Modeling Spatial Spread of Epidemic Diseases using Agent-based Simulation (Case Study: Seasonal Influenza)

Spatial epidemiology issues are outstandingly important, particularly the viral spread through populated areas is believed to be one of the major concerns. The outbreak of epidemic diseases in a community is inherently a spatio-temporal process of great importance for modern society. Modeling the spread and abrupt transmission of infectious diseases demands a better understanding of its dynamic behaviors to avoid sever consequences by appropriate preventive strategies. Agent Based Modeling (ABM) is one of the innovative technologies for observing the spread of epidemic diseases. Agent-based models allow interaction among individuals and are capable to overcome the limitations of different approaches such as cellular automata and classical epidemic models, permitting to study specific spatial aspects of the spread of epidemics and addressing naturally stochastic nature of the epidemic process. Consisting of a population of individual actors or "agents", an environment, and a set of rules, actions in ABM take place through the agents, which are simple, self-contained programs that collect information from their surroundings and use it to determine how to act. Agent based simulation together with the improved Susceptible-Exposed-Infected-Recovered (SEIR) model, provides an opportunity for the study of interactions at the individual levels that includes social and casual relationships between individuals. The signature success of agent-based modeling in public health is in the study of epidemics and infectious disease dynamics. ABMs have been used to study disease transmission at multiple scales, from individual communities to global pandemics. According to the previous researches, the relationships between factors affecting the outbreak diseases and its spread had not been ccomprehensively presented yet. Therefore, the purpose of this study is to provide a spatial agent based modeling framework for simulating the spread of Seasonal Influenza. Due to the sudden and rapid spread of seasonal Influenza, the parameters of this disease were used for simulation. In this study, to investigate the effects of spatial units and other factors affecting outbreaks of seasonal influenza, simulation was performed, and then analyzed through five different scenarios. These scenarios were presented as the effects of population size, latent period, period of disease, transmission rate and polluted places on the spread of disease. Results showed that the output of epidemic follows a traditional epidemiological curve and also the output of scenarios lead to a better understanding of the factors in the spread of disease. Our results confirms the previous studies on this subject. For example, the results of the impact of spatial units on outbreak showed that considering the impact of polluted places leading to a significantly increase of pollution in the environment. Therefore, dynamic interactions between agents and environment lead to explore the spread of disease in the model. Finally, the model can be used to inform and educate the public about the spread of infectious disease such as Seasonal Influenza, and can allow epidemiological researchers to assess systems’ behavior under various conditions. Therefore, This type of simulations can help to improve comprehension of disease spread dynamics and to take better steps towards the prevention and control of an epidemic outbreak.