Prediction of Covid 19 Cases Based on Weather Parameters

Radha, N.; Parvathi, R.

doi:10.1007/978-981-16-2422-3_4

N. Radha¹³ &
R. Parvathi¹³

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 213))

737 Accesses

Abstract

The aim of the paper is to study the relation between the weather parameters and the confirmed Covid 19 cases. The prime weather parameters are analysed with feature selection and an analysis is made on parameters which have more impact on confirmed cases. The problem is approached with linear regression, Decision tree regressor and Random Forest Regression. The metrics used for evaluation is mean squared error and R square score. The proposed experiment is to find the model that better makes prediction of confirmed cases with weather parameters as input. It also involves finding the important weather parameters which have relation with confirmed cases.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Chapter: USD 29.95; Price excludes VAT (USA)

eBook: USD 219.00; Price excludes VAT (USA)

Softcover Book: USD 279.99; Price excludes VAT (USA)

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

Bhaskar, A., Ponnuraja, C., Srinivasan, R., Padmanaban, S.: Distribution and growth rate of COVID-19 outbreak in Tamil Nadu: A log-linear regression approach. Indian J. Public Health 64(6), 188 (2020)
Article Google Scholar
Ekum, M., Ogunsanya, A.: Application of hierarchical polynomial regression models to predict transmission of COVID-19 at global level. Int. J. Clin. Biostat. Biom 6, 027 (2020)
Google Scholar
Ogundokun, R.O., Lukman, A.F., Kibria, G.B., Awotunde, J.B., Aladeitan, B.B.: Predictive modelling of COVID-19 confirmed cases in Nigeria. Infect. Dis. Model. 5, 543–548 (2020)
Google Scholar
Rath, S., Tripathy, A., Tripathy, A.R.: Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model. Diabetes & Metab. Syndr.: Clin. Res. Rev. 14(5), 1467–1474 (2020)
Article Google Scholar
Wu, X., Nethery, R.C., Sabath, M.B., Braun, D., Dominici, F.: Air pollution and COVID-19 mortality in the United States: Strengths and limitations of an ecological regression analysis. Sci. Adv. 6(45), eabd4049 (2020)
Google Scholar

Download references

Author information

Authors and Affiliations

Vellore Institute of Technology, Chennai, TamilNadu, India
N. Radha & R. Parvathi

Authors

N. Radha
View author publications
You can also search for this author in PubMed Google Scholar
R. Parvathi
View author publications
You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to N. Radha .

Editor information

Editors and Affiliations

Gnanmani College of Engineering and Technology, Namakkal, Tamil Nadu, India
Jennifer S. Raj
Department of Business Administration, The Gerald Schwartz, School of Business, St. Francis Xavier University, Nova Scotia, NS, Canada
Ram Palanisamy
Department of Computer Engineering and Informatics, University of Patras, Patra, Greece
Isidoros Perikos
Department of Computer Science, Kennesaw State University, Kennesaw, GA, USA
Yong Shi

Rights and permissions

Reprints and permissions

Copyright information

About this paper

Cite this paper

Radha, N., Parvathi, R. (2022). Prediction of Covid 19 Cases Based on Weather Parameters. In: Raj, J.S., Palanisamy, R., Perikos, I., Shi, Y. (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 213. Springer, Singapore. https://doi.org/10.1007/978-981-16-2422-3_4

Download citation

DOI: https://doi.org/10.1007/978-981-16-2422-3_4
Published: 27 August 2021
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2421-6
Online ISBN: 978-981-16-2422-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)

Publish with us

Policies and ethics