This project presents an intelligent system, named COVID-19 System for Identifying Spacio-symptom relationships (CovSIS) employing a decentralized collective intelligence platform, e.g., online news articles. This system takes online news articles as input and extracts relevant data including countries, symptoms, and others. Afterwards, these data are utilized in calculating weights using a new analytical model and a new algorithm for identifying the impacts of different symptoms locally and globally. Then, a knowledge graph is generated by integrating spacio-symptom related data that facilitates in responding relevant queries and thus, presents necessary information to various stakeholders. Again, global and local ranks are also identified and their similarity indexes are also calculated for realizing more insight on the spacio-symptom relationships. These relationships are also visualize using a chord diagram that connects various countries with respective symptoms using chords. This system can be considered as a pioneer effort in this field and can be utilized as a supporting system for promptly identifying and presenting spacio-symptom relationships of COVID-19 alongside conventional systems.
Innovators name: Dr. Md Saiful Azad, Md Saef Ullah Miah, Zahiduddin Ahmed, Talha Bin Sarwar, Dr. Mufti Mahmud, Prof. Kamal Z. Zamli, Prof. M. Shamim Kaiser