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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.

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.

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.

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.

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.

Interorganizational collaborations are crucial for delivering high-quality, integrated healthcare services. To maximize the benefits of these collaborative networks, effective governance structures and mechanisms must be in place. While previous studies have extensively examined organizational-level factors, such as partner capabilities and backgrounds, this study focuses on network-level factors, including collaboration structures and tie characteristics that shape effective network governance.

A systematic literature review (SLR) was conducted to identify and synthesize the key network-level factors influencing governance structures and mechanisms in healthcare networks.

The review identified 22 critical factors, categorized into three primary groups that impact network governance. These findings offer a robust foundation for developing context-sensitive governance models tailored to healthcare systems.

This study provides valuable insights for healthcare practitioners, policymakers and researchers by highlighting key factors that can improve interorganizational collaboration within healthcare systems. The findings contribute to both theory and practice, with the potential to enhance healthcare service delivery and patient outcomes.

This study is the first to systematically identify and categorize the network-level factors that influence governance structures and mechanisms in healthcare networks, providing a comprehensive and novel contribution to the field.