Dengue Disease Mapping with Standardized Morbidity Ratio and Poisson-gamma Model: An Analysis of Dengue Disease in Perak, Malaysia

Authors: N. A. Samat, S. H. Mohd Imam Ma’arof

Abstract:

Dengue disease is an infectious vector-borne viral disease that is commonly found in tropical and sub-tropical regions, especially in urban and semi-urban areas, around the world and including Malaysia. There is no currently available vaccine or chemotherapy for the prevention or treatment of dengue disease. Therefore prevention and treatment of the disease depend on vector surveillance and control measures. Disease risk mapping has been recognized as an important tool in the prevention and control strategies for diseases. The choice of statistical model used for relative risk estimation is important as a good model will subsequently produce a good disease risk map. Therefore, the aim of this study is to estimate the relative risk for dengue disease based initially on the most common statistic used in disease mapping called Standardized Morbidity Ratio (SMR) and one of the earliest applications of Bayesian methodology called Poisson-gamma model. This paper begins by providing a review of the SMR method, which we then apply to dengue data of Perak, Malaysia. We then fit an extension of the SMR method, which is the Poisson-gamma model. Both results are displayed and compared using graph, tables and maps. Results of the analysis shows that the latter method gives a better relative risk estimates compared with using the SMR. The Poisson-gamma model has been demonstrated can overcome the problem of SMR when there is no observed dengue cases in certain regions. However, covariate adjustment in this model is difficult and there is no possibility for allowing spatial correlation between risks in adjacent areas. The drawbacks of this model have motivated many researchers to propose other alternative methods for estimating the risk.

Keywords: Dengue disease, Disease mapping, Standardized Morbidity Ratio, Poisson-gamma model, Relative risk.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1086703

References:

[1] A. B. Lawson, W. J. Browne, and C. L.Vidal Rodeiro (2003), Disease mapping with Win BUGS and MLwiN. England: John Wiley & Sons.
[2] A.B. Lawson (2006). Statistical methods in spatial epidemiology (2nd ed.). England: John Wiley & Sons.
[3] N. A. Samat and D. F. Percy (2008), Standardized Mortality and Morbidity Ratios and their Application to Dengue Disease Mapping in Malaysia, Proceedings of the Salford Postgraduate Annual Research Conference, pp. 200-210, ISBN: 9781905732715.
[4] J. L Meza (2003). Empirical Bayes Estimation smoothing of relative risks in disease mapping, Journal of Statistical Planning and Inference, 112. pp. 43-62.
[5] N. A. Samat and D. F. Percy (2012). Dengue Disease Mapping in Malaysia based on Stochastic SIR Models in Human Populations, Proceedings of 2012 International Conference on Statistics in Science, Business and Engineering, ISBN: 9781467315807.
[6] N. A. Samat and D. F. Percy (2012), Vector-borne infectious disease mapping with stochastic difference equations: an analysis of dengue disease in Malaysia, Journal of Applied Statistics, vol. 39(9), pp. 2029- 2046. DOI: 10.1080/02664763.2012.700450.