E3S Web Conf.
Volume 309, 20213rd International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2021)
|Number of page(s)||8|
|Published online||07 October 2021|
Novel Corona Virus Prediction and Transmission Analysis using Machine Learning Models
1 Computer Science and Engineering, GRIET, Hyderabad, Telangana, India.
2 Computer Science and Engineering, VJIT, Hyderabad, Telangana, India.
* Corresponding author: email@example.com
Today we all are suffering from Covid-19, a novel virus and it is the most harmful disease across the world which mainly comes under the domain of health care research. Healthcare system gives importance to health states of the population or individual. Healthcare plays a vital role in promoting physical and mental health and well- being of people around the world. Efficient health care system leads to country’s economy, industrialization and development. Corona virus is dangerous animal and human pathogens and it is threatening people by spreading all over the world. Corona virus patients mostly suffer from lung infection studies have shown it clinically. We proposed detailed analysis on how to predict the expected death, recovered and confirmed cases based on the available data across the world using various machine learning models. Especially we constructed linear regression model (LRM), support vector machine model (SVMM) and polynomial regression models (PRM) and predicted future expected cases over a period of next 15 days. The error between the predicted model and official data curve is quite small in the process of transmission in data modeling. Compare to other models Polynomial regression model performs best prediction of corona positive cases. Forward prediction and backward inference of the epidemic helps to take decisions for necessary actions during Covid-19 propagation.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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