Survey on Forecasting the vulnerability of Covid 19 in Tamil Nadu. Issue 1 (February 2021)

• Record Type: Journal Article
• Authors: Ananthi, P.; Jabeen Begum, S.; Latha Jothi, V.; Kayalvili, S.; Gokulraj, S.
• Is Part Of: Journal of physics. Volume 1767:Issue 1(2021)
• Publication Date: 2021-02
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1767/1/012006
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Predictive and analytic models for forecasting the vulnerability and recovery rate of patients who are affected by COVID 19 are made in this project for good analysis and better decision-making. In this project, linear regression (LR) a Machine Learning model is used to forecast the number of patients will get the infection in near future. By simulating SIRD model, the infection spread and recovery rate of the disease in a geographic region can be predicted. The vulnerability of the disease is checked by observing the transmission of disease over a period. In addition to this many info graphic models and graphs are created for easy understanding of data to get more insights about the disease. However, these prediction models enable us to make quick response of pandemic and to bring a conclusion to the disease. INDEX TERMS: Covid 19, data science, machine learning, prediction, analysis, pandemic, recovery and infection.