Relative Risk Estimation for Malaria Disease Mapping in Malaysia based on Stochastic SIR-SI Model

Authors

  • Syafiqah Husna Mohd Imam Ma'arof
  • Nor Azah Samat

Keywords:

malaria disease, disease mapping, relative risk estimation, SIR-SI model, stochastic model

Abstract

Disease mapping is a study on the geographical distribution of a disease to represent the epidemiology data spatially. The production of maps is important to identify areas that deserve closer scrutiny or more attention. In this study, a mosquito-borne disease called Malaria is the focus of our application. Malaria disease is caused by parasites of the genus Plasmodium and is transmitted to people through the bites of infected female Anopheles mosquitoes. Precautionary steps need to be considered in order to avoid the malaria virus from spreading around the world, especially in the tropical and subtropical countries, which would subsequently increase the number of Malaria cases. Thus, the purpose of this paper is to discuss a stochastic model employed to estimate the relative risk of malaria disease in Malaysia. The outcomes of the analysis include a Malaria risk map for all 13 states and 3 federal territories in Malaysia, revealing the high and low risk areas of Malaria occurrences.

Downloads

Download data is not yet available.

References

Esteva, L. and Vargas, C. (1998). Analysis of a dengue disease transmission model. Mathematical Biosciences, 150, pp. 131-151.

Isham, V. (2005). Stochastic models for epidemics: Current issues and developments. In Celebrating Statistics, Eds A. C. Davison, Y. Dodge and N. Wermuth. Oxford University Press, pp. 27-54.

Kafadar, K. (1999). Simultaneous smoothing and adjusting mortality rates in U.S. counties: Melanoma in white females and white males. Statistics in Medicine.18:3167-3188.

Nishiura, H. (2006). Mathematical and statistical analysis of the spread of dengue. Dengue Bulletin, 30, pp. 51-67.

Samat, N.A. and Percy, D.F. (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.

Spiegelhalter, D., Thomas, A., Best, N., and Lunn, D. (2003).WinBUGS User Manual Version 1.4, MRC Biostatistics Unit, Cambridge, UK.

Thomas, D.M., Desch, A.D., Gaff, H.D., Scheele, S.K., Jordan, R.K. and Davis, J.R. (2004).Estimating infectius disease risk in the absence of incidence Data. ESRI International Heatlh GIS Conference, Washington, DC.

World Health Organization (WHO). (2013). Fact Sheets: Malaria. Retrieved 13 January 2014, from http://www.who.int/mediacentre/factsheets/fs094/en/index.html

World Health Organization (WHO).(2013). Trends in reported malaria incidence, 2000-2011. World Health Organization Map Production: Public Health Information and Geographic Information Systems (GIS) World Health Organization. Retrieved 24 April 2013, from http://www.who.int/gho/malaria/malaria_003.jpg?ua=1

Downloads

Published

2015-12-07

How to Cite

Mohd Imam Ma’arof, S. H., & Samat, N. A. (2015). Relative Risk Estimation for Malaria Disease Mapping in Malaysia based on Stochastic SIR-SI Model. EDUCATUM Journal of Science, Mathematics and Technology, 2(2), 27–36. Retrieved from https://ejournal.upsi.edu.my/index.php/EJSMT/article/view/69