COVID-19 Spread Prediction Using Prophet and Data Fusion Algorithm

COVID-19 Spread Prediction Using Prophet and Data Fusion Algorithm

Sangeetha V., Evangeline D., Sinthuja M.
Copyright: © 2022 |Pages: 16
DOI: 10.4018/978-1-7998-8161-2.ch001
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Today, technology plays a vital role in the healthcare industry. In the traditional way, physicians' minds were predicting the unknown disease based on their expertise and experience. Use of new technology like predictive analytics is transforming the healthcare industry. Predictive analytics in healthcare uses historical data (demographic information, person's past medical history and behaviors) to make predictions about the future. In this chapter, a predictive model is proposed to predict COVID-19 using prophet algorithm. A novel approach based on longitudinal data fusion approach will maintain temporal data from time to time. Sparse regularization regression uses data source and feature level to predict the spread of virus. The proposed model designed using longitudinal data fusion offers better clinical insights. Predictions will be very beneficial to government and healthcare groups to provoke suitable measures in controlling coronavirus. It is also beneficial to pharmaceutical companies to fabricate pills at a quicker rate.
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Epidemiological models can be used to predict the spread of any epidemic disease. Covid -19 infection is now an epidemic which can turn into an endemic. Manoj Kumar et al., have developed such a model to predict the various stages of disease transmission (Kumar, 2020). In popular, incubation period of the disorder is five days and an inflamed character may want to unfold the disease in nine days. While the time span between getting inflamed and reaching the important level is 9 days, the time span between crucial stage and restoration is 9.5 days. Also, the time span among critical degree and loss oflifeis14. 5days.One such epidemiological model is the compartmental model where in the population

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