Forecasting Covid-19 Cases in India using Deep CNN LSTM Model

EasyChair Preprint 4796

Abstract

During this worldwide crisis, it is well known that the whole world has been hit by a plenitude of untimely deaths caused during this pandemic. The lockdown in various countries has affected the lives of human beings in many ways. Because of this, it becomes necessary to study the complex interplay of various factors, ranging from macroscale components such as population density, mortality rate, and recovery rate to singular components such as diabetic patients, smokers, gender, and age. A major concern of higher authorities is the accurate forecasting of Covid-19 cases and the role of various factors in Covid-19 spread to assist the policymakers in understanding the economic situation of the country as well as the factors which affect the current mortality rate. The presented work aims to resolve these concerns by proposing a multivariate hybrid model by taking all the aforementioned factors into account to forecast Covid-19 cases. The proposed model consists of a Convolutional Neural Network (CNN) layer for feature extraction and Long Short Term Memory (LSTM) layers to forecast Covid-19 cases thus exhibiting the inherited advantage of both. The model is trained and tested on the online available dataset acquired from various resources. Experimental results show that the proposed model can forecast the number of cases in the coming month with a mean absolute error equal to 1.78 with a training accuracy of 90.63% and validation accuracy of 95.48%. The authors have also presented a complete analysis to invalidate some of the irresponsible myths that are believed to be true through various graphs.

Keyphrases: CNN, COVID-19 cases, Forecasting, LSTM, Time Series Forecasting, hybrid model, multivariate analysis

@booklet{EasyChair:4796,
  author    = {Abhishek Parashar and Yukti Mohan and Neeru Rathee},
  title     = {Forecasting Covid-19 Cases in India using Deep CNN LSTM Model},
  howpublished = {EasyChair Preprint 4796},
  year      = {EasyChair, 2020}}