A Study on COVID-19 Infection Forecasting Techniques

Yogini jawale; Akshay Thakare; Aditya Shinde; Govind Waghmare; Archna Shinde

A Study on COVID-19 Infection Forecasting Techniques

Authors: Yogini jawale, Akshay Thakare, Aditya Shinde, Govind Waghmare, Archna Shinde

Unique Paper ID: 151389
Volume: 7
Issue: 12
PageNo: 558-561

Keywords: Machine Learning Prediction Fuzzy Classification.

Abstract:
The rapid increase in Covid-19 infections has been highly problematic for a lot of individuals as it led to various governments imposing lockdowns and curfews all over the world. This situation has led to massive losses for the majority of the companies and a lot of them have also been shut down due to these losses. The epidemic has also been filled with suffering and pain as a large number of people got infected which led to overcrowding of hospitals and other medical institutes. These problems have been caused due to the inability in predicting the future course of the virus and preventive measures. Therefore, a collection of researches based on the prediction of the virus infection spread have been detailed in this research. The related works on the forecasting of the infection spread have provided valuable insight into the process of forecasting using machine learning approaches. The methodology for the covid-19 forecasting will be discussed in much more detail in the upcoming editions of this research.

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Cite This Article

ISSN: 2349-6002
Volume: 7
Issue: 12
PageNo: 558-561

A Study on COVID-19 Infection Forecasting Techniques

Available:https://ijirt.org/Article?manuscript=151389

29 May, 2021

Efficient Covid 19 Forecasting for worldwide countries using ANN

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8.01 (Year 2024)

UGC Approved
Journal no 47859

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