Volume 7 • Issue 1 • PP: 29–40 • 2026
Artificial Intelligence and Deep Learning in Hantavirus Research: A Comprehensive Review
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Hantavirus remains an important zoonotic threat because of its association with severe human diseases, including hemorrhagic fever with renal syndrome and hantavirus pulmonary syndrome. Its transmission is strongly influenced by rodent reservoirs, environmental conditions, human exposure patterns, and regional ecological variability. Recent advances in artificial intelligence (AI) and deep learning have created new opportunities for improving Hantavirus detection, outbreak prediction, ecological risk mapping, diagnostic support, and public health surveillance. This review examines the role of AI-driven methods in Hantavirus research, with emphasis on how machine learning, deep learning, image-based analysis, epidemiological modeling, and data-driven surveillance can support earlier detection and more informed decision-making. The review also discusses the potential of AI to integrate heterogeneous data sources, including clinical records, environmental variables, remote sensing indicators, genomic information, and epidemiological reports. Despite these advances, several challenges remain, including limited datasets, geographic bias, model generalization, lack of clinical validation, data imbalance, interpretability concerns, and the need for real-time deployment. Overall, AI and deep learning offer promising tools for strengthening Hantavirus surveillance and response, but their practical value depends on transparent models, high-quality data, interdisciplinary validation, and integration into public health systems.
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