Climatological and socioeconomic factors that predispose to the risk of malaria in Sudan

Malaria remains as one of the major health problems in Sudan. The purpose of this study was to investigate the relationship among malaria, climate variables and socio-economic factors in Sudan. The health production modification model was applied to examine the relations between climate variability (average temperature and average rainfall) and socio-economic factors, with the malaria rate per state in Sudan. The results of the model found that there are significant relations between the malaria rate, rainfall and water bodies. Therefore, an indepth study using monthly data and adding more control variables is needed. We evaluated the potential clustering of incidence of malaria in Sudan using two procedures: choropleth mapping to summarize the malaria spatial data, based on state boundaries, and geo-statistical kriging. The results indicate that the highest rate of malaria was recorded in the middle east of Sudan and south east, while low rates were observed in the western and northern parts. To predicted and forecasting the spread of malaria in Sudan we adopted The Auto-Regressive Integrated Moving Average (ARIMA) model. The ARIMA model used malaria cases from 2004 to 2009 as a training set, and data from 2010 as a testing set, and created the best model fitted to forecast the malaria cases in Sudan for years 2011 and 2012. The ARIMAX model was carried out to examine the relationship between malaria cases and climate factors with diagnostics of previous malaria cases using the least Bayesian Information Criteria (BIC) values. The results indicated that there were four different models, the ARIMA model of the average for the overall states is (1,0,1)(0,1,1)12. The ARIMAX model showed that there is a significant variation between the states in Sudan. We created the prediction malaria distribution model using the Fuzzy Logic Suitability (FLS) model based on the life cycle characteristics of the Anopheles mosquito. This model used the climate factors – maximum and minimum temperature, rainfall and relative humidity – from years 2004 to 2010. The results of the prediction model found that the climate factors were suitable formalaria transmission from (May to October) in Sudan. While the estimation malaria model maps results showed that the malaria rate was high from (June to November). The comparison results between prediction and estimation model discovered that the largest similarities were around 55% in the prediction ofOctober and estimation of November.

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