Abstract
The novel virus, often called COVID-19, is an infection that spreads from one to another in multiple chains. The novel virus has caused a universal pandemic, and some investigations utilize diverse statistical methods to deliver models in order to analyze the current state of the pandemic and the losses incurred for other reasons depending upon place to place. The obtained statistical models depend on diverse aspects, and studies are purely based on possible preferences. In this research, a model is suggested for predicting the diverse blowout of COVID-19. Machine learning classifiers like linear regression, multilayer perceptron, and vector autoregression can be chosen to predict the likely patterns of COVID-19 effects in various parts of the world based on their climate, environment, culture, behavior, and socioeconomic factors.
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Siddiqui, S.T., Singha, A.K., Ahmad, M.O., Khamruddin, M., Ahmad, R. (2022). IoT Devices for Detecting and Machine Learning for Predicting COVID-19 Outbreak. In: Pundir, A.K.S., Yadav, N., Sharma, H., Das, S. (eds) Recent Trends in Communication and Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-1324-2_12
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DOI: https://doi.org/10.1007/978-981-19-1324-2_12
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