An Analysis of Methods for Forecasting Epidemic Disease Outbreaks using Information from Social Media
Disha Sushant Wankhede1, Rohan Rajendra Sadawarte2, Mahek Ibrahim Mulla3, Shreya Rahul Jadhav4
1Mrs. Disha Sushant Wankhede, Assistant Professor, Department of Computer Science, Vishwakarma Institution of Information Technology, Pune (Maharashtra), India.
2Rohan Rajendra Sadawarte, Students, Department of Computer Science, Vishwakarma Institution of Information Technology, Pune (Maharashtra), India
3Mahek Ibrahim Mulla, Students, Department of Computer Science, Vishwakarma Institution of Information Technology, Pune (Maharashtra), India
4Shreya Rahul Jadhav, Students, Department of Computer Science, Vishwakarma Institution of Information Technology, Pune (Maharashtra), India
Manuscript received on 27 June 2022 | Revised Manuscript received on 02 July 2022 | Manuscript Accepted on 15 July 2022 | Manuscript published on 30 July 2022 | PP: 128-137 | Volume-11 Issue-2, July 2022 | Retrieval Number: 100.1/ijrte.B71600711222 | DOI: 10.35940/ijrte.B7160.0711222
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Predicting the rise or fall of an epidemic or pandemic is an essential part of establishing control over it. Post-World War 1, when there was an outbreak of the “Black Plague” there weren’t any means to analyze and predict. Although today we are equipped with tools like Machine Learning and Artificial Intelligence which have certainly enabled us to prevent unnecessary loss of life. It helps prepare the health officials to build the infrastructure and interpret the intensity of preparedness regulation of resources. The aim of this survey is to analyze and shed some light on the various algorithms and methods such as – regression models, neural networks, ARIMA, etc. Before building any model, gathering and processing the data is also essential. Hence our paper also focuses on which social media platforms proved beneficial in comparison to all we found and then made fit to be incorporated into the models. While researching for this paper, we observed that every disease has a different transmission type that leads to an outbreak and is a key factor in constructing a model. The literature evaluation in this work is centered on various prediction algorithms and their strategies for extracting online data from social media sites like Facebook and Twitter, all of which have drawn a lot of interest in early disease diagnosis for public health.
Keywords: Machine Learning, Artificial Intelligence, social media, Pandemic, Epidemic, Outbreak, Covid-19, Influenza, Regression Models, Neural Networks.
Scope of the Article: Machine Learning