Preprint Article Version 1 This version is not peer-reviewed

Prediction and Spread Visualization of Covid-19 Pandemic Using Machine Learning

Version 1 : Received: 7 May 2020 / Approved: 9 May 2020 / Online: 9 May 2020 (04:30:32 CEST)

How to cite: N Roy, A.; Jose, J.; Sunil, A.; Gautam, N.; Nathalia, D.; Suresh, A. Prediction and Spread Visualization of Covid-19 Pandemic Using Machine Learning. Preprints 2020, 2020050147 (doi: 10.20944/preprints202005.0147.v1). N Roy, A.; Jose, J.; Sunil, A.; Gautam, N.; Nathalia, D.; Suresh, A. Prediction and Spread Visualization of Covid-19 Pandemic Using Machine Learning. Preprints 2020, 2020050147 (doi: 10.20944/preprints202005.0147.v1).

Abstract

The sudden pervasive of severe acute respiratory syndrome Covid-19 has been leading the universe into a prominent crisis. It has influenced each zone, for example, industrial area, horticultural zone, Public transportation, economic zone, and so on. So as to see how Covid-19 affected the globe, we conducted an investigation characterizing the effects of the pandemic over the world using Machine Learning (ML) method. Prediction is a typical data science exercise that helps the administration with function planning, objective setting, and anomaly detection. We propose an additive regression model with interpretable parameters that can be naturally balanced by experts with domain intuition about the time series. We focus on global data beginning from 22nd January 2020, till 26th April 2020 and performed dynamic map visualization of Covid-19 expansion globally by date wise and predicting the spread of virus on all countries and continents. The major advantages of this work include accurate analysis of country-wise as well as province/state-wise confirmed cases, recovered cases, deaths, prediction of pandemic viral attack and how far it is expanding globally.

Subject Areas

COVID-19; Machine Learning; Pandemic; Additive regression model; Dynamic Map

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