Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model

Abstract

Introduction and Aims 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. Material and methods 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. Results Here a comparison of Linear Regression and Multiple Linear Regression model is performed where the score of the model tends 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. Conclusion 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).

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COVID-19 modelling in the Caribbean: Spatial and statistical assessments

“…We adopted a linear approach assuming strict exogeneity amongst the explanatory variables. A linear approach was outlined by several studies, as the literature shows the variations in COVID-19 cases deaths and cases fits a linear structural model best when regressed against various exogenous variables ( Adekunle et al, 2020 ; Nguimkeu and Tadadjeu, 2021 ; Ogundokun et al, 2020 ; Rath et al, 2020 ; Sardokie and Owusu, 2020 ). The functional relationship is expressed as: where Y iN are the dependent variables related to aggregated COVID-19 cases and deaths for the individual countries (i) for the total number of countries observed in the dataset (N) and X 1 ,X 2 ,.....,X iN are independent exogenous variables for the ith country in the total number of countries (N).…”

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“…Rath [16] used Multiple Linear Regression Model to predict that the number of daily active cases in India would reach 52,290 by 15 August. Ayyoubzadeh et al [17] used Multiple Linear Regression Method to predict the spread of COVID-19 in Iran, and found that in addition to the incidence of the previous day, factors that can effectively improve the accuracy of the prediction also include hand sanitizer usage and hand washing frequency.…”