Blockchain and digital twin integration for predictive and secure pandemic alerting

Due to the COVID-19 pandemic, there is a necessity to implement the latest technologies that will help provide safe, real-time, and predictive medical services. The current pandemic alert apps, including BlueDot, Aarogya Setu, and the JHU Dashboard, are based on centralized reporting and manual updates, which reduce their scalability, security, and predictions. In an effort to address these gaps, this paper proposes a novel concept that integrates blockchain, artificial intelligence (AI), and digital twins to decentralize pandemic alerting and monitoring. Within the proposed COVID-DT model, blockchain enables the tamper-free, decentralized sharing of data across healthcare stakeholders. BiLSTM networks improve predictive accuracy over time, and DTs generate dynamic digital imprints for use in ongoing monitoring and simulation of outbreaks. An implementation was designed based on Raspberry Pi edge devices, IoT sensors, and Hyperledger Fabric, and simulated in MATLAB. Findings show predictive accuracy of 97.7, lower latency time of 4.3 min using 12 worker nodes, consistent message delivery (approximately 80%), and a cost of communication of less than 700 bytes with an error of 10 percent. These results demonstrate the scalability, low power, and cybersecurity of this model. The COVID-DT framework provides a safe, efficient, and interoperable backbone for the management of future pandemics, surpassing current centralized systems.