Investigating the Effects of the Spread of Contagious Disease and Immunization of Population on Social Welfare Using Agent Based Modeling

Scientists believe that the spread of contagious diseases and immunization of the population has huge effects on the health and economy of the community, and vaccination has been pointed out to be one of the most effective methods of preventive interventions. The purpose of the study was to investigate and analyze the effects of contagious disease spread and immunization of the population on social welfare by the agent based modeling computational approach. In the current research, attempts were made to simulate the spread of contagious disease and immunization in a society of agents and investigate their effects on some welfare indicators including Gini coefficient, wealth average, and mortality rate due to starvation. The intended method in the present study was agent-based modeling and simulation along with conducting library studies, and the instrument used was NetLogo. Therefore, a model was extended and developed, and the society was simulated once in the absence of the sick agent and infection in the environment. Depending on the level of vision, the agents began to collect sugar from the environment. Next simulation was a society consisting of sick or infected agents and transmission of disease among population. Besides, the agents collected and consumed the environmental resource (sugar). In the third simulation, immunization of the population (through vaccination) was applied to control the infection and spread of the disease. Thus, some experiments were carried out, and the observations and findings were recorded to eventually be compared and analyzed. Findings of the experiments suggest that the presence and spread of contagious diseases in the population could lead to decreased wealth average of agents, increased rate of mortality due to starvation, and increased Gini coefficient (more unequal distribution of wealth) as well. Thus, it can lead to reduced welfare of agents in an artificial society. Moreover, immunization of the population and control of disease by vaccination can increase wealth average of agents, decrease the mortality rate due to starvation, and decrease Gini coefficient. Therefore, it can lead to the improvement of welfare status in an artificial society. Using agent-based modeling, the spread of contagious disease in the population and immunization of the population (through vaccination) was simulated and investigated through various experiments, and their effects on wealth distribution, mortality rate due to starvation, and wealth average of the agents were identified. Mapping the results of the artificial environment into the real world suggests that the spread of contagious disease in the population could lead to decreased social welfare in a society. Immunization of the population as well as controlling the disease can lead to the improvement of welfare in a society. Library studies also confirm the validity of the results derived from the model. Studies indicate that vaccination is capable of significantly decreasing mortality and inequality. Studies also show that contagious diseases, especially in low-income countries, still account for a significant part of mortality. Vaccination can reduce the burden of contagious diseases and their imposed costs, decrease differences in wealth, and support social development and economic growth. Moreover, studies show that controlling contagious diseases can save wealth for societies and countries and provide social and economic advantages. Finally, some points should be mentioned about the methodology of the study: despite the strengths and advantages of using of agent based modeling approach in social, economic, and health studies, some points should be noted. Some social science experts believe that an intelligent computational agent has little similarity with being human. In addition, in the development of computational models, some variables, or parameters or conditions, and rules affecting a system or a real problem may not be considered. Furthermore, the results from the artificial environment may be accompanied by errors in comparison with the results from the real environment, although the error value may be acceptable. Additionally, Some questions remain, like: Is using such modeling always possible while facing any complex problems or systems? With what combination of parameters, rules, and conditions? Is any developed model (in the artificial environment) reliable and valid? Does any developed model have the required validity and quality? What approaches or methods and issues should be considered in order to validate and verify agent based computational models? These are some of the concerns and questions that are still likely to exist in the face of the developers of these models and users of this approaches (methods) and these can be referred to as some of the limitations and challenges in similar research studies.