An approach to forecast impact of Covid-19 using supervised machine learning model

Document Type

Article

Source of Publication

Software - Practice and Experience

Publication Date

1-1-2021

Abstract

The Covid-19 pandemic has emerged as one of the most disquieting worldwide public health emergencies of the 21st century and has thrown into sharp relief, among other factors, the dire need for robust forecasting techniques for disease detection, alleviation as well as prevention. Forecasting has been one of the most powerful statistical methods employed the world over in various disciplines for detecting and analyzing trends and predicting future outcomes based on which timely and mitigating actions can be undertaken. To that end, several statistical methods and machine learning techniques have been harnessed depending upon the analysis desired and the availability of data. Historically speaking, most predictions thus arrived at have been short term and country-specific in nature. In this work, multimodel machine learning technique is called EAMA for forecasting Covid-19 related parameters in the long-term both within India and on a global scale have been proposed. This proposed EAMA hybrid model is well-suited to predictions based on past and present data. For this study, two datasets from the Ministry of Health & Family Welfare of India and Worldometers, respectively, have been exploited. Using these two datasets, long-term data predictions for both India and the world have been outlined, and observed that predicted data being very similar to real-time values. The experiment also conducted for statewise predictions of India and the countrywise predictions across the world and it has been included in the Appendix.

ISSN

0038-0644

Publisher

Wiley

Disciplines

Computer Sciences | Medicine and Health Sciences

Keywords

Covid-19, ensemble learning, healthcare, machine learning, prediction

Scopus ID

85103394313

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Bronze: This publication is openly available on the publisher’s website but without an open license

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