IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i4p1660-d496650.html
   My bibliography  Save this article

On the Transmission Dynamics of SARS-CoV-2 in a Temperate Climate

Author

Listed:
  • Ioannis Kioutsioukis

    (Department of Physics, University of Patras, 26504 Rio, Greece)

  • Nikolaos I. Stilianakis

    (Joint Research Centre (JRC), European Commission, 2027 Ispra, Italy
    Department of Biometry and Epidemiology, University of Erlangen-Nuremberg, 91054 Erlangen, Germany)

Abstract

An epidemiological model, which describes the transmission dynamics of SARS-CoV-2 under specific consideration of the incubation period including the population with subclinical infections and being infective is presented. The COVID-19 epidemic in Greece was explored through a Monte Carlo uncertainty analysis framework, and the optimal values for the parameters that determined the transmission dynamics could be obtained before, during, and after the interventions to control the epidemic. The dynamic change of the fraction of asymptomatic individuals was shown. The analysis of the modelling results at the intra-annual climatic scale allowed for in depth investigation of the transmission dynamics of SARS-CoV-2 and the significance and relative importance of the model parameters. Moreover, the analysis at this scale incorporated the exploration of the forecast horizon and its variability. Three discrete peaks were found in the transmission rates throughout the investigated period (15 February–15 December 2020). Two of them corresponded to the timing of the spring and autumn epidemic waves while the third one occurred in mid-summer, implying that relaxation of social distancing and increased mobility may have a strong effect on rekindling the epidemic dynamics offsetting positive effects from factors such as decreased household crowding and increased environmental ultraviolet radiation. In addition, the epidemiological state was found to constitute a significant indicator of the forecast reliability horizon, spanning from as low as few days to more than four weeks. Embedding the model in an ensemble framework may extend the predictability horizon. Therefore, it may contribute to the accuracy of health risk assessment and inform public health decision making of more efficient control measures.

Suggested Citation

  • Ioannis Kioutsioukis & Nikolaos I. Stilianakis, 2021. "On the Transmission Dynamics of SARS-CoV-2 in a Temperate Climate," IJERPH, MDPI, vol. 18(4), pages 1-17, February.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:4:p:1660-:d:496650
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/4/1660/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/4/1660/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. J. A. P. Heesterbeek & K. Dietz, 1996. "The concept of Ro in epidemic theory," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 50(1), pages 89-110, March.
    2. James M McCaw & James G Wood & Christopher T McCaw & Jodie McVernon, 2008. "Impact of Emerging Antiviral Drug Resistance on Influenza Containment and Spread: Influence of Subclinical Infection and Strategic Use of a Stockpile Containing One or Two Drugs," PLOS ONE, Public Library of Science, vol. 3(6), pages 1-10, June.
    Full references (including those not matched with items on IDEAS)

    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. Eugenio Valdano & Davide Colombi & Chiara Poletto & Vittoria Colizza, 2023. "Epidemic graph diagrams as analytics for epidemic control in the data-rich era," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Antoine Djogbenou & Christian Gouriéroux & Joann Jasiak & Paul Rilstone, 2022. "An Econometric Panel Data Model of the COVID-19 Pandemic," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 11(1), pages 1-3.
    3. András Schubert & Wolfgang Glänzel & Gábor Schubert, 2022. "Eponyms in science: famed or framed?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1199-1207, March.
    4. Carnehl, Christoph & Fukuda, Satoshi & Kos, Nenad, 2023. "Epidemics with behavior," Journal of Economic Theory, Elsevier, vol. 207(C).
    5. Imelda Trejo & Nicolas W Hengartner, 2022. "A modified Susceptible-Infected-Recovered model for observed under-reported incidence data," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-23, February.
    6. Lin William Cong & Ke Tang & Bing Wang & Jingyuan Wang, 2021. "An AI-assisted Economic Model of Endogenous Mobility and Infectious Diseases: The Case of COVID-19 in the United States," Papers 2109.10009, arXiv.org.
    7. Pedro, S.A. & Rwezaura, H. & Mandipezar, A. & Tchuenche, J.M., 2021. "Qualitative Analysis of an influenza model with biomedical interventions," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    8. Naveed Chehrazi & Lauren E. Cipriano & Eva A. Enns, 2019. "Dynamics of Drug Resistance: Optimal Control of an Infectious Disease," Operations Research, INFORMS, vol. 67(3), pages 619-650, May.
    9. Gong, Jiangyue & Gujjula, Krishna Reddy & Ntaimo, Lewis, 2023. "An integrated chance constraints approach for optimal vaccination strategies under uncertainty for COVID-19," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    10. Rezapour, Shabnam & Baghaian, Atefe & Naderi, Nazanin & Sarmiento, Juan P., 2023. "Infection transmission and prevention in metropolises with heterogeneous and dynamic populations," European Journal of Operational Research, Elsevier, vol. 304(1), pages 113-138.
    11. Christine Jacob, 2010. "Branching Processes: Their Role in Epidemiology," IJERPH, MDPI, vol. 7(3), pages 1-19, March.
    12. Besjana MEMA & Sabina TOSUNI, 2024. "Implementation of mathematics model in public health: Albanian case study," Smart Cities and Regional Development (SCRD) Journal, Smart-EDU Hub, Faculty of Public Administration, National University of Political Studies & Public Administration, vol. 8(3), pages 41-54, April.
    13. Florin Avram & Rim Adenane & Lasko Basnarkov, 2024. "Some Probabilistic Interpretations Related to the Next-Generation Matrix Theory: A Review with Examples," Mathematics, MDPI, vol. 12(15), pages 1-16, August.
    14. Fabricius, Gabriel & Maltz, Alberto, 2020. "Exploring the threshold of epidemic spreading for a stochastic SIR model with local and global contacts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    15. Chang, Joseph T. & Kaplan, Edward H., 2023. "Modeling local coronavirus outbreaks," European Journal of Operational Research, Elsevier, vol. 304(1), pages 57-68.

    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:jijerp:v:18:y:2021:i:4:p:1660-:d:496650. 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.