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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.

The basic purpose of this study is to increase the installation rate of mobile games and their life cycle. Little research has been conducted into the competitive market of mobile games and word-of-mouth (WOM), advertisement and quality simultaneously. In this paper, the authors aim to assist developers with a better understanding of how to invest in the game industry.

The primitive modeling has been made based on the Bass diffusion model and its extensions using features related to the game industry. Modeling and simulation have been performed using the system dynamics method.

The results show that if game developers only focus on advertising at the introduction of the game to the market, and neglect quality features, uninstall rate of adopters increases and the game life cycle decreases. And if developers only focus on the quality improvement of the game, and ignore advertisement, they couldn't create enough WOM and thus game installation wouldn't grow enough to create revenue. Furthermore, as long as the pleasure of the game is high for its adopters, developers shouldn't give up on investing in advertising to create even higher WOM. And also in communities with higher levels of variety-seeking among game players, quality is important at the start and advertisement is vital in the following steps of the game life cycle. And if the contact rate among people is high, developers could focus on quality more than the advertisement.

Even though WOM in mobile game installation is crucial, fewer studies have been done in this area and they only address the problem using qualitative or statistical methods. The quantitative system dynamics approach based on the Bass diffusion model, used in this research, fixes the shortcomings of the previous studies. Moreover, due to serious competition in the mobile game industry, only those developers can remain in the market who can predict the future trend of the game market. Furthermore, game development is a creative process and its key factors include engaging the gamers and monetizing.