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COVID 19 Prediction Model Using Prophet Forecasting with Solution for Controlling Cases and Economy
ADVANCES IN DATA SCIENCE AND INTELLIGENT DATA COMMUNICATION TECHNOLOGIES FOR COVID-19: Innovative Solutions Against COVID-19 ; 378:139-151, 2022.
Article in English | Web of Science | ID: covidwho-2030699
ABSTRACT
Coronavirus disease outbreak (COVID-19) has threatened the entire world and has made lives difficult. It has drastically affected the way of living, working, and managing routines for the human beings, by living indoors. In a country like India, with a population of about 1.35 billion, the virus is spreading so fast that the control has become unmanageable. This paper presents COVID 19 data analysis and the prediction model that helps plan and organize things as precautionary measures. In this chapter, analysis is performed on COVID 19 data, and the prediction model is proposed for October. The analysis and prediction is performed using two methods, viz. random forest and time series. The chapter also compared the analyzed results. The idea behind analyzing the available dataset and the comparison of two prediction models is to supply some solutions to control the spreading of COVID 19. In this chapter, analysis is presented state-wise and country wise for the active number of cases and the date cases. Recovery rates are also analyzed. Gender-specific detailed analysis is also presented in this chapter with different age groups in India.
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Collection: Databases of international organizations Database: Web of Science Type of study: Prognostic study Language: English Journal: ADVANCES IN DATA SCIENCE AND INTELLIGENT DATA COMMUNICATION TECHNOLOGIES FOR COVID-19: Innovative Solutions Against COVID-19 Year: 2022 Document Type: Article

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Collection: Databases of international organizations Database: Web of Science Type of study: Prognostic study Language: English Journal: ADVANCES IN DATA SCIENCE AND INTELLIGENT DATA COMMUNICATION TECHNOLOGIES FOR COVID-19: Innovative Solutions Against COVID-19 Year: 2022 Document Type: Article