Preprint Article Version 2 This version is not peer-reviewed

Data Analytics: COVID-19 Prediction Using Multimodal Data

Version 1 : Received: 15 April 2020 / Approved: 16 April 2020 / Online: 16 April 2020 (05:54:13 CEST)
Version 2 : Received: 13 May 2020 / Approved: 14 May 2020 / Online: 14 May 2020 (09:03:52 CEST)

How to cite: Mahalle, P.N.; Sable, N.P.; Mahalle, N.P.; Shinde, G.R. Data Analytics: COVID-19 Prediction Using Multimodal Data. Preprints 2020, 2020040257 (doi: 10.20944/preprints202004.0257.v2). Mahalle, P.N.; Sable, N.P.; Mahalle, N.P.; Shinde, G.R. Data Analytics: COVID-19 Prediction Using Multimodal Data. Preprints 2020, 2020040257 (doi: 10.20944/preprints202004.0257.v2).

Abstract

Globally, there is massive uptake and explosion of data and challenge is to address issues like scale, pace, velocity, variety, volume and complexity of this big data. Considering the recent epidemic in China, modeling of COVID-19 epidemic for cumulative number of infected cases using data available in early phase was big challenge. Being COVID-19 pandemic during very short time span, it is very important to analyze the trend of these spread and infected cases. This chapter presents medical perspective of COVID-19 towards epidemiological triad and the study of state-of-the-art. The main aim this chapter is to present different predictive analytics techniques available for trend analysis, different models and algorithms and their comparison. Finally, this chapter concludes with the prediction of COVID-19 using Prophet algorithm indicating more faster spread in short term. These predictions will be useful to government and healthcare communities to initiate appropriate measures to control this outbreak in time.

Subject Areas

COVID-19; Predictive Analytics; Machine Learning; Prediction; Pandemic

Comments (1)

Comment 1
Received: 14 May 2020
Commenter: Parikshit Mahalle
Commenter's Conflict of Interests: Author
Comment: Revised according to Reviewer’s Comments
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