A machine learning forecasting model for COVID-19 pandemic in India

TLDR

COVID‑19 is a novel respiratory disease causing symptoms such as cough and fever, has become a global pandemic, and researchers are employing various numerical models to forecast its progression. This study proposes a forecasting model to predict the spread of COVID‑19 in India. The authors applied linear regression, a multilayer perceptron, and vector autoregression to Kaggle COVID‑19 data, using confirmed, death, and recovered case time series to estimate future epidemiological trends. The model projects future COVID‑19 patterns in India based on the Kaggle dataset. Continuous updates to case definitions and data integration are essential for ongoing assessment and future forecasting.

Abstract

Coronavirus disease (COVID-19) is an inflammation disease from a new virus. The disease causes respiratory ailment (like influenza) with manifestations, for example, cold, cough and fever, and in progressively serious cases, the problem in breathing. COVID-2019 has been perceived as a worldwide pandemic and a few examinations are being led utilizing different numerical models to anticipate the likely advancement of this pestilence. These numerical models dependent on different factors and investigations are dependent upon potential inclination. Here, we presented a model that could be useful to predict the spread of COVID-2019. We have performed linear regression, Multilayer perceptron and Vector autoregression method for desire on the COVID-19 Kaggle data to anticipate the epidemiological example of the ailment and pace of COVID-2019 cases in India. Anticipated the potential patterns of COVID-19 effects in India dependent on data gathered from Kaggle. With the common data about confirmed, death and recovered cases across India for over the time length helps in anticipating and estimating the not so distant future. For extra assessment or future perspective, case definition and data combination must be kept up persistently.