Presenting an Evidence-Based Policymaking Model to Prevent the Coronavirus Diffusion (Case Study: Tehran)

Document Type : Research Paper


1 Ph.D. Candidate, Department of Technology Management, School of Management, Economics and Progress Engineering, Iran University of Science and Technology, Tehran, Iran.

2 Assistant Prof., Department of Management and Philosophy of Science and Technology, School of Management, Economics and Progress Engineering Iran University of Science and Technology, Tehran, Iran.

3 Ph.D. Candidate, Department of Public Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran.

4 Assistant Prof., Department of Management and Business Engineering, School of Management, Economics and Progress Engineering Iran University of Science and Technology, Tehran, Iran.


Objective: The purpose of this paper is to suggest intervention routes to responsible institutions in order to prevent the diffusion of coronavirus. The covid 19 pandemic is the most effective disease since the beginning of 2020, which in addition to many deaths, has had many economic and social effects. Many researchers are trying to treat and develop a vaccine, but one of the most basic measures is prevention because prevention is the fastest way to reduce mortality and its negative consequences.
Methods: For this purpose, with the maximum use of minimal data in the country and considering the new rules governing the behavior of this virus, the evidence-based policy-making method and Agent-based modeling have been used, which includes four steps of simulator construction, calibration, Validation, and its use to estimate how the disease is evolving.
Results: In order to determine the main factors affecting the prevention, four policy scenarios including general quarantine, non-intervention, passive intervention and intelligent intervention were examined. In simulating policy scenarios, the factors of movement rate and transmission risk change proportionally. The quarantine in an optimistic state itself includes four categories of scenarios based on different quarantine methods, and finally, the stop and treatment-related scenarios were also examined. The simulation results showed that a 50% reduction in the movement rate would lead to a reduction of more than 80% in the number of patients, and a 10% decrease in the transmission risk index would lead to a 30% reduction in the number of patients.
Conclusion: Finally, two factors of movement rate and transmission risk index were identified as the most important factors in the diffusion of coronavirus. Therefore, it is suggested that the responsible institutions focus on designing intelligent interventions related to the reduction of these two factors in order to reduce the prevalence of corona more quickly.


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