Modeling COVID-19 spreading — evidence from Canada

Research Article
Open access

Modeling COVID-19 spreading — evidence from Canada

Dongchi Jiang 1*
  • 1 Engineering College, University of Sydney, Sydney NSW 2007, Australia    
  • *corresponding author djia7414@uni.sydney.edu.au
Published on 20 December 2023 | https://doi.org/10.54254/2753-8818/24/20231093
TNS Vol.24
ISSN (Print): 2753-8826
ISSN (Online): 2753-8818
ISBN (Print): 978-1-83558-221-3
ISBN (Online): 978-1-83558-222-0

Abstract

This study delves into the comprehensive examination of the COVID-19 pandemic that has been affecting the global community since late 2019. The repercussions have been ameliorated to some extent with the advent of effective vaccination campaigns, albeit the impact varies across regions and outbreaks. Beginning with an introduction to the fundamental epidemiological SIR (Susceptibility, Infection, Recovery) model, the research extrapolates it to reflect the complex dynamics of the COVID-19 scenario, employing data from Ontario, Canada, to ground the analysis in real-world observations. Several parameters and initial conditions inform the development of differential equations and ensuing line graphs within the scope of the extended VSEAIR (Vaccinated, Susceptible, Exposed, Asymptomatic Infected, Symptomatic Infected, and Recovered) model. The study scrutinizes the interplay of two pivotal aspects: the effectiveness of vaccination and the influence of governmental interventions. It offers a rigorous review of the trajectory of COVID-19 in Ontario, shedding light on potential strategies to optimize the response to the pandemic and contributing to evidence-based policymaking.

Keywords:

COVID-19, SIR model, vaccination, VSEAIR model

Jiang,D. (2023). Modeling COVID-19 spreading — evidence from Canada. Theoretical and Natural Science,24,45-51.
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References

[1]. Moyles, I., Heffernan, J., & Kong, J. (2021). Cost and Social Distancing Dynamics in a Mathematical Model of COVID-19: An Application to Ontario, Canada. Royal Society Open Science, 8(2).

[2]. Barman, Madhab, and Nachiketa Mishra. (2020) A Time-Delay SEAIR Model for COVID-19 Spread. 2020 IEEE 4th Conference on Information & Communication Technology (CICT).

[3]. Angeli, Mattia, et al. (2022) Modeling the Effect of the Vaccination Campaign on the COVID-19 Pandemic. Chaos, Solitons &amp, Fractals, 154, 111621.

[4]. Batistela, Cristiane M., et al. (2021) Sirsi Compartmental Model for COVID-19 Pandemic with Immunity Loss.” Chaos, Solitons & Fractals, 142, 110388.

[5]. Canada, Public Health Agency of. (2021) COVID-19 Daily Epidemiology Update. Canada.ca, 28 May 2021.

[6]. Evolution of COVID-19 Case Growth in Ontario.

[7]. World Health Organization WHO Coronavirus disease (COVID-19) dashboard2020[online] [cited 25 Jun 2020].

[8]. Mishra, B. K., Keshri, A. K., Rao, Y. S., Mishra, B. K., Mahato, B., Ayesha, S., Rukhaiyyar, B. P., Saini, D. K., & Singh, A. K. (2020). COVID-19 created chaos across the globe: Three novel quarantine epidemic models. Chaos, Solitons & Fractals, 138, 109928.

[9]. Smirnova A., deCamp L., Chowell G. (2017) Forecasting epidemics through nonparametric estimation of time-dependent transmission rates using the SEIR model. Bull Math Biol, 81(11):4343–4365.

[10]. Alanazi S.A., Kamruzzaman M.M., Alruwaili M., Alshammari N., Alqahtani S.A., Karime A. (2020) Measuring and preventing COVID-19 using the SIR model and machine learning in smart health care. J Healthc Eng. 1–12.

[11]. Fanelli, D., Piazza, F. (2020). Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos, Solitons & Fractals, 134(134), 109761.

Cite this article

Jiang,D. (2023). Modeling COVID-19 spreading — evidence from Canada. Theoretical and Natural Science,24,45-51.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Biological Engineering and Medical Science

ISBN：978-1-83558-221-3(Print) / 978-1-83558-222-0(Online)
Editor：Alan Wang
Conference website: https://www.icbiomed.org/
Conference date: 2 September 2023
Series: Theoretical and Natural Science
Volume number: Vol.24
ISSN：2753-8818(Print) / 2753-8826(Online)

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References

[1]. Moyles, I., Heffernan, J., & Kong, J. (2021). Cost and Social Distancing Dynamics in a Mathematical Model of COVID-19: An Application to Ontario, Canada. Royal Society Open Science, 8(2).

[2]. Barman, Madhab, and Nachiketa Mishra. (2020) A Time-Delay SEAIR Model for COVID-19 Spread. 2020 IEEE 4th Conference on Information & Communication Technology (CICT).

[3]. Angeli, Mattia, et al. (2022) Modeling the Effect of the Vaccination Campaign on the COVID-19 Pandemic. Chaos, Solitons &amp, Fractals, 154, 111621.

[4]. Batistela, Cristiane M., et al. (2021) Sirsi Compartmental Model for COVID-19 Pandemic with Immunity Loss.” Chaos, Solitons & Fractals, 142, 110388.

[5]. Canada, Public Health Agency of. (2021) COVID-19 Daily Epidemiology Update. Canada.ca, 28 May 2021.

[6]. Evolution of COVID-19 Case Growth in Ontario.

[7]. World Health Organization WHO Coronavirus disease (COVID-19) dashboard2020[online] [cited 25 Jun 2020].

[8]. Mishra, B. K., Keshri, A. K., Rao, Y. S., Mishra, B. K., Mahato, B., Ayesha, S., Rukhaiyyar, B. P., Saini, D. K., & Singh, A. K. (2020). COVID-19 created chaos across the globe: Three novel quarantine epidemic models. Chaos, Solitons & Fractals, 138, 109928.

[9]. Smirnova A., deCamp L., Chowell G. (2017) Forecasting epidemics through nonparametric estimation of time-dependent transmission rates using the SEIR model. Bull Math Biol, 81(11):4343–4365.

[10]. Alanazi S.A., Kamruzzaman M.M., Alruwaili M., Alshammari N., Alqahtani S.A., Karime A. (2020) Measuring and preventing COVID-19 using the SIR model and machine learning in smart health care. J Healthc Eng. 1–12.

[11]. Fanelli, D., Piazza, F. (2020). Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos, Solitons & Fractals, 134(134), 109761.

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