Flexible spatio-temporal strategies for modeling mosquito-borne diseases
| dc.catalogador | pva | |
| dc.contributor.advisor | Quintana Quintana, Fernando | |
| dc.contributor.author | Pavani, Jessica Letícia | |
| dc.contributor.other | Pontificia Universidad Católica de Chile. Facultad de Matemáticas | |
| dc.date | 2025-06-30 | |
| dc.date.accessioned | 2025-01-08T15:16:49Z | |
| dc.date.issued | 2024 | |
| dc.date.updated | 2025-01-07T20:45:40Z | |
| dc.description | Tesis (Doctor in Statistics)--Pontificia Universidad Católica de Chile, 2024 | |
| dc.description.abstract | Growing awareness of environmental threats has encouraged researchers to increasingly focus on analyzing spatial and temporal patterns of diseases, including vector-borne diseases. A byproduct of this is the also increased interest in cluster analysis. Over the last few decades, the frequency and magnitude of disease outbreaks caused by insects have increased dramatically. In addition to areas that are recurrently affected, outbreaks are spreading into regions that were previously unaffected. Faced with such a scenario, clustering analysis is essential for recognizing areas and times with high disease incidence, thus aiding in intervention planning. Moreover, the increasing availability of large datasets of high quality has culminated in the emergence of more sophisticated statistical models and methods. In response to this need, we have developed some flexible Bayesian approaches whose main goal is to identify and cluster neighboring regions where the infection behaves similarly, and to evaluate how the spatial clustering pattern changes over time. To begin with, we develop a technique for recognizing and grouping regions that display similar time-based patterns for a specific disease. Our method employs product partition models that take into account the influence of neighboring regions to cluster geographical data. This prior is tied to temporal modeling, as it aligns the classification of regions with their time trends. Consequently, the temporal coefficients are common among areas within the same cluster. Furthermore, we introduce a directed acyclic graph structure to manage the spatial dependencies among these regions. As a contribution to the literature on multivariate data, we extend the first approach to jointly modeling multiple diseases, explicitly accounting for potential space-time correlations between them. In this case, we employ a multivariate directed acyclic graph autoregressive framework to capture both spatial and inter-disease dependencies. In the initial two models, the spatial cluster stays unchanged throughout time. However, the challenge of modeling intensifies when we attempt to examine temporal changes across different spatial partitions. To address this, we introduce a model for time-dependent sequences of spatial random partitions, establishing a prior based on product partition models that correlate spatial configurations. By utilizing random spanning trees as a methodological tool, we ease the exploration of the complex partition search space. We validate the properties of all models through simulation studies, demonstrating its competitive performance against alternative approaches. Furthermore, we apply them to mosquito-borne diseases dataset in the Brazilian Southeast region. | |
| dc.description.version | 2025-06-30 | |
| dc.fechaingreso.objetodigital | 2025-01-07 | |
| dc.format.extent | xiii páginas, 199 páginas sin numerar | |
| dc.fuente.origen | Autoarchivo | |
| dc.identifier.doi | 10.7764/tesisUC/MAT/89566 | |
| dc.identifier.uri | https://doi.org/10.7764/tesisUC/MAT/89566 | |
| dc.identifier.uri | https://repositorio.uc.cl/handle/11534/89566 | |
| dc.information.autoruc | Facultad de Matemáticas; Quintana Quintana, Fernando; 0000-0002-9088-756X; 100343 | |
| dc.information.autoruc | Facultad de Matemáticas; Pavani, Jessica Letícia; 0000-0001-9613-2400; 1092791 | |
| dc.language.iso | en | |
| dc.nota.acceso | contenido completo | |
| dc.rights | acceso abierto | |
| dc.rights.license | Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0) | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-sa/4.0/deed.es | |
| dc.subject.ddc | 510 | |
| dc.subject.dewey | Matemática física y química | es_ES |
| dc.subject.ods | 03 Good health and well-being | |
| dc.subject.odspa | 03 Salud y bienestar | |
| dc.title | Flexible spatio-temporal strategies for modeling mosquito-borne diseases | |
| dc.type | tesis doctoral | |
| sipa.codpersvinculados | 100343 | |
| sipa.codpersvinculados | 1092791 |