Academic Journal
Similarity- and neighbourhood-based dynamic models for infection data ; Uncovering the complexities of the COVID-19 infection risks
| العنوان: | Similarity- and neighbourhood-based dynamic models for infection data ; Uncovering the complexities of the COVID-19 infection risks |
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| المؤلفون: | Baptista, Helena, Mendes, Jorge M., MacNab, Ying C. |
| المساهمون: | Information Management Research Center (MagIC) - NOVA Information Management School, NOVA Information Management School (NOVA IMS), Comprehensive Health Research Centre (CHRC) - pólo NMS, NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM) |
| سنة النشر: | 2024 |
| المجموعة: | Repositório da Universidade Nova de Lisboa (UNL) |
| مصطلحات موضوعية: | COVID-19, Gaussian Markov random field, Similarity-based Gaussian Markov random fields, Adaptive modelling, Forecasting, Epidemiology, Geography, Planning and Development, Infectious Diseases, Health, Toxicology and Mutagenesis, SDG 3 - Good Health and Well-being, SDG 10 - Reduced Inequalities |
| الوصف: | Baptista, H., Mendes, J. M., & MacNab, Y. C. (2024). Similarity- and neighbourhood-based dynamic models for infection data: Uncovering the complexities of the COVID-19 infection risks. Spatial and Spatio-temporal Epidemiology, 51, 1-11. Article 100681. https://doi.org/10.1016/j.sste.2024.100681 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), Portugal , under the project UIDB/04152/2020 (DOI:10.54499/UIDB/04152/2020) - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. ; Understanding spatial and temporal risk dependencies and correlation is crucial when studying infectious diseases which spread out in consecutive waves. By analysing weekly COVID-19 case data collected from the disease’s first reported case on March 3, 2020, to April 22, 2021, in 278 municipalities in Mainland Portugal, we demonstrate that the complexity of infection risks varies based on the outbreak’s severity, suggesting that a single model definition is insufficient to explain the multifaceted underlying phenomena. This study employs a dynamic, conditionally specified Gaussian Markov random field model with a novel approach to characterise COVID-19 infection risk dependencies through the similarity of areal-level covariates within a Bayesian hierarchical model framework that accounts for each identifiable wave. The results indicate that the neighbourhood-based conditional autoregressive model, which is static and based on an adjacency-based neighbourhood matrix, do not necessarily captures the disease’s complex spatial–temporal nature. Furthermore, the best-fitting dynamic model may not necessarily be the best predicting model in certain situations, which can lead to inadequate resource allocation in epidemic situations. Accurate forecasting can help inform decisions regarding difficult-to-measure impacts, potentially saving lives. Implementing the proposed novel approach would have produced information that would have been overwhelmingly critical to the respective ... |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| Relation: | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT; https://doi.org/10.54499/UIDB/04152/2020; PURE: 99312746; crossref: 10.1016/j.sste.2024.100681; Scopus: 85203145530; WOS: 001309721600001; http://hdl.handle.net/10362/171610; https://doi.org/10.1016/j.sste.2024.100681 |
| DOI: | 10.1016/j.sste.2024.100681 |
| الاتاحة: | http://hdl.handle.net/10362/171610 https://doi.org/10.1016/j.sste.2024.100681 |
| Rights: | openAccess |
| رقم الانضمام: | edsbas.43D8A4B6 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1016/j.sste.2024.100681 |
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