Impact analysis of recovery cases due to COVID19 using LSTM deep learning model

TL;DR

This paper employs LSTM deep learning models to analyze and predict COVID-19 recovery cases across 258 regions over a period of 403 days, aiming to improve prognosis accuracy.

Contribution

It introduces a novel application of LSTM for COVID-19 recovery case prediction using extensive regional and temporal data.

Findings

LSTM effectively captures time series patterns in COVID-19 recovery data.

The model provides insights into the spread and recovery trends of COVID-19.

Deep learning enhances prognosis accuracy over traditional methods.

Abstract

The present world is badly affected by novel coronavirus (COVID-19). Using medical kits to identify the coronavirus affected persons are very slow. What happens in the next, nobody knows. The world is facing erratic problem and do not know what will happen in near future. This paper is trying to make prognosis of the coronavirus recovery cases using LSTM (Long Short Term Memory). This work exploited data of 258 regions, their latitude and longitude and the number of death of 403 days ranging from 22-01-2020 to 27-02-2021. Specifically, advanced deep learning-based algorithms known as the LSTM, play a great effect on extracting highly essential features for time series data (TSD) analysis.There are lots of methods which already use to analyze propagation prediction. The main task of this paper culminates in analyzing the spreading of Coronavirus across worldwide recovery cases using LSTM deep learning-based architectures.