Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 9, 13 November 2023
Open
Access | Article
The application of SIR model in COVID-19
* Author to whom correspondence should be addressed.
Abstract
The SIR model was used to better comprehend and analyse the transmission dynamics of COVID-19. This mathematical framework splits the population into three compartments: suspectable, infectious, and recovered, allowing disease spread to be simulated across time. After making some essential assumptions of SIR model, the project illustrates the rate of suspectable, infected, recovered individuals over time by constructing several differential equations using specific parameters. Also, SIR model gives insights into expected disease trajectories, the impact of therapies, and other pertinent discoveries by including critical factors and assumptions. Researchers successfully anticipate disease trajectories using this simulation, indicating the usefulness of actions in preventing viral propagation. Researchers have found that the incubation period of COVID-19 has vital impact on the epidemic curve, which results in a slower growth in the number of infected people overtime and a delay in the upward slope of the infectious in the epidemic curve. The SIR model’s examination of epidemic curves has assisted in identifying the peak of infections, estimating the duration of outbreaks, and assessing the efficiency of public health measures in various context. Further study, continued data collecting, and integration with real-world data will improve the accuracy and usefulness of the SIR model, enabling evidence-based ways to combating COVID-19’s issues.
Keywords
SIR Model, COVID-19, Disease Dynamics, Disease Transmission, Epidemic Modelling
References
1. Charles R T, Henrique L and Diodo F 2020 SARS-COV-2: SIR Model Limitations and Predictive Constraints. SYMMETRY.
2. Lawson A B and Kim J 2022 Bayesian Space-time SIR Modeling of COVID-19 in Two US States During the 2020-2021 Pandemic. MED. EPIDEMIOLOGY.
3. Martin N, Benard D M and Babu L 2020 Modelling of COVID-19 (Coronavirus) in Kenya Using SIR Model. MATHEMATICS TIRE.
4. Ranjan R 2020 Estimating The Final Epidemic Size For COVID-19 Outbreak Using Improved Epidemiological Models. MED.EPIDEMIOLOGY.
5. Dmitry A et al, 2020 Prediction of The COVID-19 Spread in Russia Based on SIR and SEIR Models of Epidemics. IFAC-PAPERSONLINE.
6. Qin Y, Wu B B and Yang W H 2021 Modeling and Analysis of Environment-aware COVID-19 Transmission. APPLICATION RESEARCH OF COMPUTERS.
7. Pedro F 2021 Epidemiology SIR with Regression, Arima, and Prophet in Forecasting COVID-19. ENGINEERING PROCEEDINGS.
8. Different Communities. CHAOS, SOLITONS, AND FRACTALS.
9. Lgor N 2021 Visible and Real Sizes of New COVID-19 Pandemic Waves in Ukraine. CHAOS, SOLITONS, AND FRACTALS.
10. Liu M, Raphael T and Yao S 2020 Forecasting The Spread Of COVID-19 Under Different Reopening strategies. SCIENTIFIC REPOETS.
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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- Volume Title
- Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
- ISBN (Print)
- 978-1-83558-129-2
- ISBN (Online)
- 978-1-83558-130-8
- Published Date
- 13 November 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/9/20240709
- Copyright
- 13 November 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited