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Keywords:

  • statistics;
  • disease;
  • spatial regression model

Malaria is a leading cause of infectious disease and death worldwide. As a common example of a vector-borne disease, malaria could be greatly affected by the influence of climate change. Climate impacts the transmission of malaria in several ways, affecting all stages of the disease's development. Using various weather-related factors that influence climate change, this study utilizes statistical analysis to determine the effect of climate change on reported malaria rates in an African region with endemic malaria. It examines the relationship between malaria prevalence and climate in western Africa using spatial regression modeling and tests for correlation. Our analysis suggests that minimal correlation exists between reported malaria rates and climate in western Africa. This analysis further contradicts the prevailing theory that climate and malaria prevalence are closely linked and negates the idea that climate change will increase malaria transmission in this region.