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Bayesian spatiotemporal forecasting and mapping of COVID‐19 risk with application to West Java Province, Indonesia
Author(s) -
Jaya I. Gede Nyoman M.,
Folmer Henk
Publication year - 2021
Publication title -
journal of regional science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.171
H-Index - 79
eISSN - 1467-9787
pISSN - 0022-4146
DOI - 10.1111/jors.12533
Subject(s) - outbreak , java , bayesian probability , covid-19 , computer science , geography , population , identification (biology) , econometrics , statistics , data mining , artificial intelligence , infectious disease (medical specialty) , mathematics , disease , demography , ecology , sociology , medicine , pathology , virology , biology , programming language
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.