tetano
Editor, Senior Moderator
Diabetes Metab Syndr
. 2020 Aug 1;14(5):1467-1474.
doi: 10.1016/j.dsx.2020.07.045. Online ahead of print.
Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model
Smita Rath[SUP] 1 [/SUP], Alakananda Tripathy[SUP] 2 [/SUP], Alok Ranjan Tripathy[SUP] 3 [/SUP]
Affiliations
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 R[SUP]2[/SUP][SUB]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.[/SUB] 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).
Keywords: Coronavirus; Correlation coefficient; India; Linear regression; Multiple linear regression; Odisha.
. 2020 Aug 1;14(5):1467-1474.
doi: 10.1016/j.dsx.2020.07.045. Online ahead of print.
Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model
Smita Rath[SUP] 1 [/SUP], Alakananda Tripathy[SUP] 2 [/SUP], Alok Ranjan Tripathy[SUP] 3 [/SUP]
Affiliations
- PMID: 32771920
- DOI: 10.1016/j.dsx.2020.07.045
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 R[SUP]2[/SUP][SUB]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.[/SUB] 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).
Keywords: Coronavirus; Correlation coefficient; India; Linear regression; Multiple linear regression; Odisha.