Machine Learning Analyses of Climate Variability Trends and Malaria Transmission Dynamics in Yobe State, Nigeria

The fluctuations of the climatic variables have considerable impacts on the livelihood security of Yobe State, Nigeria, particularly as it relates to malaria transmission and public health in general. This paper investigated how, between 2014 and 2023, temperature and rainfall patterns changes correlate with the prevalence of malaria in the micro-climatic zones of the state, which are SaSZ, TZ, and SuSZ. By employing machine learning algorithms on the climatic data obtained from NiMet, the study highlighted patterns in climate variability and correlates same with malaria risk. It revealed seasonality fluctuations in temperature (20°C to 47.5°C) and rainfall patterns that coincided with heightened malaria transmission. Temperature, spiking higher than 30°C, aligned with increasing malaria cases, whereas 40°C of the climatic element appears to reduce mosquito survival. On the other hand, the rainfall patterns, especially with the oscillations between 0mm and 120mm, provided breeding grounds for mosquitoes, amplifying malaria transmission risks. Thus, the study concluded that while the transmission could be a function of changing climate, the control methods are inadequate in the face of increasing climate unpredictability. This implied the need for an integrated approach combining climate monitoring, epidemiological surveillance, and adaptive public health policies is necessary to mitigate the effects of climate-driven malaria risks.