ArticleJournal of geographical systems2022
Spatiotemporal high-resolution prediction and mapping: methodology and application to dengue disease.
Article in Journal of geographical systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of the data, methods and environmental covariates used to map Aedes-borne arbovirus transmission risk.BMC infectious diseases · 2023Pooled it
- A Systematic Review on Modeling Methods and Influential Factors for Mapping Dengue-Related Risk in Urban Settings.International journal of environmental research and public health · 2022Pooled it
- Venous Nanoflap Oscillations: Biomechanical Determinants and Hydrodynamic Consequences in the Deep Cerebral Venous System.International journal of molecular sciences · 2026Review
- Review
- Article
- Spatial autocorrelation of environmental factors influencing dengue outbreaks using Moran's I: A study from Nepal (2020-2023).PloS one · 2025Article
- Bayesian spatio-temporal analysis of dengue transmission in Lao PDR.Scientific reports · 2024Article
- Predicting the dengue cluster outbreak dynamics in Yogyakarta, Indonesia: a modelling study.The Lancet regional health. Southeast Asia · 2023Article
- A joint Bayesian spatiotemporal risk prediction model of COVID-19 incidence, IC admission, and death with application to Sweden.The Annals of regional science · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dengue disease has become a major public health problem. Accurate and precise identification, prediction and mapping of high-risk areas are crucial elements of an effective and efficient early warning system in countering the spread of dengue disease. In this paper, we present the fusion area-cell spatiotemporal generalized geoadditive-Gaussian Markov random field (FGG-GMRF) framework for joint estimation of an area-cell model, involving temporally varying coefficients, spatially and temporally structured and unstructured random effects, and spatiotemporal interaction of the random effects. The spatiotemporal Gaussian field is applied to determine the unobserved relative risk at cell level. It is transformed to a Gaussian Markov random field using the finite element method and the linear stochastic partial differential equation approach to solve the "big Supplementary Information: The online version contains supplementary material available at 10.1007/s10109-021-00368-0.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.