Abstract
Viral diseases are extremely widespread infections caused by viruses, which is a type of microorganism. Some of the common curable viral diseases are common cold, flu, pneumonia mumps, measles, etc. In addition to this, there are also some deadly viral diseases are human immunodeficiency virus (HIV), human pappilomavirus (HPV), SARS, Ebola, etc., which is incurable. The recent coronavirus has also taken its place in this latter list for which the vaccine is yet to be discovered. As early diagnosis is the only option as of now which could control the death rate of this disease, several researchers are in the process of inventing drugs and vaccines for the same. At this stage, it is vital to develop some automated systems that could possibly detect the virus’s presence at an early stage. Numerous scholarly articles concerning proposing computational models encompassing the spread of the coronavirus disease have been studied, analyzed, and juxtaposed with an aim to determine the optimality and accuracy of various models. This work aims to develop a collective study on the models developed so far for the prediction and spread of coronavirus.
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Soam, A., Kaul, K., Ushasukhanya, S. (2022). Literature Survey: Computational Models for Analyzing and Predicting the Spread of the Coronavirus Pandemic. In: Borah, S., Mishra, S.K., Mishra, B.K., Balas, V.E., Polkowski, Z. (eds) Advances in Data Science and Management . Lecture Notes on Data Engineering and Communications Technologies, vol 86. Springer, Singapore. https://doi.org/10.1007/978-981-16-5685-9_34
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DOI: https://doi.org/10.1007/978-981-16-5685-9_34
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