A Dynamic Model for Predicting the Impact of Vaccine Avoidance and Health Protocols on the Covid-19 Outbreak

Journal of Systems Thinking in Practice

دوره 2، شماره 2 - شماره پیاپی 5، شهریور 2023، صفحه 78-92   

نوع مقاله: Research Article

شناسه دیجیتال (DOI): 10.22067/jstinp.2023.81259.1039

نویسندگان

Hamed Jabari^{* 1}؛ Hamid Shabandarzadeh²؛ Ahmad Ghorbanpour²

¹Mathematics Secretary of Education, Bushehr, Iran.

²Department of Industrial Management, Faculty of Business and Economics, Persian Gulf University, Bushehr, Iran.

چکیده

The COVID-19 pandemic is one of the most critical issues in the world today. Although many countries have been able to control the infection, much research is still needed to uncover the complex dynamics of virus transmission. This study aimed to utilize a mathematical model for analyzing epidemiological data of infectious diseases, aiming to comprehend their behavior, predict future trends, and investigate the influence of external factors on key indicators. This model extends the Susceptible-Exposed-Infected-Recovered (SEIR) framework by incorporating additional populations, such as vaccinated individuals, asymptomatic cases, and hospitalized patients. It also developed dynamics related to vaccination avoidance behavior and adherence to health protocols.  It is coded in MATLAB 2018-b software and is executed for 360 days. The results of the simulation showed that it is not possible to achieve the desired level of immunity from vaccine injection without following health protocols. On the other hand, considering the level of infection, increasing the participation rate in receiving vaccines and reducing the population of vaccines can control the epidemic. Therefore, a change in social behavior and an increase in the amount of vaccination can increase the awareness of society in reducing the avoidance of vaccination and improving compliance with health protocols.

کلیدواژه‌ها

A dynamic system؛ SEIR model؛ COVID-19 pandemic؛ Vaccination avoidance

مراجع

Amouch, M. and Karim, N., 2021. Modeling the dynamic of COVID-19 with different types of transmissions. Chaos, solitons & fractals, 150, p.111188. https://doi.org/10.1016/j.chaos.2021.111188.

Asgary, A., Najafabadi, M.M., Karsseboom, R. and Wu, J., 2020, November. A drive-through simulation tool for mass vaccination during COVID-19 pandemic. In Healthcare (Vol. 8, No. 4, p. 469). MDPI. https://doi.org/10.3390/healthcare8040469.

Barmparis, G.D. and Tsironis, G.P., 2020. Estimating the infection horizon of COVID-19 in eight countries with a data-driven approach. Chaos, Solitons & Fractals, 135, p.109842. https://doi.org/10.1016/j.chaos.2020.109842.

Basiri, N. and Koushki, M., 2021. Study of Vaccine Production Abroad and Scientific and Research Challenges of COVID-19 Vaccine Production in Iran. Annals of the Romanian Society for Cell Biology, pp.17249-17256. URL: https://annalsofrscb.ro/index.php/journal/article/view/7528.

Boudaoui, A., El hadj Moussa, Y., Hammouch, Z. and Ullah, S., 2021. A fractional-order model describing the dynamics of the novel coronavirus (COVID-19) with nonsingular kernel. Chaos, Solitons & Fractals, 146, p.110859. https://doi.org/10.1016/j.chaos.2021.110859.

Chadwick, A., Kaiser, J., Vaccari, C., Freeman, D., Lambe, S., Loe, B.S., Vanderslott, S., Lewandowsky, S., Conroy, M., Ross, A.R. and Innocenti, S., 2021. Online social endorsement and Covid-19 vaccine hesitancy in the United Kingdom. Social media+ society, 7(2), pp.1-17. https://doi.org/10.1177/20563051211008817.

Chou, W.Y.S. and Budenz, A., 2020. Considering emotion in COVID-19 vaccine communication: addressing vaccine hesitancy and fostering vaccine confidence. Health communication, 35(14), pp.1718-1722. https://doi.org/10.1080/10410236.2020.1838096.

Chung, N.N. and Chew, L.Y., 2021. Modelling Singapore COVID-19 pandemic with a SEIR multiplex network model. Scientific Reports, 11(1), p.10122. https://doi.org/10.1038/s41598-021-89515-7.

Cui, Q., Hu, Z., Li, Y., Han, J., Teng, Z. and Qian, J., 2020. Dynamic variations of the COVID-19 disease at different quarantine strategies in Wuhan and mainland China. Journal of infection and public health, 13(6), pp.849-855. https://doi.org/10.1016/j.jiph.2020.05.014.

Díaz, F. and Henríquez, P.A., 2021. Social sentiment segregation: Evidence from Twitter and Google Trends in Chile during the COVID-19 dynamic quarantine strategy. PloS one, 16(7), p.e0254638. https://doi.org/10.1371/journal.pone.0254638.

Dror, A.A., Eisenbach, N., Taiber, S., Morozov, N.G., Mizrachi, M., Zigron, A., Srouji, S. and Sela, E., 2020. Vaccine hesitancy: the next challenge in the fight against COVID-19. European journal of epidemiology, 35, pp.775-779. https://doi.org/ 10.1007/s10654-020-00671-y.

Eurofound, 2023. Https://www.eurofound.europa.eu.

Fanelli, D. and Piazza, F., 2020. Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos, Solitons & Fractals, 134, p.109761. https://doi.org/10.1016/j.chaos.2020.109761.

Foy, B.H., Wahl, B., Mehta, K., Shet, A., Menon, G.I. and Britto, C., 2021. Comparing COVID-19 vaccine allocation strategies in India: A mathematical modelling study. International Journal of Infectious Diseases, 103, pp.431-438. https://doi.org/10.1016/j.ijid.2020.12.075.

Jabari, H., 2022. Simulation data mining approach to predict Covid-19 epidemic behavior and hospital inventory management. Unpublished thesis, Persian Gulf University Bushehr.

Khankeh, H.R., Farrokhi, M., Khanjani, M.S., Momtaz, Y.A., Forouzan, A.S., Norouzi, M., Ahmadi, S., Harouni, G.G., Roudini, J., Ghanaatpisheh, E. and Hamedanchi, A., 2021. The barriers, challenges, and strategies of COVID-19 (SARS-CoV-2) vaccine acceptance: a concurrent mixed-method study in Tehran City, Iran. Vaccines, 9(11), p.1248. https://doi.org/10.3390/vaccines9111248.

Kristjanpoller, W., Michell, K. and Minutolo, M.C., 2021. A causal framework to determine the effectiveness of dynamic quarantine policy to mitigate COVID-19. Applied Soft Computing, 104, p.107241. https://doi.org/10.1016/j.asoc.2021.107241.

Li, Y., Ge, L., Zhou, Y., Cao, X. and Zheng, J., 2021a. Toward the impact of non-pharmaceutical interventions and vaccination on the COVID-19 pandemic with time-dependent SEIR model. Frontiers in Artificial Intelligence, 4, p.648579. https://doi.org/10.3389/frai.2021.648579.

Li, Y., Zhang, X. and Cao, H., 2021b. Large time behavior in a diffusive SEIR epidemic model with general incidence. Applied Mathematics Letters, 120, p.107322. https://doi.org/10.1016/j.aml.2021.107322.

Libotte, G.B., Lobato, F.S., Platt, G.M. and Neto, A.J.S., 2020. Determination of an optimal control strategy for vaccine administration in COVID-19 pandemic treatment. Computer methods and programs in biomedicine, 196, p.105664. https://doi.org/10.1016/j.cmpb.2020.105664.

Loomba, S., de Figueiredo, A., Piatek, S.J., de Graaf, K. and Larson, H.J., 2021. Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA. Nature human behaviour, 5(3), pp.337-348. https://doi.org/10.1038.

Makhoul, M., Chemaitelly, H., Ayoub, H.H., Seedat, S. and Abu-Raddad, L.J., 2021. Epidemiological differences in the impact of COVID-19 vaccination in the United States and China. Vaccines, 9(3), p.223. https://doi.org/10.3390/vaccines9030223.

Mallapaty, S., 2021. China’s COVID vaccines are going global—but questions remain. Nature, 593(7858), pp.178-179. https://doi.org/10.1038/d41586-021-01146-0.

Mallapaty, S. and Callaway, E., 2021. What scientists do and don’t know about the Oxford-AstraZeneca COVID vaccine. Nature, 592(7852), pp.15-17. https://doi.org/10.1038/d41586-021-00785-7.

Partohaghighi, M. and Akgül, A., 2021. Modelling and simulations of the SEIR and Blood Coagulation systems using Atangana-Baleanu-Caputo derivative. Chaos, Solitons & Fractals, 150, p.111135. https://doi.org/10.1016/j.chaos.2021.111135.

Poonia, R.C., Saudagar, A.K.J., Altameem, A., Alkhathami, M., Khan, M.B. and Hasanat, M.H.A., 2022. An Enhanced SEIR Model for Prediction of COVID-19 with Vaccination Effect. Life, 12(5), p.647. https://doi.org/10.3390/life12050647.

Ricac. AC., 2023. https://www.ricac.ac.ir

Sabteahval, 2023.  Https://www.sabteahval.ir.

Saporta-Keating, S. and Nyquist, A.C., 2021. State of the union on COVID-19. Contemporary PEDS Journal, 38(01).

Shakhany, M.Q. and Salimifard, K., 2021. Predicting the dynamical behavior of COVID-19 epidemic and the effect of control strategies. Chaos, Solitons & Fractals, 146, p.110823. https://doi.org/10.1016/j.chaos.2021.110823.

Usherwood, T., LaJoie, Z. and Srivastava, V., 2021. A model and predictions for COVID-19 considering population behavior and vaccination. Scientific reports, 11(1), pp.1-11. https://doi.org/10.1038/s41598-021-91514-7.

Waheed, S., Bayas, A., Hindi, F., Rizvi, Z. and Espinosa, P.S., 2021. Neurological complications of COVID-19: Guillain-Barre syndrome following Pfizer COVID-19 vaccine. Cureus, 13(2). doi.org/10.7759/cureus.13426.

Zhonghua, L., Xing, B. and Zhi, Z., 2019. Novel coronavirus pneumonia emergency response epidemiology team. The epidemiological characteristics of an outbreak of, 41, pp.145-151.

Zhu, C.C. and Zhu, J., 2021. Dynamic analysis of a delayed COVID-19 epidemic with home quarantine in temporal-spatial heterogeneous via global exponential attractor method. Chaos, Solitons & Fractals, 143, p.110546. https://doi.org/10.1016/j.chaos.2020.110546.