JAMC
Analysis of COVID-19 Prediction Models: Design & Analysis of New Machine Learning Approach
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Abstract
Recently the Coronavirus disease 2019 is the most worldwide health concern that is caused by the SARS-Cov2 virus. This results in a greater mortality rate with various clinical manifestations. The prediction of case trends and identification are becoming crucial in effective virus mitigation. There are different prediction methods such as statistical and epidemiological models are designed worldwide to determine and analyze the count of infected individuals with mortality rate. There are certain drawbacks in these models—in terms of lack of essential information and high uncertainty level results in lower prediction accuracy. To resolve the issues in these existing models, it is necessary to construct a new model using machine learning to predict the trends in the cases with better accuracy. This research focuses on the design of a new model using different soft computing methods. The performance of the model is evaluated to predict and analyze the infection and rate of mortality. The proposed model is expected to find the best results for predicting COVID case trends and mortality rates over the recent well-known methods.
References
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How to cite this paper
Analysis of COVID-19 Prediction Models: Design & Analysis of New Machine Learning Approach
How to cite this paper: Raja Marappan, S. Bhaskaran, N. Aakaash, S. Mathu Mitha. (2022) Analysis of COVID-19 Prediction Models: Design & Analysis of New Machine Learning Approach. Journal of Applied Mathematics and Computation, 6(1), 121-126.
DOI: http://dx.doi.org/10.26855/jamc.2022.03.013
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