Skip to main content

A Comparative Study of the SIR Prediction Models and Disease Control Strategies: A Case Study of the State of Kerala, India

Part of the Studies in Computational Intelligence book series (SCI,volume 923)

Abstract

The Novel Coronavirus (nCoV or COVID-19) that hit the City of Wuhan in the Hubei Province of China in December last year has become the greatest concern throughout the world. The countries in the world have shown a significant difference in the control of the spread of disease and the mortality rate. Kerala—a southern state in India—has shown notable performance in the field of disease control of COVID-19. Various measures of disease control are proved effective in the containment of COVID-19. A study of the situation in Kerala after the outbreak of COVOD-19 is used to analyze the effect of the control strategies. In this chapter, the main focus is on a comparative study of the predictions of the SIR model and the actual performance made by the state in controlling the disease.

Keywords

  • Mathematical epidemiology
  • SIR models
  • Social network analysis
  • Mathematical modeling
  • Disease control strategies

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-981-15-8534-0_8
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   169.00
Price excludes VAT (USA)
  • ISBN: 978-981-15-8534-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   219.99
Price excludes VAT (USA)
Hardcover Book
USD   219.99
Price excludes VAT (USA)
Fig. 1
Fig. 2

References

  1. World Health Organization. (2020). Coronavirus disease 2019 (COVID-19) Situation Report—152. WHO. Available from. https://www.who.int/docs/defaultsource/coronaviruse/situation-reports/20200620-covid-19-sitrep152.pdf?sfvrsn=83aff8ee_4. Accessed on June 23 2020.

  2. https://www.who.int/csr/don/05-January-2020-pneumonia-of-unkown-cause-china/en/. Accessed on 30 June 2020.

  3. https://www.who.int/csr/don/12-January-2020-novel-coronavirus-china/en/. Accessed on 30 June 2020.

  4. https://www.who.int/csr/don/14-January-2020-novel-coronavirus-thailand/en/. Accessed on 30 June 2020.

  5. https://www.who.int/csr/don/17-January-2020-novel-coronavirus-japan-ex-china/en/. Accessed on 30 June 2020.

  6. https://www.who.int/csr/don/21-January-2020-novel-coronavirus-republic-of-korea-ex-china/en/. Accessed on 30 June 2020.

  7. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200402-sitrep-73-COVID-19.pdf?sfvrsn=5ae25bc7_6. Accessed on 30 June 2020.

  8. https://en.wikipedia.org/wiki/Timeline_of_the_COVID-19_pandemic_in_India. Accessed on 30 June 2020.

  9. Mukesh, R. (2020). Coronavirus in India: Tracking country’s first 50 COVID-19 cases; what numbers tell. India Today. Retrieved 12 March 2020.

    Google Scholar 

  10. Ajith Kumar, A. K., & Anoop Kumar, A. S. (2018). Deadly Nipah outbreak in Kerala: Lessons learned for the future. Indian Journal of Critical Care Medicine, 22, 475–476.

    CrossRef  Google Scholar 

  11. Arunkumar, G., Chandni, R., Mourya, D. T., Singh, S. K., Sadanandan, R., Sudan, P., et al. (2018). Outbreak investigation of Nipah virus disease in Kerala, India, The Journal of Infectious Diseases, https://doi.org/10.1093/infdis/jiy612.

  12. Brauer, F., Van den Driessche, P., & Wu, J. (Eds.). (2008). Mathematical epidemiology. Berlin, Heidelberg: Springer.

    MATH  Google Scholar 

  13. Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings Royal Society London B Biological Science, 115, 700–721.

    MATH  Google Scholar 

  14. Kermack, W. O., & McKendrick, A. G. (1932). Contributions to the mathematical theory of epidemics, part. II. Proceedings Royal Society London, 138, 55–83.

    MATH  Google Scholar 

  15. Kermack, W. O., & McKendrick, A. G. (1932). Contributions to the mathematical theory of epidemics, part. III. Proceedings Royal Society London B Biological Science, 141, 94–112.

    MATH  Google Scholar 

  16. Yang, Z., Zeng, Z., Wang, K., Wong, S. S., Liang, W., Zanin, M., et al. (2020). Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. Journal of Thoracic Disease, 12(3), 165.

    CrossRef  Google Scholar 

  17. Chen, Y. C., Lu, P. E., Chang, C. S., & Liu, T. H. (2020). A Time-dependent SIR model for COVID-19 with undetectable infected persons. arXiv preprint arXiv:2003.00122.

  18. Calafiore, G. C., Novara, C., & Possieri, C. (2020). A modified sir model for the COVID-19 contagion in Italy. arXiv preprint arXiv:2003.14391.

  19. Roda, W. C., Varughese, M. B., Han, D., & Li, M. Y. (2020). Why is it difficult to accurately predict the COVID-19 epidemic? Infectious Disease Modelling, 5(2020), 271–281.

    CrossRef  Google Scholar 

  20. Alvarez, F. E., Argente, D., & Lippi, F. (2020). A simple planning problem for COVID-19 lockdown (No. w26981). National Bureau of Economic Research.

    Google Scholar 

  21. Maier, B. F., & Brockmann, D. (2020). Effective containment explains sub exponential growth in recent confirmed COVID-19 cases in China. Science, 368(6492), 742–746.

    CrossRef  Google Scholar 

  22. Prem, K., Liu, Y., Russell, T. W., Kucharski, A. J., Eggo, R. M., Davies, N., et al. (2020). The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modeling study. The Lancet Public Health.

    Google Scholar 

  23. Fang, Y., Nie, Y., & Penny, M. (2020). Transmission dynamics of the COVID-19 outbreak and effectiveness of government interventions: A data-driven analysis. Journal of Medical Virology, 92(6), 645–659.

    CrossRef  Google Scholar 

  24. Raza, K. (2020). Artificial intelligence against COVID-19: A meta-analysis of current research. In Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach. Studies in Big Data, 78, 2020. Berlin: Springer (In Press).

    Google Scholar 

  25. Haider Ali Biswas. (2012). Model, and control strategy of the deadly Nipah virus (NiV) infections in Bangladesh. Research and Reviews in BioSciences, 6(12), 370–377.

    Google Scholar 

  26. Reji Kumar, K. (2020). Nipah outbreak in Kerala—A network-based study, to appear in the proceedings of the International conference, ICMMCMSE 2020.

    Google Scholar 

  27. http://dhs.kerala.gov.in/. Accessed on 30 June 2020.

  28. http://dhs.kerala.gov.in/route-map/. Accessed on 30 June 2020.

Download references

Acknowledgements

I express my gratitude to the anonymous reviewers for the valuable comments and suggestions that helped me to improve the quality of this chapter.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to K. Reji Kumar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Reji Kumar, K. (2021). A Comparative Study of the SIR Prediction Models and Disease Control Strategies: A Case Study of the State of Kerala, India. In: Raza, K. (eds) Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis. Studies in Computational Intelligence, vol 923. Springer, Singapore. https://doi.org/10.1007/978-981-15-8534-0_8

Download citation