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Vector-borne diseases pose a significant threat to human health, particularly in regions vulnerable to climate change. Among these diseases, malaria, caused by the parasite Plasmodium falciparum and transmitted through the Anopheles mosquito, remains a major global health concern, particularly in sub-Saharan Africa. This study explores the use of machine learning techniques to identify and predict the impact of climate change on the transmission dynamics of P. falciparum malaria in Africa.The research utilizes a combination of climate data, epidemiological records, and machine learning algorithms to analyze historical patterns and project future trends in malaria transmission. Key climate variables such as temperature, precipitation, humidity, and vegetation cover are integrated into predictive models to assess their influence on the abundance and distribution of mosquito vectors and the parasite's lifecycle. Through the application of machine learning models such as Maximum Entropy, this study aims to uncover complex relationships between climatic factors and malaria transmission dynamics. By training these models on historical data, they can accurately predict future scenarios under various climate change scenarios. The findings of this research will provide valuable insights into the potential impact of climate change on the spatial and temporal distribution of P. falciparum malaria in Africa. Such insights are crucial for designing targeted interventions and adaptation strategies to mitigate the anticipated rise in malaria cases and associated morbidity and mortality in the region. Moreover, the methodology developed in this study can serve as a framework for assessing and addressing the impact of climate change on other vector-borne diseases globally. [ABSTRACT FROM AUTHOR]

Recent advancements in spatial modelling and mapping methods have opened up new horizons for monitoring the migration of bird species, which have been altered due to the climate change. The rise of citizen science has also aided the spatiotemporal data collection with associated attributes. The biodiversity data from citizen observatories can be employed in machine learning algorithms for predicting suitable environmental conditions for species' survival and their future migration behaviours. In this study, different environmental variables effective in birds' migrations were analysed, and their habitat suitability was assessed for future understanding of their responses in different climate change scenarios. The Jacobin cuckoo (Clamator jacobinus) was selected as the subject species, since their arrival to India has been traditionally considered as a sign for the start of the Indian monsoon season. For suitability predictions in current and future scenarios, maximum entropy (Maxent) modelling was carried out with environmental variables and species occurrences observed in India and Africa. For modelling, the correlation test was performed on the environmental variables (bioclimatic, precipitation, minimum temperature, maximum temperature, precipitation, wind and elevation). The results showed that precipitation-related variables played a significant role in suitability, and through reclassified habitat suitability maps, it was observed that the suitable areas of India and Africa might decrease in future climatic scenarios (SSPs 2.6, 4.5, 7.0 and 8.5) of 2030 and 2050. In addition, the suitability and unsuitability areas were calculated (in km2) to observe the subtle changes in the ecosystem. Such climate change studies can support biodiversity research and improve the agricultural economy. [ABSTRACT FROM AUTHOR]