Skip to main content
SpringerLink
Log in

Prediction of Growth and Review of Factors Influencing the Transmission of COVID-19

  • Chapter
  • First Online:
Mathematical Modeling and Intelligent Control for Combating Pandemics

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 203))

  • 143 Accesses

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)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. WHO.: Coronavirus disease (COVID 19) outbreak https://www.who.int/emergencies/diseases/novelcoronavirus. Accessed 25 Mar 2020

  2. Chan, K.H., Peiris, J.S., Lam, S.Y., Poon, L.L.M., Yuen, K.Y., Seto, W.H.: The effects of temperature and relative humidity on the viability of the SARS coronavirus. Adv. Virol. (2011)

    Google Scholar 

  3. Wang, M., Jiang, A., Gong, L., Luo, L., Guo, W., Li, C., et al., Chen, Y.: Temperature significant change COVID-19 Transmission in 429 cities. medRxiv 2020a

    Google Scholar 

  4. Weather Report.: https://www.windy.com/articles/doesoutside-temperature-affect-the-spreading-ofcoronavirus-covid-19-11519?29.966,76.837,5. Accessed 25 Mar 2020a

  5. Van Doremalen, N., Bushmaker, T., Morris, D.: Aerosol and surface stability of HCoV-19 (SARS-CoV-2) compared to SARS-CoV-1. medRxiv 2020. Basic science virology research

    Google Scholar 

  6. Araujo, M.B., Naimi, B.: Spread of SARSCoV-2 Coronavirus likely to be constrained by climate. medRxiv 2020

    Google Scholar 

  7. Report on Coronavirus.: https://www.theguardian.com/world/2020/mar/24/nearly-20-of-global-population-under-coronavirus-lockdown. Accessed 11 Apr 2020

  8. Anderson, R.M., Heesterbeek, H., Klinkenberg, D., Hollingsworth, T.D.: How will country-based mitigation measures influence the course of the COVID-19 epidemic? Lancet 395(10228), 931–934 (2020)

    Article  Google Scholar 

  9. Liu, J., Zhou, J., Yao, J., Zhang, X., Li, L., Xu, X., et al., Zhang, K.: Impact of meteorological factors on the COVID-19 transmission: a multi-city study in China. Sci. Tot. Environ. 726, 138513 (2020)

    Google Scholar 

  10. Rosenberg, M.: Population Density. Geography.about.com. March 2, 2011. Retrieved on December 10, 2011

    Google Scholar 

  11. Quéré, C., Jackson, R., Jones, M., et al., Peters, G.: Temporary reduction in daily global CO2 emissions during the COVID-19 forced confinement. Nat. Clim. Change 10, 647–653 (2020)

    Google Scholar 

  12. Chen, H., Goldberg, M.S., Villeneuve, P.J.: A systematic review of the relation between long-term exposure to ambient air pollution and chronic diseases. Rev. Environ. Health 23(4), 243–297 2008

    Google Scholar 

  13. Peacock, A.J.: Oxygen at high altitude. Br. Med. J. (1998). PMC 1114067. PMID 9774298

    Google Scholar 

  14. Lauraitis, A., Maskeliunas, R., Damaševicius, R.: ANN and fuzzy logic based model to evaluate huntington disease symptoms. J. Healthc Eng. 2018, 1–10 (2018)

    Article  Google Scholar 

  15. Awotunde, J.B., Matiluko, O.E., Fatai, O.W.: Medical diagnosis system using fuzzy logic. Afr. J. Comput. ICT Ref. 7, 99–106 (2014)

    Google Scholar 

  16. Aroyehun, A., Olabiyisi, S., Omidiora, E., Ganiyu, R., Idowu, P.: Development of a fuzzy logic model for predicting the likelihood of cholera disease. WJERT 4, 340–363 (2018)

    Google Scholar 

  17. Wieczorek, M., Silka, J., Wozniak, M.: Neural network powered COVID-19 spread forecasting model. Chaos Solitons Fractals 140, 110203 (2020)

    Article  MathSciNet  Google Scholar 

  18. Majhi, B.: A modified Artificial Neural Network (ANN)-based time series prediction of COVID-19 cases from multi-country data. J. Inst. Eng. India Ser. B, 1–16 (2023)

    Google Scholar 

  19. Wang, P., Liu, H., Zheng, X., Ma, R.: A new method for spatio-temporal transmission prediction of COVID-19. Chaos Solitons Fractals 167, 112996 (2023)

    Article  MathSciNet  Google Scholar 

  20. Hasan, N.: A methodological approach for predicting COVID-19 epidemic using EEMD-ANN hybrid model. Internet Things 11, 100228 (2020)

    Article  Google Scholar 

  21. Hernandez-Matamoros, A., Fujita, H., Hayashi, T., Perez-Meana, H.: Forecasting of COVID-19 per regions using ARIMA models and polynomial functions. Appl. Soft Comput. 96, 106610 (2020)

    Article  Google Scholar 

  22. Pourhomayoun, M., Shakibi, M.: Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making. Smart Health 20, 100178 (2021)

    Article  Google Scholar 

  23. Jalali, S.M.J., Ahmadian, M., Ahmadian, S., Hedjam, R., Khosravi, A., Nahavandi, S.: X-ray image based COVID-19 detection using evolutionary deep learning approach. Expert Syst. Appl. 201, 116942 (2022)

    Article  Google Scholar 

  24. Bi, L., Fili, M., Hu, G.: COVID-19 forecasting and intervention planning using gated recurrent unit and evolutionary algorithm. Neural Comput. Appl. 34(20), 17561–17579 (2022)

    Article  Google Scholar 

  25. Acosta-González, E., Andrada-Félix, J., Fernández-Rodríguez, F.: On the evolution of the COVID-19 epidemiological parameters using only the series of deceased. A study of the Spanish outbreak using Genetic Algorithms. Math. Comput. Simul. 197, 91–104 (2022)

    MATH  Google Scholar 

  26. Hassan, M.R., Ismail, W.N., Chowdhury, A., Hossain, S., Huda, S., Hassan, M.M.: A framework of genetic algorithm-based CNN on multi-access edge computing for automated detection of COVID-19. J. Supercomput. 78(7), 10250–10274 (2022)

    Article  Google Scholar 

  27. Hazarika, B.B., Gupta, D.: Modelling and forecasting of COVID-19 spread using wavelet-coupled random vector functional link networks. Appl. Soft Comput. 96, 106626 (2020)

    Article  Google Scholar 

  28. Rustam, F., Reshi, A.A., Mehmood, A., Ullah, S., On, B.W., Aslam, W., Choi, G.S.: COVID-19 future forecasting using supervised machine learning models. IEEE Access 8, 101489–101499 (2020)

    Article  Google Scholar 

  29. Song, Q., Zheng, Y.J., Yang, J., Huang, Y.J., Sheng, W.G., Chen, S.Y.: Predicting demands of COVID-19 prevention and control materials via co-evolutionary transfer learning. IEEE Trans. Cybern. (2022)

    Google Scholar 

  30. WHO.: Coronavirus disease 2019 Situation reports. https://www.who.int/emergencies/diseases/novelcoronavirus-2019/situation-reports. Accessed 11 April 2020

  31. Bootsma, M.C., Ferguson, N.M.: The effect of public health measures on the 1918 Influenza pandemic in US cities. Proc. Natl. Acad. Sci. 104(18), 7588–7593 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology, National Institute of Technology Raipur, Raipur, India

    Gyanendra K. Verma

Authors
  1. Gyanendra K. Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gyanendra K. Verma .

Editor information

Editors and Affiliations

  1. Department of Medical Research, China Medical University Hospital, Taichung, Taiwan

    Zakia Hammouch

  2. University of Hassan II Casablanca, Casablanca, Morocco

    Mohamed Lahby

  3. Department of Mathematics and Computer Science, Cankaya University, Etimesgut, Türkiye

    Dumitru Baleanu

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Verma, G.K. (2023). Prediction of Growth and Review of Factors Influencing the Transmission of COVID-19. In: Hammouch, Z., Lahby, M., Baleanu, D. (eds) Mathematical Modeling and Intelligent Control for Combating Pandemics. Springer Optimization and Its Applications, vol 203. Springer, Cham. https://doi.org/10.1007/978-3-031-33183-1_12

Download citation

Publish with us

Policies and ethics

Search

Navigation