What is it about?
Dengue fever affects more than 100 million every year and is one of the most important mosquito-borne diseases in the world. The aims of this systematic review were to identify and review published spatial and spatio-temporal model that have been applied to dengue fever, assess analytical methods including structure of the model and the inclusion of covariates.
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Why is it important?
Bayesian models are becoming increasingly popular for small-area analyses. Our findings shows that the most frequent Bayesian statistical approaches to dengue modelling was a generalised linear mixed model with spatial random effects described by a conditional auto-regressive prior. Temperature and precipitation were shown to often influence the risk of dengue.
Perspectives
This paper was my first examining Bayesian spatial and spatio-temporal models, and gave a solid understanding of the current modelling approaches for dengue fever. It was enjoyable to work with my co-authors on this, and this article has importance for anyone investigating dengue fever, as well as people involved in statistics.
Aswi Aswi
Queensland University of Technology
Read the Original
This page is a summary of: Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review, Epidemiology and Infection, October 2018, Cambridge University Press,
DOI: 10.1017/s0950268818002807.
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