A spatial–temporal study of dengue in Peninsular Malaysia for the year 2017 in two different space–time model

ABSTRACT

Spatio-temporal disease mapping models give a great worth in epidemiology, especially in describing the pattern of disease incidence across geographical space and time. This paper analyses the spatial and temporal variability of dengue disease rates based on generalized linear mixed models. For spatio-temporal study, the models incorporate spatially correlated random effects as well as temporal effects. In this study, two different spatial random effects are applied and compared. The first model is based on Leroux spatial model, while the second model is based on the stochastic partial differential equation approach. For the temporal effects, both models follow an autoregressive model of first-order model. The models are fitted within a hierarchical Bayesian framework with integrated nested Laplace approximation methodology. The main objective of this study is to compare both spatio-temporal models in terms of their ability in representing the disease phenomenon. The models are applied to weekly dengue fever data in Peninsular Malaysia reported to the Ministry of Health Malaysia in the year 2017 according to the district level.

KEYWORDS:

• Disease mapping
• Bayesian estimation
• INLA
• SPDE
• dengue

Acknowledgments

Special thanks to Vector Borne Disease Sector, Ministry of Health Malaysia for providing dengue disease data to this study.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was financially supported by the Fundamental Research Grant Scheme (FRGS) offered by Ministry of Education Malaysia (203.PMATHS.6711666).