Abstract
The mathematical models can help to characterize, quantify, summarize, and determine the severity of the outbreak of the Coronavirus, the estimation of the dynamics of the pandemic helps to identify the type of measures and interventions that can be taken to minimize the impact by classified information. In this work, we propose four epidemiological models to study the spread of SARS-CoV-2. Specifically, two versions of the SIR model (Susceptible, Infectious, and Recovered) are considered, the classical Crank-Nicolson method is used with a stochastic version of the Beta-Dirichlet state-space models. Subsequently, the SEIR model (Susceptible, Exposed, Infectious, and Recovered) is fitted, the Euler method and a stochastic version of the Beta-Dirichlet state-space model are used. In the results of this study (Portoviejo-Ecuador), the SIR model with the Beta-Dirichlet state-space form determines the maximum point of infection in less time than the SIR model with the Crank-Nicolson method. Furthermore, the maximum point of infection is shown by the SEIR model, that is reached during the first two weeks where the virus begins to spread, more efficient is shown by this model. To measure the quality of the estimation of the algorithms, we use three measures of goodness of fit. The estimated errors are negligible for the analyzed data. Finally, the evolution of the spread is predicted, that can be helpful to prevent the capacity of the country’s health system.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Allen, L.: A primer on stochastic epidemic models: formulation numerical simulation and analysis. Infect. Dis. Model. 2(2), 128–142 (2017)
Anderson, R., May, R.: Infectious Diseases of Humans; Dynamic and Control. Oxford University Press, Oxford (1991)
Andersson, H., Britton, T.: Stochastic Epidemic Models and their Statistical Analysis. Springer Lecture Notes in Statistics, Springer, New York (2000). https://doi.org/10.1007/978-1-4612-1158-7
Bailey, N.: The mathematical theory of infectious diseases and its applications. London, Griffin (1975)
Bartlett, M.: Some evolutionary stochastic processes. J. R. Stat. Soc. Ser. B 11(2), 211–229 (1949)
Britton, T.: Stochastic Epidemic Models with Inference. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-030-30900-8
Daley, D., Gani, J.: Epidemic Modelling: An Introduction. Cambridge University Press, Cambridge (1999)
Fuchs, C.: Inference for Diffusion Processes with Applications in Life Sciences. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-25969-2
Geman, S., Geman, D.: Stochastic relaxation Gibbs distributions and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6, 721–741 (1984)
Infante, S., Sánchez, L., Hernández, A.: Stochastic models to estimate population dynamics. Stat. Optim. Inf. Comput. 7, 311–328 (2019)
Kermack, W., McKendrick, A.: A contribution to the mathematical theory of epidemics. Proc. R. Soc. Lond. 115, 700–721 (1927)
Lux, T.: Inference for systems of stochastic differential equations from discretely sampled data: A numerical maximum likelihood approach. Kiel Working Paper. Kiel Institute for the World Economy (IfW) (1781) (2012)
McKendrick, A.: Applications of mathematics to medical problems. Proc. Edinb. Math. Soc. 13, 98–130 (1926)
Ndanguza, D., Mbalawata, I., Nsabimana, J.: Analysis of SDEs applied to SEIR epidemic models by extended Kalman filter method. Appl. Math. 7, 2195–2211 (2016)
Osthus, D., Hickmann, K., Caragea, P., Higdon, D., Valle, S.D.: Forecasting seasonal influenza with a state-space sir model. Ann. Appl. Stat. 11(1), 202–224 (2017)
Poulsen, R.: Approximate maximum likelihood estimation of discretely observed diffusion processes. Manuscript. University of Aarhus (1999)
Strikwerda, J.: Finite Difference Schemes and Partial Differential Equations, 2nd edn. Society for Industrial and Applied Mathematics, Philadelphia (2004)
Tang, B., et al.: Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions. J. Clin. Med. 9(462) (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix A: Supporting Information
Appendix A: Supporting Information
1.1 Figures
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Luis, S., Saba, I. (2022). Epidemic Models and Estimation of the Spread of SARS-CoV-2: Case Study Portoviejo-Ecuador. In: Narváez, F.R., Proaño, J., Morillo, P., Vallejo, D., González Montoya, D., Díaz, G.M. (eds) Smart Technologies, Systems and Applications. SmartTech-IC 2021. Communications in Computer and Information Science, vol 1532. Springer, Cham. https://doi.org/10.1007/978-3-030-99170-8_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-99170-8_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-99169-2
Online ISBN: 978-3-030-99170-8
eBook Packages: Computer ScienceComputer Science (R0)