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Abstract
The outbreak and abrupt transmission of infectious diseases demand better understanding of the dynamic behavior of such phenomena and pro-active actions to avoid sever consequences. The most recent epidemic disease, Ebola virus, exemplifies the urgent response as the fatality rate has been varied from 25% to 90% in past outbreaks. In this study, we propose a simulation framework to utilize the advantages of agent-based modeling to simulate epidemic diseases. Based on the improved Susceptible-Exposed-Infected-Recovered (SEIR) epidemic model, the agent-based simulation provides the opportunity to study the effect of social interactions in individual levels including social and causal contacts. Incorporating the recent CDC's Ebola evaluation algorithms for parameters' estimation, the model can be used to inform and educate the public about the spread of infectious disease such as Ebola, allow epidemiological researchers and other decision makers to conduct "what-if" analyses with the purpose of assessing systems' behavior under various conditions.
Keywords
Agent-based Model (ABM), Simulation, Epidemic Models
1. Introduction
The outbreaks of infectious diseases such as the H1N1 flu, bird flu, severe acute respiratory syndrome, and Ebola, the most recent one, have sparked a huge growth in the mathematical modeling and simulation literature to gain further insight regarding the transmission dynamics and devise effective prevention and control/intervention strategies. In pursuance of gaining a better understanding of the mechanism of infection spreading and other similar propagation processes, it is crucial to examine the impact of the connectivity pattern of the underlying network on the spread of infection [1].
In the absence of reliable pandemic detection systems, mathematical and computational models have extensively been used by health policy makers to predict and control disease epidemics [2]. The increasing availability of computer resources facilitates the modeling of epidemical outbreaks on several tiers, including individual, community, state, and country levels. Computer models promise an improvement in representing and understanding the complex social structure as well as the heterogeneous patterns in the contact networks of real-world populations, determining the transmission dynamics [3]. The most recent and flexible approaches of such sophisticated modeling are agent-based modeling and system dynamics.
Agent-based modeling of pandemics recreates the entire populations and their dynamics through incorporating social structures, heterogeneous connectivity patterns, and meta-population grouping at the scale of the single individual [4]. Such systems...