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Journal of Data Science


Incorporating Interventions to an Extended SEIRD Model with Vaccination: Application to COVID-19 in Qatar
Volume 22, Issue 1 (2024), pp. 97–115
Pub. online: 13 June 2023      Type: Data Science In Action      Open accessOpen Access
Received
28 October 2022
Accepted
14 May 2023
Published
13 June 2023

Abstract

The COVID-19 outbreak of 2020 has required many governments to develop and adopt mathematical-statistical models of the pandemic for policy and planning purposes. To this end, this work provides a tutorial on building a compartmental model using Susceptible, Exposed, Infected, Recovered, Deaths and Vaccinated (SEIRDV) status through time. The proposed model uses interventions to quantify the impact of various government attempts made to slow the spread of the virus. Furthermore, a vaccination parameter is also incorporated in the model, which is inactive until the time the vaccine is deployed. A Bayesian framework is utilized to perform both parameter estimation and prediction. Predictions are made to determine when the peak Active Infections occur. We provide inferential frameworks for assessing the effects of government interventions on the dynamic progression of the pandemic, including the impact of vaccination. The proposed model also allows for quantification of number of excess deaths averted over the study period due to vaccination.

Supplementary material

 Supplementary Material
The supplementary material contains the functional form of the posterior distribution of the model parameters, a discussion on the behavior of R e ( t ) over time, and some trace plots validating convergence of model parameters. The dataset and code used for this project can be found at https://github.com/elizabethamona/SEIRDV-model.

References

 
Albert J (2009). An introduction to R. In: Bayesian Computation with R, 1–17. Springer.
 
Antonelli E, Piccolomini EL, Zama F (2022). Switched forced SEIRDV compartmental models to monitor COVID-19 spread and immunization in Italy. Infectious Disease Modelling, 7(1): 1–15. https://doi.org/10.1016/j.idm.2021.11.001
 
Bayes T (1763). LII. An essay towards solving a problem in the doctrine of chances. by the late Rev. Mr. Bayes, FRS communicated by Mr. Price, in a letter to John Canton, AMFR S. Philosophical Transactions of the Royal Society of London, (53): 370–418.
 
Berger JO (1985). Prior information and subjective probability. In: Statistical Decision Theory and Bayesian Analysis, 74–117. Springer.
 
Bertsimas D, Ivanhoe J, Jacquillat A, Li M, Previero A, Lami OS, et al. (2020). Optimizing vaccine allocation to combat the COVID-19 pandemic. medRxiv.
 
Boone EL, Abdel-Salam ASG, Sahoo I, Ghanam R, Chen X, Hanif A (2023). Monitoring SEIRD model parameters using MEWMA for the COVID-19 pandemic with application to the state of Qatar. Journal of Applied Statistics, 50(2): 231–246. https://doi.org/10.1080/02664763.2021.1985091
 
Casella G, Berger RL (2021). Statistical Inference. Cengage Learning.
 
Chinazzi M, Davis JT, Ajelli M, Gioannini C, Litvinova M, Merler S, et al. (2020). The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science, 368(6489): 395–400. https://doi.org/10.1126/science.aba9757
 
Dirac P (1958). General Principles of Quantum Mechanics. International Series of Monographs on Physics. Oxford, Clarendon Press, 4th edition.
 
Gelman A, Carlin JB, Stern HS, Rubin DB (1995). Bayesian Data Analysis. Chapman and Hall/CRC.
 
Ghanam R, Boone EL, Abdel-Salam ASG (2021). SEIRD model for Qatar COVID-19 outbreak: A case study. Letters in Biomathematics, 8(1). https://doi.org/10.30707/LiB8.1.1647878866.012596
 
Ghostine R, Gharamti M, Hassrouny S, Hoteit I (2021). An extended SEIR model with vaccination for forecasting the COVID-19 pandemic in Saudi Arabia using an ensemble Kalman filter. Mathematics, 9(6): 636. https://doi.org/10.3390/math9060636
 
Gilks WR, Richardson S, Spiegelhalter D (1995). Markov Chain Monte Carlo in Practice. CRC press.
 
Giuliani D, Dickson MM, Espa G, Santi F (2020). Modelling and predicting the spatio-temporal spread of COVID-19 in Italy. BMC Infectious Diseases, 20(1): 1–10. https://doi.org/10.1186/s12879-019-4717-5
 
Kermack WO, McKendrick AG (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 115(772): 700–721.
 
Koufi AE, Bennar A, Yousfi N (2020). Dynamics of a stochastic SIRS epidemic model with regime switching and specific functional response. Discrete Dynamics in Nature and Society. https://doi.org/10.1155/2020/5898456
 
Luo W, Tay WP (2013). Finding an infection source under the SIS model. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2930–2934. IEEE.
 
Mathieu E, Roser M (2021). How do death rates from COVID-19 differ between people who are vaccinated and those who are not. Our World in Data. Recuperado de: https://ourworldindata.org/covid-deaths-by-vaccination.
 
Mercer GN, Glass K, Becker NG (2011). Effective reproduction numbers are commonly overestimated early in a disease outbreak. Statistics in Medicine, 30(9): 984–994. https://doi.org/10.1002/sim.4174
 
Miller M (2020). 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository: Johns Hopkins University Center for Systems Science and Engineering. Bulletin-Association of Canadian Map Libraries and Archives (ACMLA), (164): 47–51. https://doi.org/10.15353/acmla.n164.1730
 
Nakamura GM, Martinez AS (2019). Hamiltonian dynamics of the SIS epidemic model with stochastic fluctuations. Scientific Reports, 9(1): 1–9. https://doi.org/10.1038/s41598-018-37186-2
 
Poonia RC, Saudagar AKJ, Altameem A, Alkhathami M, Khan MB, Hasanat MHA (2022). An Enhanced SEIR Model for Prediction of COVID-19 with Vaccination Effect. Life, 12(5): 647. https://doi.org/10.3390/life12050647
 
Rey D, Hammad AW, Saberi M (2021). Vaccine allocation policy optimization and budget sharing mechanism using Thompson sampling. arXiv preprint: https://arxiv.org/abs/2109.10004.
 
Rezabakhsh A, Ala A, Khodaei SH (2020). Novel coronavirus (COVID-19): a new emerging pandemic threat. Journal of Research in Clinical Medicine, 8(1): 5. https://doi.org/10.34172/jrcm.2020.005
 
Ridenhour B, Kowalik JM, Shay DK (2014). Unraveling R0: Considerations for public health applications. American Journal of Public Health, 104(2): e32–e41. https://doi.org/10.2105/AJPH.2013.301704
 
Van den Driessche P, Watmough J (2002). Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Mathematical Biosciences, 180(1–2): 29–48. https://doi.org/10.1016/S0025-5564(02)00108-6
 
Wackerly D, Mendenhall W, Scheaffer RL (2014). Mathematical Statistics with Applications. Cengage Learning.
 
Wintachai P, Prathom K (2021). Stability analysis of SEIR model related to efficiency of vaccines for COVID-19 situation. Heliyon, 7(4): e06812. https://doi.org/10.1016/j.heliyon.2021.e06812
 
Wu F, Zhao S, Yu B, Chen YM, Wang W, Song ZG, et al. (2020). A new coronavirus associated with human respiratory disease in China. Nature, 579(7798): 265–269. https://doi.org/10.1038/s41586-020-2008-3

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2024 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
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Keywords
Bayesian statistics compartmental model epidemiology intervention analysis reproduction number

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