IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v136y2020ics0960077920302289.html
   My bibliography  Save this article

SBDiEM: A new mathematical model of infectious disease dynamics

Author

Listed:
  • Bekiros, Stelios
  • Kouloumpou, Dimitra

Abstract

A worldwide multi-scale interplay among a plethora of factors, ranging from micro-pathogens and individual or population interactions to macro-scale environmental, socio-economic and demographic conditions, entails the development of highly sophisticated mathematical models for robust representation of the contagious disease dynamics that would lead to the improvement of current outbreak control strategies and vaccination and prevention policies. Due to the complexity of the underlying interactions, both deterministic and stochastic epidemiological models are built upon incomplete information regarding the infectious network. Hence, rigorous mathematical epidemiology models can be utilized to combat epidemic outbreaks. We introduce a new spatiotemporal approach (SBDiEM) for modeling, forecasting and nowcasting infectious dynamics, particularly in light of recent efforts to establish a global surveillance network for combating pandemics with the use of artificial intelligence. This model can be adjusted to describe past outbreaks as well as COVID-19. Our novel methodology may have important implications for national health systems, international stakeholders and policy makers.

Suggested Citation

  • Bekiros, Stelios & Kouloumpou, Dimitra, 2020. "SBDiEM: A new mathematical model of infectious disease dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    • Handle: RePEc:eee:chsofr:v:136:y:2020:i:c:s0960077920302289
    DOI: 10.1016/j.chaos.2020.109828
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077920302289
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2020.109828?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bai, Yanping & Jin, Zhen, 2005. "Prediction of SARS epidemic by BP neural networks with online prediction strategy," Chaos, Solitons & Fractals, Elsevier, vol. 26(2), pages 559-569.
    2. Radina P Soebiyanto & Farida Adimi & Richard K Kiang, 2010. "Modeling and Predicting Seasonal Influenza Transmission in Warm Regions Using Climatological Parameters," PLOS ONE, Public Library of Science, vol. 5(3), pages 1-10, March.
    3. Helen J Wearing & Pejman Rohani & Matt J Keeling, 2005. "Appropriate Models for the Management of Infectious Diseases," PLOS Medicine, Public Library of Science, vol. 2(7), pages 1-1, July.
    4. Xing, Yi & Song, Lipeng & Sun, Gui-Quan & Jin, Zhen & Zhang, Juan, 2017. "Assessing reappearance factors of H7N9 avian influenza in China," Applied Mathematics and Computation, Elsevier, vol. 309(C), pages 192-204.
    5. Höhle, Michael & Paul, Michaela, 2008. "Count data regression charts for the monitoring of surveillance time series," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4357-4368, May.
    6. Neil M. Ferguson & Derek A.T. Cummings & Simon Cauchemez & Christophe Fraser & Steven Riley & Aronrag Meeyai & Sopon Iamsirithaworn & Donald S. Burke, 2005. "Strategies for containing an emerging influenza pandemic in Southeast Asia," Nature, Nature, vol. 437(7056), pages 209-214, September.
    7. Alex Spanos & George Theocharis & Drosos E Karageorgopoulos & George Peppas & Dimitris Fouskakis & Matthew E Falagas, 2012. "Surveillance of Community Outbreaks of Respiratory Tract Infections Based on House-Call Visits in the Metropolitan Area of Athens, Greece," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-6, August.
    8. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    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. Victor Zakharov & Yulia Balykina & Ovanes Petrosian & Hongwei Gao, 2020. "CBRR Model for Predicting the Dynamics of the COVID-19 Epidemic in Real Time," Mathematics, MDPI, vol. 8(10), pages 1-10, October.
    2. Hanthanan Arachchilage, Kalpana & Hussaini, Mohammed Yousuff, 2021. "Ranking non-pharmaceutical interventions against Covid-19 global pandemic using global sensitivity analysis—Effect on number of deaths," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    3. Veli B. Shakhmurov & Muhammet Kurulay & Aida Sahmurova & Mustafa Can Gursesli & Antonio Lanata, 2023. "A Novel Nonlinear Dynamic Model Describing the Spread of Virus," Mathematics, MDPI, vol. 11(20), pages 1-15, October.
    4. Milad Haghani & Michiel C. J. Bliemer, 2020. "Covid-19 pandemic and the unprecedented mobilisation of scholarly efforts prompted by a health crisis: Scientometric comparisons across SARS, MERS and 2019-nCoV literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2695-2726, December.
    5. Matouk, A.E., 2020. "Complex dynamics in susceptible-infected models for COVID-19 with multi-drug resistance," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    6. Mishra, A.M. & Purohit, S.D. & Owolabi, K.M. & Sharma, Y.D., 2020. "A nonlinear epidemiological model considering asymptotic and quarantine classes for SARS CoV-2 virus," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    7. Anand, Monalisa & Danumjaya, P. & Rao, P. Raja Sekhara, 2023. "A nonlinear mathematical model on the Covid-19 transmission pattern among diabetic and non-diabetic population," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 210(C), pages 346-369.
    8. Yigit Aydogan, 2020. "A Microeconomic Analysis of the COVID-19 Distribution in Turkey," Bingol University Journal of Economics and Administrative Sciences, Bingol University, Faculty of Economics and Administrative Sciences, vol. 4(2), pages 11-25, December.
    9. Barrio, Rafael A. & Kaski, Kimmo K. & Haraldsson, Guđmundur G. & Aspelund, Thor & Govezensky, Tzipe, 2021. "A model for social spreading of Covid-19: Cases of Mexico, Finland and Iceland," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    10. Javier Cifuentes-Faura & Ursula Faura-Martínez & Matilde Lafuente-Lechuga, 2022. "Mathematical Modeling and the Use of Network Models as Epidemiological Tools," Mathematics, MDPI, vol. 10(18), pages 1-14, September.
    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.
    12. Gandzha, I.S. & Kliushnichenko, O.V. & Lukyanets, S.P., 2021. "Modeling and controlling the spread of epidemic with various social and economic scenarios," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).

    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. Robin N Thompson & Christopher A Gilligan & Nik J Cunniffe, 2016. "Detecting Presymptomatic Infection Is Necessary to Forecast Major Epidemics in the Earliest Stages of Infectious Disease Outbreaks," PLOS Computational Biology, Public Library of Science, vol. 12(4), pages 1-18, April.
    2. Linus Schiöler & Marianne Fris�n, 2012. "Multivariate outbreak detection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 223-242, April.
    3. Ayaz Hyder & David L Buckeridge & Brian Leung, 2013. "Predictive Validation of an Influenza Spread Model," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-20, June.
    4. Marianne Frisén, 2014. "Spatial outbreak detection based on inference principles for multivariate surveillance," IISE Transactions, Taylor & Francis Journals, vol. 46(8), pages 759-769, August.
    5. Soo Beom Choi & Juhyeon Kim & Insung Ahn, 2019. "Forecasting type-specific seasonal influenza after 26 weeks in the United States using influenza activities in other countries," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-15, November.
    6. Logan C Brooks & David C Farrow & Sangwon Hyun & Ryan J Tibshirani & Roni Rosenfeld, 2018. "Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions," PLOS Computational Biology, Public Library of Science, vol. 14(6), pages 1-29, June.
    7. Jurić, Tado, 2021. "Google Trends as a Method to Predict New COVID-19 Cases and Socio-Psychological Consequences of the Pandemic," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 7(forthcomi).
    8. Anna K. Lugnér & Sido D. Mylius & Jacco Wallinga, 2010. "Dynamic versus static models in cost‐effectiveness analyses of anti‐viral drug therapy to mitigate an influenza pandemic," Health Economics, John Wiley & Sons, Ltd., vol. 19(5), pages 518-531, May.
    9. Taesik Lee & Hayong Shin, 2016. "Combining syndromic surveillance and ILI data using particle filter for epidemic state estimation," Flexible Services and Manufacturing Journal, Springer, vol. 28(1), pages 233-253, June.
    10. Logan C Brooks & David C Farrow & Sangwon Hyun & Ryan J Tibshirani & Roni Rosenfeld, 2015. "Flexible Modeling of Epidemics with an Empirical Bayes Framework," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-18, August.
    11. Tamer Edirne & Dilek Avci & Burçak Dagkara & Muslum Aslan, 2011. "Knowledge and anticipated attitudes of the community about bird flu outbreak in Turkey, 2007–2008: a survey-based descriptive study," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 56(2), pages 163-168, April.
    12. Xiaoli Wang & Shuangsheng Wu & C Raina MacIntyre & Hongbin Zhang & Weixian Shi & Xiaomin Peng & Wei Duan & Peng Yang & Yi Zhang & Quanyi Wang, 2015. "Using an Adjusted Serfling Regression Model to Improve the Early Warning at the Arrival of Peak Timing of Influenza in Beijing," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-14, March.
    13. Wei Zhong, 2017. "Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 475-495, December.
    14. Lahrouz, A. & El Mahjour, H. & Settati, A. & Bernoussi, A., 2018. "Dynamics and optimal control of a non-linear epidemic model with relapse and cure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 299-317.
    15. Markowitz, Sara & Nesson, Erik & Robinson, Joshua J., 2019. "The effects of employment on influenza rates," Economics & Human Biology, Elsevier, vol. 34(C), pages 286-295.
    16. Bentzen, Jeanet Sinding, 2021. "In crisis, we pray: Religiosity and the COVID-19 pandemic," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 541-583.
    17. Jesse T. Richman & Ryan J. Roberts, 2023. "Assessing Spurious Correlations in Big Search Data," Forecasting, MDPI, vol. 5(1), pages 1-12, February.
    18. Chen, Dandan & Zheng, Muhua & Zhao, Ming & Zhang, Yu, 2018. "A dynamic vaccination strategy to suppress the recurrent epidemic outbreaks," Chaos, Solitons & Fractals, Elsevier, vol. 113(C), pages 108-114.
    19. Zhang, Tailei & Teng, Zhidong, 2008. "Global asymptotic stability of a delayed SEIRS epidemic model with saturation incidence," Chaos, Solitons & Fractals, Elsevier, vol. 37(5), pages 1456-1468.
    20. Linus Nyiwul, 2021. "Epidemic Control and Resource Allocation: Approaches and Implications for the Management of COVID-19," Studies in Microeconomics, , vol. 9(2), pages 283-305, December.

    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:eee:chsofr:v:136:y:2020:i:c:s0960077920302289. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.