Forecasting COVID-19 cases in India Using Machine Learning Models

Abstract

COVID-19 pandemic has affected the economy and changed the human way of life, disrupting everyone's mental, physical, and financial well-being. Many of the fastestgrowing economies are strained owing to the severity and communicability of the epidemic. Because of the increasing diversity of cases and the resulting burden on healthcare practitioners and the government, therefore, predicting the number of infected COVID-19 cases which could be useful in planning the required hospital resources in the future. In this paper, we focussed on information-led methods of estimating the numbers of COVID-19 confirmed cases in the country and their implications in the future, using different learning models such as Sigmoid modelling, ARIMA, SEIR model and LSTM, for protective measures, such as social isolation or the lockout of COVID-19. . Use of raw data by separating an event from the previous event in order to set the time series. The computation of number of positive incidents, number of rereferred incidents are reliable within a limited range. A datadriven forecasting method has been used to approximate the total confirmed cases in coming months. These LSTM model gave very promising results than other models. Hence, this work would help the decision makers to understand the upcoming of the pandemic trajectory in the country and take necessary actions for the effect of interventions.

Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review

“…To predict the epidemic growth rare, an LSTM method is proposed in Yudistira [104] . In Rani et al [105] ARIMA is combined with LSTM to predict the pandemic.…”

A novel predictive model for capturing threats for facilitating effective social distancing in COVID-19

“…The work carried out by Rezaei and Azarmi [20] have developed a framework which can perform assessment of risk associated with COVID-19 using a new idea of social distancing. A closer look into various attempted approach of social distancing shows the adoption of artificial intelligence [21]- [24], machine learning [25], [26], game theory [27], [28], different variants of neural network [29], [30] when it comes to controlling the pandemic situation control.…”