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Advanced deep learning algorithms for infectious disease modeling using clinical data- A Case Study on CoVID-19


Ajay Kumar, Smita Nivrutti Kolnure, Kumar Abhishek, Fadi-Al-Turjman , Pranav Nerurkar, Muhammad Rukunuddin Ghalib and Achyut Shankar*  


Background: Infectious disease happens when an individual is defiled by a micro-organism/virus from another person or an animal. It is troublesome that causes hurt at both individual and huge scope scales.

Case Presentation: The ongoing episode of COVID-19 ailment brought about by the new coronavirus first distinguished in Wuhan China, and its quick spread far and wide, revived the consideration of the world towards the impacts of such plagues on individual’s regular daily existence. We attempt to exploit this effectiveness of Advanced deep learning algorithms to predict the Growth of Infectious disease based on time series data and classification based on (symptoms) text data and X-ray image data.

Conclusion: Goal is identifying the nature of the phenomenon represented by the sequence of observations and forecasting.


Big data analysis, Deep learning, Time series forecasting Infectious disease modeling, COVID-19


Dept. of Computer Science & Engineering, NIT Patna, Bihar, Dept. of Computer Science & Engineering, NIT Patna, Bihar, Dept. of Computer Science & Engineering, NIT Patna, Bihar, Research Centre for AI and IoT, Department of Artificial Intelligence Engineering, Near East University, Nicosia, Mersin 10, Dept. of CE & IT, VJTI Dept. of Data Science, MPSTME, NMIMS University, Mumbai, School of Computer Science and Engineering, Vellore Institue of Technology (VIT), Vellore, Department of CSE, Amity School of Engineering and Technology, Amity University, Uttar Pradesh

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