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

I. Gede Nyoman M. Jaya*, Henk Folmer

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

26 Citaten (Scopus)
35 Downloads (Pure)


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.

Originele taal-2English
Pagina's (van-tot)849-881
Aantal pagina's33
TijdschriftJournal of Regional Science
Nummer van het tijdschrift4
StatusPublished - sep.-2021

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