COVID-19 cases prediction using regression and novel SSM model for non-converged countries

TL;DR

This paper introduces a novel statistical model combining SARIMAX and regression techniques to predict COVID-19 cases in non-converged countries, aiding government planning and healthcare preparedness.

Contribution

The study develops a new Statistical SARIMAX model for better forecasting COVID-19 in countries still experiencing rising cases, addressing limitations of existing models.

Findings

Achieved an R-Squared of 0.986 in peak case prediction.

Identified that including more countries in training worsens prediction accuracy.

Demonstrated the model's effectiveness on countries like India, US, and Brazil.

Abstract

Anticipating the quantity of new associated or affirmed cases with novel coronavirus ailment 2019 (COVID-19) is critical in the counteraction and control of the COVID-19 flare-up. The new associated cases with COVID-19 information were gathered from 20 January 2020 to 21 July 2020. We filtered out the countries which are converging and used those for training the network. We utilized the SARIMAX, Linear regression model to anticipate new suspected COVID-19 cases for the countries which did not converge yet. We predict the curve of non-converged countries with the help of proposed Statistical SARIMAX model (SSM). We present new information investigation-based forecast results that can assist governments with planning their future activities and help clinical administrations to be more ready for what's to come. Our framework can foresee peak corona cases with an R-Squared value of 0.986 utilizing linear regression and fall of this pandemic at various levels for countries like India, US, and Brazil. We found that considering more countries for training degrades the prediction process as constraints vary from nation to nation. Thus, we expect that the outcomes referenced in this work will help individuals to better understand the possibilities of this pandemic.