Skip to main content

Advertisement

Log in

Mathematical Model and AI Integration for COVID-19: Improving Forecasting and Policy-Making

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

In this work, a new susceptible–exposed–infectious–recovered (SEIR) compartmental model is proposed which has additional media influence for precise quantization of the coronavirus disease 2019 (COVID-19). In the proposed model, first-order ordinary differential equations (ODEs) are used for the formulation of basic reproduction number, whereas genetic algorithm (GA) is used for its estimation. The inclusion of climatic parameters, governmental impact, and human behavioral response toward the disease provides an upper hand in determining the dynamics of its transmissibility, thereby indicating their significance in precising the outcomes. In addition, the future trends for the new normalized confirmed cases of COVID-19 are predicted using the long short-term memory (LSTM) model which helps in evaluating and modifying the current preventive actions taken to improve the situation. The robustness of the proposed model is measured by five different error functions which are tested in five different countries. According to the experimental results, this is observed that the proposed model has a smaller prediction deviation as well and the proposed scheme outperforms state-of-the-art models of COVID-19.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Algorithm 1
Fig. 3
Algorithm 2
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data Availability

The datasets analyzed during the current study are available from the corresponding author upon reasonable request.

References

  1. Srivastav AK, Tiwari PK, Srivastava PK, Ghosh M, Kang Y. A mathematical model for the impacts of face mask, hospitalization and quarantine on the dynamics of COVID-19 in India: deterministic vs. stochastic. Math Biosci Eng. 2021;18(1):182–213.

    Article  MathSciNet  Google Scholar 

  2. Samsuzzoha MD. A study on numerical solutions of epidemic models. 2012.

  3. Siettos CI, Russo L. Mathematical modeling of infectious disease dynamics. Virulence. 2013;4(4):295–306.

    Article  Google Scholar 

  4. Kermack WO, McKendrick AG. A contribution to the mathematical theory of epidemics. Proc R Soc Lond. 1927;115:700–21.

    Google Scholar 

  5. Dixit R, Panda DS, Panda SS. An advanced susceptible–exposed–infectious–recovered model for quantitative analysis of COVID-19. Sādhanā. 2021;46(2):1–10.

    Article  MathSciNet  Google Scholar 

  6. Farkas C, Iclanzan D, Olteán-Péter B, Vekov G. Estimation of parameters for a humidity-dependent compartmental model of the COVID-19 outbreak. PeerJ. 2021;9: e10790.

    Article  Google Scholar 

  7. Casagrandi R, Bolzoni L, Levin SA, Andreasen V. The SIRC model and influenza A. Math Biosci. 2006;200(2):152–69.

    Article  MathSciNet  Google Scholar 

  8. Feng L. SEIR model combined with LSTM and GRU for the trend analysis of COVID-19. 2021.

  9. Bagal DK, Rath A, Barua A, Patnaik D. Estimating the parameters of susceptible-infected-recovered model of COVID-19 cases in India during lockdown periods. Chaos Solitons Fractals. 2020;140: 110154.

    Article  MathSciNet  Google Scholar 

  10. Chowell G, Ammon CE, Hengartner NW, Hyman JM. Estimation of the reproductive number of the Spanish flu epidemic in Geneva, Switzerland. Vaccine. 2006;24(44–46):6747–50.

    Article  Google Scholar 

  11. Ceylan Z. Short-term prediction of COVID-19 spread using grey rolling model optimized by particle swarm optimization. Appl Soft Comput. 2021;109: 107592.

    Article  Google Scholar 

  12. Martinez ME. The calendar of epidemics: seasonal cycles of infectious diseases. PLoS Pathog. 2018;14(11): e1007327.

    Article  Google Scholar 

  13. Fares A. Factors influencing the seasonal patterns of infectious diseases. Int J Prev Med. 2013;4(2):128.

    Google Scholar 

  14. Rai RK, Khajanchi S, Tiwari PK, Venturino E, Misra AK. Impact of social media advertisements on the transmission dynamics of COVID-19 pandemic in India. J Appl Math Comput. 2022;68(1):19–44.

    Article  MathSciNet  Google Scholar 

  15. Misra AK, Sharma A, Shukla JB. Modeling and analysis of effects of awareness programs by media on the spread of infectious diseases. Math Comput Model. 2011;53(5–6):1221–8.

    Article  MathSciNet  Google Scholar 

  16. Misra AK, Rai RK, Takeuchi Y. Modeling the control of infectious diseases: effects of TV and social media advertisements. Math Biosci Eng. 2018;15(6):1315.

    Article  MathSciNet  Google Scholar 

  17. Xiao Y, Tang S, Wu J. Media impact switching surface during an infectious disease outbreak. Sci Rep. 2015;5(1):1–9.

    Google Scholar 

  18. Ljubic B, Roychoudhury S, Cao XH, Pavlovski M, Obradovic S, Nair R, Glass L, Obradovic Z. Influence of medical domain knowledge on deep learning for Alzheimer’s disease prediction. Comput Methods Progr Biomed. 2020;197: 105765.

    Article  Google Scholar 

  19. Santosh T, Ramesh D, Reddy D. LSTM-based prediction of malaria abundances using big data. Comput Biol Med. 2020;124: 103859.

    Article  Google Scholar 

  20. Yudistira N, Sumitro SB, Nahas A, Riama NF. Learning where to look for COVID-19 growth: multivariate analysis of COVID-19 cases over time using explainable convolution-LSTM. Appl Soft Comput. 2021;109: 107469.

    Article  Google Scholar 

  21. Shahid F, Zameer A, Muneeb M. Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM. Chaos Solitons Fractals. 2020;140: 110212.

    Article  MathSciNet  Google Scholar 

  22. Kumar M, Gupta S, Kumar K, Sachdeva M. Spreading of COVID-19 in India, Italy, Japan, Spain, UK, US: a prediction using ARIMA and LSTM model. Digit Govern Res Pract. 2020;1(4):1–9.

    Article  Google Scholar 

  23. Bedi P, Dhiman S, Gole P, Gupta N, Jindal V. Prediction of COVID-19 trend in India and its four worst-affected states using modified SEIRD and LSTM models. SN Comput Sci. 2021;2(3):1–24.

    Article  Google Scholar 

  24. Kırbaş İ, Sözen A, Tuncer AD, Kazancıoğlu FŞ. Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches. Chaos Solitons Fractals. 2020;138: 110015.

    Article  Google Scholar 

  25. Chimmula VKR, Zhang L. Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos Solitons Fractals. 2020;135: 109864.

    Article  Google Scholar 

  26. Bai S. Simulations of COVID-19 spread by spatial agent-based model and ordinary differential equations. Int J Simul Process Model. 2020;15(3):268–77.

    Article  Google Scholar 

  27. Tang B, Wang X, Li Q, Bragazzi NL, Tang S, Xiao Y, Wu J. Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions. J Clin Med. 2020;9(2):462.

    Article  Google Scholar 

  28. Ralph R, Lew J, Zeng T, Francis M, Xue B, Roux M, Ostadgavahi AT, Rubino S, Dawe NJ, Al-Ahdal MN. 2019-nCoV (Wuhan virus), a novel coronavirus: human-to-human transmission, travel-related cases, and vaccine readiness. J Infect Dev Ctries. 2020;14(1):3–17.

    Article  Google Scholar 

  29. Fisman DN, Greer AL, Tuite AR. Bidirectional impact of imperfect mask use on reproduction number of COVID-19: a next generation matrix approach. Infect Dis Model. 2020;5:405–8.

    Google Scholar 

  30. Zandavi SM, Rashidi TH, Vafaee F. Forecasting the spread of COVID-19 under control scenarios using LSTM and dynamic behavioral models. 2020. arXiv preprint arXiv:2005.12270.

  31. Abadi MQH, Rahmati S, Sharifi A, Ahmadi M. HSSAGA: designation and scheduling of nurses for taking care of COVID-19 patients using novel method of Hybrid Salp Swarm Algorithm and Genetic Algorithm. Appl Soft Comput. 2021;108: 107449.

    Article  Google Scholar 

  32. Rahimi I, Chen F, Gandomi AH. A review on COVID-19 forecasting models. Neural Comput Appl. 2021:1–11.

  33. Demir F. DeepCoroNet: a deep LSTM approach for automated detection of COVID-19 cases from chest X-ray images. Appl Soft Comput. 2021;103: 107160.

    Article  Google Scholar 

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

Authors

Contributions

DSP: Conceptualization, Methodology, Data Curation, Writing—Review and Editing. RD: Writing—Review and Editing, Research Supervision. AD: Writing—Original Draft. HD: Research Survey. AS: Research Supervision.

Corresponding author

Correspondence to Dev Sourav Panda.

Ethics declarations

Conflict of interest

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Research involving human and/or animals

Not applicable.

Informed consent

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Machine Learning for Pandemic Prediction and Control” guest edited by Anand J Kulkarni, Akash Tayal, Patrick Siarry, Arun Solanki and Ali Husseinzadeh Kashan.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Panda, D.S., Dixit, R., Dixit, A. et al. Mathematical Model and AI Integration for COVID-19: Improving Forecasting and Policy-Making. SN COMPUT. SCI. 5, 246 (2024). https://doi.org/10.1007/s42979-023-02574-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-023-02574-7

Keywords

Navigation