Bayesian spatiotemporal forecasting and mapping of COVID‐19 risk with application to West Java Province, Indonesia

Jaya, I Gede Nyoman Mindra; Folmer, Henk

doi:10.1111/jors.12533

Cited by 31 publications

(42 citation statements)

References 99 publications

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“…Rapid response measures such as fogging are critical in areas with high dengue incidence. Finally, based on the experiences in the present paper, it is worthwhile to investigate the suitability of the FGG-GMRF model for a variety of other spatiotemporal problems, including other infectious diseases such as COVID-19 (see Jaya and Folmer 2021b ), vaccination coverage (Utazi et al 2019 ), particulate matter concentration (Cameletti et al 2013 ; Lee et al 2016 ), and social issues such as unemployment and crime.…”

Section: Discussionmentioning

confidence: 95%

“…The expected rate is calculated using external standardization. It is defined based on the overall average across all areas and periods (Abente et al 2018 ; Jaya and Folmer 2020 , 2021a , b ): …”

Section: The Spatiotemporal Generalized Geoadditive-gaussian Field Modelmentioning

confidence: 99%

“…Following Moraga et al ( 2017 ), Jaya and Folmer ( 2020 , 2021a , b ), and Utazi et al ( 2019 ), we model the relative risk as a non-separable Poisson log-linear model as follows 6 : …”

Section: The Spatiotemporal Generalized Geoadditive-gaussian Field Modelmentioning

confidence: 99%

“…However, for Bandung, socioeconomic risk variables such as income, education, occupation and living conditions are unavailable for districts and cells. These factors are accounted for by the random effects, thus controlling for omitted variable bias (Jaya and Folmer 2020 , 2021a , b ). By contrast, the monthly averages of the weather risk variables of precipitation (mm), temperature (°C), sunshine duration (kJ/m 2 day) and water vapor pressure (kPa) are available at cell level from the WorldClim2.0 database (Fick and Hijmans 2017 ), obtained from 19 weather stations surrounding Bandung in West Java for 1970–2000.…”

Section: Application: Relative Dengue Risk At Subdistrict Level In Ba...mentioning

confidence: 99%

“…High-resolution information is needed for research on the etiology of the disease and the development of control and prevention strategies (Ak et al 2018 ; Jaya et al 2017 ; Jaya and Folmer 2020 , 2021a , b ; Pokharel and Deardon 2016 ). Dengue disease typically involves a spatiotemporal pattern (Jaya and Folmer 2020 , 2021a , b ; Phanitchat et al 2019 ; Puggioni et al 2020 ); therefore, high-resolution spatiotemporal models and maps of the distribution of relative risk are basic elements of an early warning system aimed at identifying where and when an outbreak will occur (Hanigan et al 2019 ; Shi et al 2013 ; Xu et al 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal high-resolution prediction and mapping: methodology and application to dengue disease

Jaya

Folmer

2022

J Geogr Syst

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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 n” problem. Sub-area relative risk estimates are obtained as block averages of the cell outcomes within each sub-area boundary. The FGG-GMRF model is estimated by applying Bayesian Integrated Nested Laplace Approximation. In the application to Bandung city, Indonesia, we combine low-resolution area level (district) spatiotemporal data on population at risk and incidence and high-resolution cell level data on weather variables to obtain predictions of relative risk at subdistrict level. The predicted dengue relative risk at subdistrict level suggests significant fine-scale heterogeneities which are not apparent when examining the area level. The relative risk varies considerably across subdistricts and time, with the latter showing an increase in the period January–July and a decrease in the period August–December.

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Section: Discussionmentioning

confidence: 95%

Section: The Spatiotemporal Generalized Geoadditive-gaussian Field Modelmentioning

confidence: 99%

“…Following Moraga et al ( 2017 ), Jaya and Folmer ( 2020 , 2021a , b ), and Utazi et al ( 2019 ), we model the relative risk as a non-separable Poisson log-linear model as follows 6 : …”

Section: The Spatiotemporal Generalized Geoadditive-gaussian Field Modelmentioning

confidence: 99%