A climate distribution model of malaria transmission in Sudan

  • Mohammed I. Musa Department of Biology, Faculty of Science, Putra University, Selangor, Malaysia; Economic and Social Research Bureau, Ministry of Science and Technology, Khartoum, Sudan.
  • Shamarina Shohaimi | shamarina@putra.upm.edu.my Department of Biology, Faculty of Science, Putra University, Selangor, Malaysia.
  • Nor R. Hashim Department of Environmental Sciences, Faculty of Environmental Studies, Putra University, Selangor, Malaysia.
  • Isthrinayagy Krishnarajah Department of Mathematics, Faculty of Science, Putra University, Serdang, Selangor; Laboratory of Computational Statistics and Operations Research, Institute for Mathematical Research, Putra University, Serdang, Selangor, Malaysia.


Malaria remains a major health problem in Sudan. With a population exceeding 39 million, there are around 7.5 million cases and 35,000 deaths every year. The predicted distribution of malaria derived from climate factors such as maximum and minimum temperatures, rainfall and relative humidity was compared with the actual number of malaria cases in Sudan for the period 2004 to 2010. The predictive calculations were done by fuzzy logic suitability (FLS) applied to the numerical distribution of malaria transmission based on the life cycle characteristics of the Anopheles mosquito accounting for the impact of climate factors on malaria transmission. This information is visualized as a series of maps (presented in video format) using a geographical information systems (GIS) approach. The climate factors were found to be suitable for malaria transmission in the period of May to October, whereas the actual case rates of malaria were high from June to November indicating a positive correlation. While comparisons between the prediction model for June and the case rate model for July did not show a high degree of association (18%), the results later in the year were better, reaching the highest level (55%) for October prediction and November case rate.



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Original Articles
fuzzy logic, climate suitability, malaria rate, geographical information system, mapping, Sudan.
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How to Cite
Musa, M. I., Shohaimi, S., Hashim, N. R., & Krishnarajah, I. (2012). A climate distribution model of malaria transmission in Sudan. Geospatial Health, 7(1), 27-36. https://doi.org/10.4081/gh.2012.102