IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i9p1501-d807054.html
   My bibliography  Save this article

Exploring Radial Kernel on the Novel Forced SEYNHRV-S Model to Capture the Second Wave of COVID-19 Spread and the Variable Transmission Rate

Author

Listed:
  • Fehaid Salem Alshammari

    (Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia)

  • Ezgi Akyildiz Tezcan

    (Department of Physical Medicine and Rehabilitation, Cumra State Hospital, 42500 Cumra, Konya, Turkey)

Abstract

The transmission rate of COVID-19 varies over time. There are many reasons underlying this mechanism, such as seasonal changes, lockdowns, social distancing, and wearing face masks. Hence, it is very difficult to directly measure the transmission rate. The main task of the present paper was to identify the variable transmission rate ( β 1 ) for a SIR-like model. For this, we first propose a new compartmental forced SEYNHRV-S differential model. We then drive the nonlinear differential equation and present the finite difference technique to obtain the time-dependent transmission rate directly from COVID-19 data. Following this, we show that the transmission rate can be represented as a linear combination of radial kernels, where several forms of radial kernels are explored. The proposed model is flexible and general, so it can be adapted to monitor various epidemic scenarios in various countries. Hence, the model may be of interest for policymakers as a tool to evaluate different possible future scenarios. Numerical simulations are presented to validate the prediction of our SEYNHRV and forced SEYNHRV-S models, where the data from confirmed COVID-19 cases reported by the Ministry of Health in Saudi Arabia were used. These confirmed cases show the second wave of the infected population in Saudi Arabia. By using the COVID-19 data, we show that our model (forced SEYNHRV-S) is able to predict the second wave of infection in the population in Saudi Arabia. It is well known that COVID-19 epidemic data cannot be accurately represented by any compartmental approach with constant parameters, and this is also true for our SEYNHRV model.

Suggested Citation

  • Fehaid Salem Alshammari & Ezgi Akyildiz Tezcan, 2022. "Exploring Radial Kernel on the Novel Forced SEYNHRV-S Model to Capture the Second Wave of COVID-19 Spread and the Variable Transmission Rate," Mathematics, MDPI, vol. 10(9), pages 1-17, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1501-:d:807054
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/9/1501/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/9/1501/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ioannis Chalkiadakis & Hongxuan Yan & Gareth W Peters & Pavel V Shevchenko, 2021. "Infection rate models for COVID-19: Model risk and public health news sentiment exposure adjustments," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-39, June.
    2. Asamoah, Joshua Kiddy K. & Jin, Zhen & Sun, Gui-Quan & Seidu, Baba & Yankson, Ernest & Abidemi, Afeez & Oduro, F.T. & Moore, Stephen E. & Okyere, Eric, 2021. "Sensitivity assessment and optimal economic evaluation of a new COVID-19 compartmental epidemic model with control interventions," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fehaid Salem Alshammari & Fahir Talay Akyildiz, 2023. "Epidemic Waves in a Stochastic SIRVI Epidemic Model Incorporating the Ornstein–Uhlenbeck Process," Mathematics, MDPI, vol. 11(18), pages 1-15, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Abidemi, Afeez & Ackora-Prah, Joseph & Fatoyinbo, Hammed Olawale & Asamoah, Joshua Kiddy K., 2022. "Lyapunov stability analysis and optimization measures for a dengue disease transmission model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 602(C).
    2. Asamoah, Joshua Kiddy K. & Fatmawati,, 2023. "A fractional mathematical model of heartwater transmission dynamics considering nymph and adult amblyomma ticks," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    3. Sheng Bin, 2022. "Construction and Simulation Analysis of Epidemic Propagation Model Based on COVID-19 Characteristics," IJERPH, MDPI, vol. 20(1), pages 1-16, December.
    4. Wen-Jing Zhu & Shou-Feng Shen & Wen-Xiu Ma, 2022. "A (2+1)-Dimensional Fractional-Order Epidemic Model with Pulse Jumps for Omicron COVID-19 Transmission and Its Numerical Simulation," Mathematics, MDPI, vol. 10(14), pages 1-14, July.
    5. Lv, Wei & Zhuang, Shi-Jia & Yu, Changjun, 2022. "Robust bi-objective optimal control of tungiasis diseases," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    6. Addai, Emmanuel & Zhang, Lingling & Ackora-Prah, Joseph & Gordon, Joseph Frank & Asamoah, Joshua Kiddy K. & Essel, John Fiifi, 2022. "Fractal-fractional order dynamics and numerical simulations of a Zika epidemic model with insecticide-treated nets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    7. de Mello-Sampayo, F.;, 2024. "Uncertainty in Healthcare Policy Decisions: An Epidemiological Real Options Approach to COVID-19 Lockdown Exits," Health, Econometrics and Data Group (HEDG) Working Papers 24/01, HEDG, c/o Department of Economics, University of York.
    8. Cosma, Simona & Rimo, Giuseppe & Torluccio, Giuseppe, 2023. "Knowledge mapping of model risk in banking," International Review of Financial Analysis, Elsevier, vol. 89(C).
    9. Ioannis Chalkiadakis & Gareth W. Peters & Matthew Ames, 2023. "Hybrid ARDL-MIDAS-Transformer time-series regressions for multi-topic crypto market sentiment driven by price and technology factors," Digital Finance, Springer, vol. 5(2), pages 295-365, June.
    10. Farai Nyabadza & Josiah Mushanyu & Rachel Mbogo & Gift Muchatibaya, 2023. "Modelling the Influence of Dynamic Social Processes on COVID-19 Infection Dynamics," Mathematics, MDPI, vol. 11(4), pages 1-17, February.
    11. Hoang Pham, 2022. "Mathematical Modeling the Time-Delay Interactions between Tumor Viruses and the Immune System with the Effects of Chemotherapy and Autoimmune Diseases," Mathematics, MDPI, vol. 10(5), pages 1-15, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1501-:d:807054. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.