ArticleJournal of regional science2021
Bayesian spatiotemporal forecasting and mapping of COVID-19 risk with application to West Java Province, Indonesia.
Article in Journal of regional science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review.International journal of environmental research and public health · 2022Pooled it
- Respiratory viral infections: when and where? A scoping review of spatiotemporal methods.Journal of global health · 2025Article
- Assessing the vulnerability of urban public health system based on a hybrid model.Frontiers in public health · 2025Article
- Spatiotemporal Analysis of Out-of-Hospital Cardiac Arrest Incidence and Survival Outcomes in Korea (2009-2021).Journal of Korean medical science · 2024Observational
- Bayesian spatio-temporal analysis of the COVID-19 pandemic in Catalonia.Scientific reports · 2024Article
- Exploration of spatiotemporal heterogeneity and socio-demographic determinants on COVID-19 incidence rates in Sarawak, Malaysia.BMC public health · 2023Article
- Adaptive Gaussian Markov random field spatiotemporal models for infectious disease mapping and forecasting.Spatial statistics · 2023Article
- Spatio-temporal dengue risk modelling in the south of Thailand: a Bayesian approach to dengue vulnerability.PeerJ · 2023Article
- The burden of big spaces: Russian regions and cities in the COVID-19 pandemic.Letters in spatial and resource sciences · 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
- Near real time monitoring and forecasting for COVID-19 situational awareness.Applied geography (Sevenoaks, England) · 2022Article
- Identifying spatiotemporal patterns of COVID-19 transmissions and the drivers of the patterns in Toronto: a Bayesian hierarchical spatiotemporal modelling.Scientific reports · 2022Article
- Effects of Vaccination and the Spatio-Temporal Diffusion of Covid-19 Incidence in Turkey.Geographical analysis · 2022Article
- Spatiotemporal high-resolution prediction and mapping: methodology and application to dengue disease.Journal of geographical systems · 2022Article
- A Novel Predictor for Micro-Scale COVID-19 Risk Modeling: An Empirical Study from a Spatiotemporal Perspective.International journal of environmental research and public health · 2021Article
- Bayesian spatiotemporal forecasting and mapping of COVID-19 risk with application to West Java Province, Indonesia.Journal of regional science · 2021Article
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Authors and funding
2 authors.
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Abstract
The coronavirus disease (COVID-19) has spread rapidly to multiple countries including Indonesia. Mapping its spatiotemporal pattern and forecasting (small area) outbreaks are crucial for containment and mitigation strategies. Hence, we introduce a parsimonious space-time model of new infections that yields accurate forecasts but only requires information regarding the number of incidences and population size per geographical unit and time period. Model parsimony is important because of limited knowledge regarding the causes of COVID-19 and the need for rapid action to control outbreaks. We outline the basics of Bayesian estimation, forecasting, and mapping, in particular for the identification of hotspots. The methodology is applied to county-level data of West Java Province, Indonesia.
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