A Simulation Model to Estimate the Impact of Self-protection and Social Distancing on the Spread of COVID-19 in the Public Transportation (Case Study: Tehran Metro)

Document Type : Research Paper

Authors

1 Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran

2 Industrial Engineering Department, K. N. Toosi University of Technology, Tehran, Iran

3 Industrial Engineering department, K.N. Toosi University of Technology, Tehran, Iran.

4 Human Science Department, Islamic Azad University, Tehran North Branch, Tehran, Iran

10.22070/jqepo.2022.15670.1220

Abstract

Metro is one of the most important urban transportation systems in Tehran. Before the pandemic, almost two million trips were made by metro daily. A crowded metro is one of the most important sources of the outbreak. In this paper, a discrete event model is introduced to simulate the spread of the COVID-19 in Tehran metro. Three types of passengers are defined: Healthy passengers and infected passengers with acute conditions and not acute conditions. Two important ways of preventing virus transmission are self-protection (e.g., wearing a mask) and social distancing. Different scenarios of social distancing and self-protection are surveyed based on multiple replications of the proposed model. Results of the simulation showed that noncompliance with social distancing has an exponentially negative effect on the number of infected passengers. In addition, the compliance of infected passengers with the social distancing and self-protection tips is almost twice important as the compliance of healthy passengers. 

Keywords

References

Azimi, S. S., Koohi, F., Aghaali, M., et al. (2020). Estimation of the basic reproduction number (𝑅0) of the COVID-19 epidemic in Iran. Medical Journal of the Islamic Republic of Iran, 34, 95.
Agrawal, A., & Bhardwaj, R. (2021). Probability of COVID-19 infection by cough of a normal person and a super-spreader. Physics of fluids, 33(3), 031704.
Badham J., Chattoe-Brown E., Gilbert N., et al. (2018) Developing agent-based models of complex health behaviour, Health & Place, 54, 170-177,
Banks J., Carson J.S., Nelson B.L., Nicol D.M. (2010), Discrete-Event System Simulation, Fifth Edition, Pearson.
Currie, C. S. M., Fowler, J. W., Kotiadis, K., et al. (2020). How simulation modelling can help reduce the impact of COVID-19. Journal of Simulation, 14:2, 1–15.
De Vos J. (2020), The effect of COVID-19 and subsequent social distancing on travel behavior, Transportation Research Interdisciplinary Perspectives, Volume 5, 100121.
Dzisi E.K.J., & Dei O.A. (2020) Adherence to social distancing and wearing of masks within public transportation during the COVID 19 pandemic, Transportation Research Interdisciplinary Perspectives, Volume 7, 100191
Ferguson N. M., Laydon, D., Nedjati-Gilani, G., et al. (2020). Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand. https://doi.org/10.25561/77482
Gameiro Da, & S. M., (2020). An Analysis of the Transmission Modes of COVID-19 in Light of the Concepts of Indoor Air Quality. DOI:10.13140/RG.2.2.28663.78240.
Gupta J.K., Lin C.H., Chen Q. (2012) Risk assessment of airborne infectious diseases in aircraft cabins, Indoor Air, 22 (5), pp. 388-395, 10.1111/j.1600-0668.2012.00773.x
Haghbayan M., Khatami S.S., Nasrollahi Heravi F. (2021) The Estimation Of Newly Infected Cases Of Covid-19 With Consideration Of Governmental Action And Behavior Of People In Iran, SRPH JOURNAL OF MEDICAL SCIENCES AND HEALTHCARE MANAGEMENT, 3(1), pp. 1-7.
Hu M., Lin H., Wang J.⸴ et al. (2020), Risk of Coronavirus Disease 2019 Transmission in Train Passengers: an Epidemiological and Modeling Study, Clinical Infectious Diseases, https://doi.org/10.1093/cid/ciaa1057.
Jalayer, M., Orsenigo, C. & Vercellis, C. (2020) CoV-ABM: A stochastic discrete-event agent-based framework to simulate spatiotemporal dynamics of COVID-19. Preprint at URL https://arxiv.org/abs/2007.13231.
Kang, B. G., Park, H. M., Jang, M., & Seo, K. M. (2021). Hybrid Model-Based Simulation Analysis on the Effects of Social Distancing Policy of the COVID-19 Epidemic. International Journal of Environmental Research and Public Health, 18(21), 11264.
Katrakazas C., Michelaraki E., Sekadakis M., Yannis G. (2020), A descriptive analysis of the effect of the COVID-19 pandemic on driving behavior and road safety, Transportation Research Interdisciplinary Perspectives, Volume 7,100186.
Lotfi M., Hamblin M.R., Rezaei N. (2020) COVID-19: Transmission, prevention, and potential therapeutic opportunities, Clinica Chimica Acta, 508, 154-266.
Manout O., El-Megzari I., Ciari F. (2021) Modeling the COVID-19 Pandemic: A Sensitivity Analysis on Input Data Using Agent-Based Transportation Simulation. Transportation Research Board (TRB), Washington, DC (virtual), United States.
Mielke R.R., Leathrum J.F., Collins A.J., Audette M.A. (2019) Overview of Computational Modeling and Simulation. In: Nestel D., Hui J., Kunkler K., Scerbo M., Calhoun A. (eds) Healthcare Simulation Research. Springer, Cham. https://doi.org/10.1007/978-3-030-26837-4_6
Nikpouraghdam, M., Farahani, A. J., Alishiri, G., et al. (2020), Epidemiological characteristics of coronavirus disease 2019 (COVID-19) patients in IRAN: A single center study, Journal of Clinical Virology, 127, 104378.
Paytner S. (2015), Humidity and respiratory virus transmission in tropical and temperate settings, Epidemiology & Infection, Volume 143 Issue 6, pp. 1110 – 1118
Perumalla K.S. and Seal S.K. (2011), Discrete event modeling and massively parallel execution of epidemic outbreak phenomena, Simulation: Transactions of the Society for Modeling and Simulation International 88(7) 768–78.
Peng-yun C., & Hui Y. (2020) System dynamics simulation on spread of COVID-19 by traffic and transportation[J].Journal of  Traffic and Transportation Engineering, 20(03):100-109.
Pourmalek F., Rezaei Hemami M., Janani L., Moradi-Lakeh M. (2021) Rapid review of COVID-19 epidemic estimation studies for Iran, BMC Public Health) 21:257, 1-30.
Raunak M., & Olsen M. (2014) Quantifying validation of discrete event simulation models, Winter Simulation Conference, Savannah, GA, USA
Richard M., & Fouchier R.M.A (2016), Influenza A virus transmission via respiratory aerosols or droplets as it relates to pandemic potential, FEMS Microbiology Reviews, Volume 40, Issue 1, January 2016, Pages 68–85.
Richardson G.P. (2019) Core of System Dynamics. In: Meyers R. (eds) Encyclopedia of Complexity and Systems Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27737-5_536-4
Shiu E.Y.C., Leung N.H.L., Cowling B.J. (2019), Controversy around airborne versus droplet transmission of respiratory viruses: implication for infection prevention, Current Opinion in Infectious Diseases, 32:4, 372-379.
Stapelberg NJC, Smoll NR, Randall M., et al. (2021) A Discrete-Event, Simulated Social Agent-Based Network Transmission (DESSABNeT) model for communicable diseases: Method and validation using SARS-CoV-2 data in three large Australian cities. PLoS ONE 16(5): e0251737
Tirachini A., & Cats O. (2020) COVID-19 and Public Transportation: Current Assessment, Prospects, and Research Needs, Journal of Public Transportation, Vol. 22 No. 1, pp. 1-21
Troko J., Myles P., Gibson J., et al. (2011) Is public transport a risk factor for acute respiratory infection? BMC Infect. Dis., 11, p. 16.
UITP. 2020. "Management of COVID-19: Guidelines for Public Transport Operators." Factsheet, March 2020. International Association of Public Transport (UITP). Accessed May 4, 2020. https://www.uitp.org/ management-covid-19-guidelines-public-transport-operators.
Wang M., & Flessa S. (2020), Modelling Covid‑19 under uncertainty: what can we expect?, The European Journal of Health Economics, 21:665–668.
Yaylali, E., Ivy, J., Uzsoy, R., et al. (2016) Modeling the effect of public health resources and alerting on the dynamics of pertussis spread. Health Syst 5, 81–97.
Journal of Quality Engineering and Production Optimization
Volume 7, Issue 1
May 2022
Pages 213-226

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