Purpose

The purpose of this paper is to develop a system dynamics approach based on Susceptible, Exposed, Infected, Recovered (SEIR) model to investigate the coronavirus pandemic and the impact of therapeutic and preventive interventions on epidemic disaster.

Design/methodology/approach

To model the behavior of COVID-19 disease, a system dynamics model is developed in this paper based on SEIR model. In the proposed model, the impact of people's behavior, contact reduction, isolation of the sick people as well as public quarantine on the spread of diseases is analyzed. In this model, data collected by the Iran Ministry of Health have been used for modeling and verification of the results.

Findings

The results show that besides the intervening policies, early application of them is also of utmost priority and makes a significant difference in the result of the system. Also, if the number of patients with extreme conditions passes available hospital intensive care capacity, the death rate increases dramatically. Intervening policies play an important role in reducing the rate of infection, death and consequently control of pandemic. Also, results show that if proposed policies do not work before the violation of the hospital capacity, the best policy is to increase the hospital’s capacity by adding appropriate equipment.

Research limitations/implications

The authors also had some limitations in the study including the lack of access to precise data regarding the epidemic of coronavirus, as well as accurate statistics of death rate and cases in the onset of the virus due to the lack of diagnostic kits in Iran. These parameters are still part of the problem and can negatively influence the effectiveness of intervening policies introduced in this paper.

Originality/value

The contribution of this paper includes the development of SEIR model by adding more policymaking details and considering the constraint of the hospital and public health capacity in the rate of coronavirus infection and death within a system dynamics modeling framework.

中文翻译：

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COVID-19 大流行控制的系统动力学方法：以伊朗为例

目的

本文的目的是开发一种基于易感、暴露、感染、恢复 (SEIR) 模型的系统动力学方法，以研究冠状病毒大流行以及治疗和预防干预措施对流行病灾难的影响。

设计/方法/方法

为了模拟 COVID-19 疾病的行为，本文基于 SEIR 模型开发了系统动力学模型。在提出的模型中，分析了人们的行为、减少接触、隔离病人以及公共隔离对疾病传播的影响。在该模型中，伊朗卫生部收集的数据已用于对结果进行建模和验证。

发现

结果表明，除了干预政策外，早期应用政策也是重中之重，对制度的结果有显着影响。此外，如果极端情况的患者数量超过可用的医院重症监护能力，死亡率会急剧上升。干预政策在降低感染率、死亡率并进而控制大流行方面发挥着重要作用。此外，结果表明，如果提出的政策在违反医院容量之前不起作用，最好的策略是通过增加适当的设备来增加医院的容量。

研究限制/影响

作者在研究中也有一些局限性，包括无法获得有关冠状病毒流行的准确数据，以及由于伊朗缺乏诊断试剂盒而无法准确统计死亡率和病毒发作的病例数。这些参数仍然是问题的一部分，会对本文介绍的干预政策的有效性产生负面影响。

原创性/价值

本文的贡献包括通过添加更多决策细节并在系统动力学建模框架内考虑医院和公共卫生能力在冠状病毒感染率和死亡率方面的约束来开发 SEIR 模型。