Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model - Rath, Smita; Tripathy, Alakananda; Tripathy, Alok Ranjan (Diabetes & metabolic syndrome clinical research & reviews, 2020)
The COVID-19 pandemic originated from the city of Wuhan of China has highly affected the health, socio-economic and financial matters of the different countries of the world. India is one of the countries which is affected by the disease and thousands of people on daily basis are getting infected. In this paper, an analysis of daily statistics of people affected by the disease are taken into account to predict the next days trend in the active cases in Odisha as well as India. A valid global data set is collected from the WHO daily statistics and correlation among the total confirmed, active, deceased, positive cases are stated in this paper. Regression model such as Linear and Multiple Linear Regression techniques are applied to the data set to visualize the trend of the affected cases. Here a comparison of Linear Regression and Multiple Linear Regression model is performed where the score of the model R2tends to be 0.99 and 1.0 which indicates a strong prediction model to forecast the next coming days active cases. Using the Multiple Linear Regression model as on July month, the forecast value of 52,290 active cases are predicted towards the next month of 15th August in India and 9,358 active cases in Odisha if situation continues like this way. These models acquired remarkable accuracy in COVID-19 recognition. A strong correlation factor determines the relationship among the dependent (active) with the independent variables (positive, deceased, recovered). •Multiple linear regression model is proposed for prediction of Active cases in COVID-19 daily data.•The model predicts a value of 52,290 active cases in India and 9358 active cases in Odisha towards the 15th of August.•The ANOVA results shows a significant P value that accepts the proposed model.•Statistical results show MLR model has fair predictive potential over the LR model.