3066-280XISSN (Online)
Volume 1 , Issue 1 , PP: 55-65, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmed Mohamed Zaki 1 * , Khaled Sh. Gaber 2 , Faris H. Rizk 3 , Mahmoud Elshabrawy Mohamed 4
Doi: https://doi.org/10.54216/MOR.010105
The current review summarizes the latest trends in malaria literature, emphasizing transmission ecology, new diagnostics and treatment. It stresses the additional focus on the transmission, according to the spatiotemporal models and predictive analytics, which help identify periods and the locations with the most significant risk, noting that these processes should consider the environmental factors. The change in the diagnostic approach, especially the introduction of artificial intelligence techniques such as deep learning, has improved the rate and precision at which malaria parasites are diagnosed in resource-limited countries where time is of the essence. Furthermore, there have been significant advances in drug discovery due to machine learning applications that have made it quicker to find new antimalarial drugs in the face of drug resistance. Despite these developments, there are still problems such as drug resistance, socio-economic disparities, and the environment that are being altered and still require an integrated and transdisciplinary approach. Combining these determinants is indispensable for eliminating these challenges and further promoting global efforts to control malaria.
Malaria , Transmission ecology , Machine learning , Drug resistance and diagnostics
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