information from neighboring regions will help mitigate the effects of sparsely populated regions and deliver better inference. Perhaps the most conspicuous manner of modeling spatial dependence is to introduce spatially associated random effects within a Bayesian hierarchical setting. The Bayesian modeling and inferential framework are flexible and extremely rich in its capabilities to accumulate various scientific hypotheses and assumptions. The spatial and spatial temporal epidemiology is concerned with the description and analysis of spatial and spatial temporal variations in disease risk with respect to risk factors. As the primary aim of this work is to quantify the spatial disease pattern of dengue incidences apart from the mapping of disease modelling the disease and finding spatial clusters/hotpots is one important aspect in epidemiology is to find the temporal trends in or outside of clusters. In this study, a spatial-temporal trends model is fitted using the Leroux CAR priors set up for studying the spatial-temporal disease patterns with the estimation of the temporal trends with reference to dengue incidences in Tamil Nadu, India. 2025, Gnedenko Forum. All rights reserved. Subject The topic of the resource Bayesian hierarchical modeling; Bayesian modeling; Leroux CAR prior; Spatial temporal Publisher An entity responsible for making the resource available Gnedenko Forum Relation A related resource ISSN: 19322321; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online