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The recent COVID-19 outbreak highlighted the requirement for a more sophisticated healthcare system and real-time data analytics in the pandemic mitigation process. Moreover, real-time data plays a crucial role in the detection and alerting process. Combining smart healthcare systems with accurate real-time information about medical service availability, vaccination, and how the pandemic is spreading can directly affect the quality of life and economy. The existing architecture models are become inadequate in handling the pandemic mitigation process using real-time data. The present models are server-centric and controlled by a single party, where the management of confidentiality, integrity, and availability (CIA) of data is doubtful. Therefore, a decentralised user-centric model is necessary, where the CIA of user data is assured. In this paper, we have suggested a decentralized blockchain-based pandemic detection and assistance system (iBlock). The iBlock uses robust technologies like hybrid computing and IPFS to support system functionality. A pseudo-anonymous personal identity is introduced using H-PCS and cryptography for anonymous data sharing. The distributed data management module guarantees data CIA, security, and privacy using cryptography mechanisms. Furthermore, it delivers useful intelligent information in the form of suggestions and alerts to assist the users. Finally, the iBlock reduces stress on healthcare infrastructure and workers by providing accurate predictions and early warnings using AI/ML. [ABSTRACT FROM AUTHOR]

Extreme weather events like tropical cyclone result in colossal catastrophe during landfall causing widespread inland flooding due to storm surge and also the post-landfall event result in extensive damage to infrastructural facilities and property hinterland. The state of Odisha located in east coast of India experienced a Very Severe Cyclonic Storm (VSCS) named Phailin during the post-monsoon season of October 2013. Timely warnings and alertness on storm surge coordinated with a massive evacuation effort by National Disaster Management Authorities (NDMA) were quite effective in minimizing the loss of human life. However, there was a trial of destruction due to extremely high winds and rainfall that followed during post-landfall causing extensive damage to property and major infrastructure facilities in the Odisha State. This study critically investigated the Phailin post-landfall phase focusing on the impact of high winds and rainfall on the power distribution network using the Weather Research and Forecasting (WRF) model. The study evaluated the spatial and temporal variability of wind speed and rainfall distribution from the WRF model configured for three different spatial domains and selecting the best available microphysics and land surface parameterization schemes. The proposed outer, intermediate, and inner domains had spatial resolutions of 27, 9, and 3 km respectively and that provided the best estimate for onshore wind speed, track forecast, and rainfall distribution highly relevant for the management of power distribution and transmission network. In context to weather model application for the Indian region, this effort is novel and probably for the first time that linked a suitable customized weather model output to evaluate its impact on observed tripping in transmission network of electric power grids. The dynamic model outputs from WRF were compared with data from synchrophasors used in electrical technology that monitored the transient and dynamic behavior of power systems in real-time operations. A close examination of the results signifies that the atmospheric model performed exceptionally well in capturing the tripping time of power lines, and the overall knowledge obtained from this study has a broader scope to develop a framework for efficient planning operations of the power network, resource allocation, and emergency preparedness. [ABSTRACT FROM AUTHOR]