[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
Browse::
Journal Info::
Guide for Authors::
Submit Manuscript::
Articles archive::
For Reviewers::
Contact us::
Site Facilities::
Reviewers::
::
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
:: Volume 12, Issue 2 (1-2023) ::
JGST 2023, 12(2): 206-221 Back to browse issues page
Spatio-temporal agent based simulation of COVID-19 disease and investigating the effect of vaccination (case study: Urmia)
Amir Hossein Ebrahimi * , Ali Asghar Alesheikh , Navid Hooshangi
Abstract:   (1005 Views)
Proper management of epidemic diseases such as Covid-19 is very important because of its effects on the economy, culture and society of nations. By applying various control strategies such as closing schools, restricting night traffic and mass vaccination program, the spread of this disease has been somewhat controlled but not completely stopped. The main goal of this research is to provide a flexible spatio-temporal model for simulating the spread of the Covid-19 disease in order to investigate and evaluate the effectiveness of vaccination. For this purpose, the combination of Agent Based Modelling (ABM) with changeable input parameters and Geospatial Information System (GIS) has been used. The disease spreads through the interaction of the designed agents with each other and with the environment, with the help of the SEIRD epidemic model, and the characteristics of the agents are monitored during the simulation period. To evaluate the model, the real data of patients with the disease in Urmia city from the time of the outbreak to 140 days later were used. The results show that the implemented model simulates the spread of the disease with MAPE= 32.86% and NRMSE= 8.62%. By simulating the vaccination implementation plan, the total number of infected people will decrease by 36.12% and the total number of deaths will decrease by 44.48%. Comparison of simulation outputs and real data shows a similarity of 82% between model results and reality. The result of this research shows that agent based modelling has been able to simulate the spread of the corona virus to an acceptable extent and evaluate the control strategies effectively; Therefore, agent based models can be used to simulate the spread of different variants of the Corona virus and other epidemic diseases, as well as to simulate the environment's response and control strategies.
 
Article number: 15
Keywords: Agent Based Modelling, Epidemic Diseases, Geospatial Information System, COVID-19, Vaccination
Full-Text [PDF 1031 kb]   (682 Downloads)    
Type of Study: Research | Subject: GIS
Received: 2022/11/29
References
1. F. Araya, "Modeling the spread of COVID-19 on construction workers: An agent-based approach," Safety Science, vol. 133, p. 105022, 2021. [DOI:10.1016/j.ssci.2020.105022]
2. S. L. Chang, N. Harding, C. Zachreson, O. M. Cliff and M. Prokopenko, "Modelling transmission and control of the COVID-19 pandemic in Australia," Nature Communications, vol. 11, p. 5710, 2020. [DOI:10.1038/s41467-020-19393-6]
3. W. Organization, "Coronavirus (COVID-19) Dashboard," 16 September 2022. [Online]. Available: https://covid19.who.int/.
4. N. M. Gharakhanlou and N. Hooshangi, "Spatio-temporal simulation of the novel coronavirus (COVID-19) outbreak using the agent-based modeling approach (case study: Urmia, Iran)," Informatics in Medicine Unlocked, vol. 20, p. 100403, 2020. [DOI:10.1016/j.imu.2020.100403]
5. A. Rodríguez, E. Cuevas, D. Zaldivar, B. Morales-Castañeda, R. Sarkar and E. H. Houssein, "An agent-based transmission model of COVID-19 for re-opening policy design," Computers in Biology and Medicine, vol. 148, p. 105847, 2022. [DOI:10.1016/j.compbiomed.2022.105847]
6. C. J. L. Murray and P. Piot, "The Potential Future of the COVID-19 Pandemic: Will SARS-CoV-2 Become a Recurrent Seasonal Infection?," JAMA, vol. 325, pp. 1249-1250, April 2021. [DOI:10.1001/jama.2021.2828]
7. A. C. Morrison, C. Ferro, R. Pardo, M. Torres, B. Devlin, M. L. Wilson and R. B. Tesh, "Seasonal abundance of Lutzomyia longipalpis (Diptera: Psychodidae) at an endemic focus of visceral leishmaniasis in Colombia.," Journal of medical entomology, vol. 32, no. 4, pp. 538-48, July 1995. [DOI:10.1093/jmedent/32.4.538]
8. A. Mollalo, A. Alimohammadi, M. R. Shirzadi and M. R. Malek, "Geographic information system-based analysis of the spatial and spatio-temporal distribution of zoonotic cutaneous leishmaniasis in Golestan Province, north-east of Iran.," Zoonoses and public health, vol. 62, no. 1, pp. 18-28, February 2015. [DOI:10.1111/zph.12109]
9. R. A. J. Williams and A. T. Peterson, "Ecology and geography of avian influenza (HPAI H5N1) transmission in the Middle East and northeastern Africa," International Journal of Health Geographics, vol. 8, p. 47, 2009. [DOI:10.1186/1476-072X-8-47]
10. J. Jafari, A. Jiryaee and M. S. Mesgari, "Modeling the Spread of Infectious Diseases Malaria," Journal of Geomatics Science and Technology, vol. 11, 2021.
11. M. Tabasi and A. A. Alesheikh, "Development of an Agent-Based Model for Simulation of the Spatiotemporal Spread of Leishmaniasis in GIS (Case Study: Maraveh Tappeh)," Journal of Geomatics Science and Technology, vol. 8, 2019.
12. E. Cuevas, "An agent-based model to evaluate the COVID-19 transmission risks in facilities," Computers in Biology and Medicine, vol. 121, p. 103827, 2020. [DOI:10.1016/j.compbiomed.2020.103827]
13. W. O. Kermack and A. G. McKendrick, "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, vol. 115, p. 700-721, 1927. [DOI:10.1098/rspa.1927.0118]
14. N. T. J. Bailey and others, The mathematical theory of infectious diseases and its applications, Charles Griffin & Company Ltd, 5a Crendon Street, High Wycombe, Bucks HP13 6LE., 1975.
15. J. Arino, C. C. McCluskey and P. van den Driessche, "Global Results for an Epidemic Model with Vaccination that Exhibits Backward Bifurcation," SIAM Journal on Applied Mathematics, vol. 64, pp. 260-276, 2003. [DOI:10.1137/S0036139902413829]
16. X.-B. Zhang, H.-F. Huo, H. Xiang and X.-Y. Meng, "Dynamics of the deterministic and stochastic SIQS epidemic model with non-linear incidence," Applied Mathematics and Computation, vol. 243, pp. 546-558, 2014. [DOI:10.1016/j.amc.2014.05.136]
17. N. M. Gharakhanlou, M. S. Mesgari and N. Hooshangi, "Developing an agent-based model for simulating the dynamic spread of Plasmodium vivax malaria: A case study of Sarbaz, Iran," Ecological Informatics, vol. 54, p. 101006, 2019. [DOI:10.1016/j.ecoinf.2019.101006]
18. M. R. Azarmehr, M. S. Mesgari and M. Karimi, "Spatio - temporal modelling of malaria disease by geo-spatial information system (GIS) and cellular automata (CA)," Infectious diseases and tropical medicine, vol. 15, no. 48, pp. 61-, 2010.
19. S. H. White, A. M. del Rey and G. R. Sánchez, "Modeling epidemics using cellular automata," Applied Mathematics and Computation, vol. 186, pp. 193-202, 2007. [DOI:10.1016/j.amc.2006.06.126]
20. M. Rajabi, P. Pilesjö, M. R. Shirzadi, R. Fadaei and A. Mansourian, "A spatially explicit agent-based modeling approach for the spread of Cutaneous Leishmaniasis disease in central Iran, Isfahan," Environmental Modelling & Software, vol. 82, pp. 330-346, 2016. [DOI:10.1016/j.envsoft.2016.04.006]
21. M. Tabasi and A. A. Alesheikh, "A Review of the Applications of Agent Based Simulation in Epidemic Diseases (Case study: Cutaneous Leishmaniasis)," Geospatial Engineering Journal, vol. 8, 2017.
22. M. Tabasi and A. A. Alesheikh, "Modeling Spatial Spread of Epidemic Diseases using Agent-based Simulation (Case Study: Seasonal Influenza)," Journal of Geomatics Science and Technology, vol. 6, 2017.
23. P. C. L. Silva, P. V. C. Batista, H. S. Lima, M. A. Alves, F. G. Guimarães and R. C. P. Silva, "COVID-ABS: An agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions," Chaos, Solitons & Fractals, vol. 139, p. 110088, 2020. [DOI:10.1016/j.chaos.2020.110088]
24. A. Bouchnita and A. Jebrane, "A hybrid multi-scale model of COVID-19 transmission dynamics to assess the potential of non-pharmaceutical interventions.," Chaos, solitons, and fractals, vol. 138, p. 109941, September 2020. [DOI:10.1016/j.chaos.2020.109941]
25. R. German, A. Djanatliev, L. Maile, P. Bazan and H. Hackstein, "Modeling Exit Strategies from COVID-19 Lockdown with a Focus on Antibody Tests," medRxiv, 2020. [DOI:10.1101/2020.04.14.20063750]
26. W. Mckibbin and R. Fernando, "The Global Macroeconomic Impacts of COVID-19: Seven Scenarios," Asian Economic Papers, vol. 20, p. 1-55, August 2020. [DOI:10.1162/asep_a_00796]
27. F. Araya, "Modeling working shifts in construction projects using an agent-based approach to minimize the spread of COVID-19," Journal of Building Engineering, vol. 41, p. 102413, 2021. [DOI:10.1016/j.jobe.2021.102413]
28. A. Takian, A. Raoofi and S. Kazempour-Ardebili, COVID-19 battle during the toughest sanctions against Iran., vol. 395, 2020, pp. 1035-1036. [DOI:10.1016/S0140-6736(20)30668-1]
29. U. Wilensky and W. Rand, An introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo, Mit Press, 2015.
30. S. Abar, G. K. Theodoropoulos, P. Lemarinier and G. M. P. O'Hare, "Agent Based Modelling and Simulation tools: A review of the state-of-art software," Computer Science Review, vol. 24, pp. 13-33, 2017. [DOI:10.1016/j.cosrev.2017.03.001]
31. I. Korolev, "Identification and estimation of the SEIRD epidemic model for COVID-19.," Journal of econometrics, vol. 220, no. 1, pp. 63-85, January 2021. [DOI:10.1016/j.jeconom.2020.07.038]
32. W. H. O. (WHO), "Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19)," 2019. [Online]. Available: https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf.
33. S. Zhang, M. Diao, W. Yu, L. Pei, Z. Lin and D. Chen, "Estimation of the reproductive number of novel coronavirus (COVID-19) and the probable outbreak size on the Diamond Princess cruise ship: A data-driven analysis.," International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases, vol. 93, pp. 201-204, April 2020. [DOI:10.1016/j.ijid.2020.02.033]
34. "Statistical Center of Iran," 2017. [Online]. Available: https://www.amar.org.ir/.
35. "Urmia University of Medical Sciences," [Online]. Available: https://webda.umsu.ac.ir/.
36. W. Organization, "General's opening remarks at the media briefing on COVID-19," 11 March 2020. [Online]. Available: https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020.
Send email to the article author

Add your comments about this article
Your username or Email:

CAPTCHA



XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Ebrahimi A H, Alesheikh A A, Hooshangi N. Spatio-temporal agent based simulation of COVID-19 disease and investigating the effect of vaccination (case study: Urmia). JGST 2023; 12 (2) : 15
URL: http://jgst.issgeac.ir/article-1-1126-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 12, Issue 2 (1-2023) Back to browse issues page
نشریه علمی علوم و فنون نقشه برداری Journal of Geomatics Science and Technology