Modeling the Influence of Climate Factors on Malaria Transmission Dynamics in North Kordofan State, Sudan

Abstract

Background: Despite great efforts by the government to control malaria in Sudan, the disease is the most significant human disease and was widespread in North Kordofan State. Morbidity and mortality of the disease are increasing in the State. Usually, the disease reached its peak after rainy season. This study aims to estimate the role of climate factors on malaria transmission dynamic by modeling the relationship between malaria cases and climatic variables, such as rainfall, relative humidity, and temperature, in Kordofan State. Methods: We used Pearson correlation coefficient and an ordinary least square method to assess this relationship. Results: The results show that there are statistically significant associations between malaria cases and rainfall, relative humidity, and minimum temperature (P-value < 0.001). The regression analysis results suggest that the appropriate model for predicting malaria incidence includes malaria cases lagged by one month, maximum temperature, and minimum temperature. This model explained 72% of the variance in monthly malaria incidence. Conclusion: The results of this study suggest that climatic factors have potential use for malaria prediction in the State.