Individual behavior plays an important role in the spread of emerging infectious diseases, and the ability to mitigate and control a disease through behavior changes is a vital aspect of response strategies. It is essential and important to understand how individuals behavior changes affect the spread of emerging infectious diseases. To address this, a psychological model of individual decision-making on social networks with infectious disease model (LHBM) is developed, and its applicability to the course of the 2009 A/H1N1 influenza outbreak is demonstrated.Individual behavior models (IBMs) are formalized to incorporate the rate of change of individuals behavior and investigated how individuals behavior changes affect the dynamical development of A/H1N1. In addition, approximate Bayesian computation sequential Monte Carlo (ABC SMC) methods are used to evaluate unknown parameters. The main results indicated that the novel model can depict the real transmission process and reveal the effects of individual behavior changes on A/H1N1 outbreak more accurately. Moreover, as far as mitigating and controlling emerging infectious diseases are concerned, it is essential for media publicity to focus on how to guide individuals behavior changes and get individuals to response positively.

individual behavior change; A/H1N1; individual behavior models (IBMs); LHBM;approximate Bayesian computation sequential Monte Carlo(ABC SMC)