Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model
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. I...
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| Veröffentlicht in: | Diabetes & metabolic syndrome clinical research & reviews Jg. 14; H. 5; S. 1467 - 1474 |
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01.09.2020
Diabetes India. Published by Elsevier Ltd |
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| Abstract | 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. |
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| AbstractList | 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.INTRODUCTION AND AIMSThe 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.MATERIAL AND METHODSA 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. 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).RESULTSHere 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. 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). 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 R 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). • 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. 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. |
| Author | Tripathy, Alok Ranjan Tripathy, Alakananda Rath, Smita |
| Author_xml | – sequence: 1 givenname: Smita orcidid: 0000-0003-0547-6609 surname: Rath fullname: Rath, Smita email: smitarath@soa.ac.in organization: Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan Deemed to be University, Odisha, India – sequence: 2 givenname: Alakananda surname: Tripathy fullname: Tripathy, Alakananda email: alakanandatripathy@soa.ac.in organization: Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan Deemed to be University, Odisha, India – sequence: 3 givenname: Alok Ranjan surname: Tripathy fullname: Tripathy, Alok Ranjan email: tripathyalok@gmail.com organization: Department of Computer Science,Ravenshaw University, Cuttack, India |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32771920$$D View this record in MEDLINE/PubMed |
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| License | Copyright © 2020 Diabetes India. Published by Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
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| Snippet | 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... • 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... |
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| SubjectTerms | Betacoronavirus Coronavirus Coronavirus Infections - epidemiology Correlation coefficient COVID-19 Forecasting Humans India India - epidemiology Linear Models Linear regression Multiple linear regression Odisha Pandemics Pneumonia, Viral - epidemiology SARS-CoV-2 |
| Title | Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model |
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