The role of artificial intelligence in predicting disease outbreaks: A multidisciplinary approach
Keywords:
Artificial intelligence, multidisciplinary approach, outbreak, diseaseAbstract
This transdisciplinary research examines the use of Artificial Intelligence (AI) in forecasting disease epidemics. The rising frequency and complexity of epidemics need proactive solutions, and AI provides robust capabilities for evaluating extensive information, recognizing trends, and producing predicting insights. The study analyzes many AI models and technologies, including statistical models and machine learning approaches, assessing their strengths and limitations via case studies and benchmarking. A primary emphasis is the vital function of interdisciplinary cooperation, amalgamating the proficiency of nurses (offering real-time clinical data), medical record professionals (guaranteeing data quality and accessibility), and biochemists (giving molecular-level insights). The paper examines difficulties including ethical concerns, data protection, and the need for effective governance systems. Additionally, it examines prospective future avenues, such as deep learning, ensemble learning, the amalgamation of data from wearable devices and social media, and the implementation of the One Health paradigm. Improvements in genetic monitoring, expedited diagnostics, and citizen science activities are emphasized as vital components in augmenting epidemic prediction and response. The work underscores the revolutionary potential of AI, enabled by interdisciplinary cooperation, to enhance global health security and disease outbreak control.
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