A Framework for AI-Enabled Nuclear Emergency Response: A Case Study of Malaysia's MySejahtera and its Applicability to National Digital Health Strategies

Muhammad Sukri Bin Ramli

No v2c4q_v1, OSF Preprints from Center for Open Science

Abstract: Nuclear events present a critical threat to public well-being, necessitating swift and effective mitigation strategies. Leveraging the success of Malaysia's MySejahtera app, a digital health platform that showcased the power of AI in managing the COVID-19 pandemic, this paper proposes harnessing its AI capabilities for nuclear Emergency Preparedness and Response (EPR). By adapting MySejahtera's proven AI-driven tools for data analysis, prediction, and resource optimization, Malaysia can significantly enhance its nuclear EPR capabilities within the existing national digital health framework. This adaptation promises to expedite response times by rapidly identifying affected populations, optimize resource allocation through predictive modeling of contamination zones, and improve decision-making through real-time radiation tracking and risk assessment. This approach empowers authorities with the tools to swiftly assess, respond to, and manage nuclear events, ultimately bolstering public safety and resilience. Furthermore, the integration of advanced AI/ML models to forecast radiation spread and long-term health impacts, coupled with the exploration of federated learning approaches for collaborative data analysis while upholding privacy and security, presents a significant opportunity to fortify national nuclear EPR capabilities. This forward-looking strategy not only addresses immediate emergency response needs but also lays the groundwork for a more robust and resilient infrastructure to safeguard public health and safety in the face of potential nuclear events.

Date: 2024-12-30
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:v2c4q_v1

DOI: 10.31219/osf.io/v2c4q_v1

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