Zhong Wei
School of Public Administration and Policy, Renmin University of China, No. 59 Zhongguancun Street, Haidian District, Beijing, 100872 China.
Comput Math Organ Theory. 2017;23(4):475-495. doi: 10.1007/s10588-016-9238-9. Epub 2016 Nov 29.
Individual responsive behavior to an influenza pandemic has significant impacts on the spread dynamics of this epidemic. Current influenza modeling efforts considering responsive behavior either oversimplify the process and may underestimate pandemic impacts, or make other problematic assumptions and are therefore constrained in utility. This study develops an agent-based model for pandemic simulation, and incorporates individual responsive behavior in the model based on public risk communication literature. The resultant model captures the stochastic nature of epidemic spread process, and constructs a realistic picture of individual reaction process and responsive behavior to pandemic situations. The model is then applied to simulate the spread dynamics of 2009 H1N1 influenza in a medium-size community in Arizona. Simulation results illustrate and compare the spread timeline and scale of this pandemic influenza, without and with the presence of pubic risk communication and individual responsive behavior. Sensitivity analysis sheds some lights on the influence of different communication strategies on pandemic impacts. Those findings contribute to effective pandemic planning and containment, particularly at the beginning of an outbreak.
个体对流感大流行的反应行为对该流行病的传播动态有着重大影响。当前考虑反应行为的流感建模工作要么过度简化过程，可能低估大流行的影响，要么做出其他有问题的假设，因此在实用性方面受到限制。本研究开发了一种基于主体的大流行模拟模型，并根据公共风险沟通文献在模型中纳入个体反应行为。所得模型捕捉了疫情传播过程的随机性，并构建了个体对大流行情况的反应过程和反应行为的现实图景。然后将该模型应用于模拟2009年甲型H1N1流感在亚利桑那州一个中等规模社区的传播动态。模拟结果说明了并比较了这种大流行性流感在有无公共风险沟通和个体反应行为情况下的传播时间线和规模。敏感性分析揭示了不同沟通策略对大流行影响的作用。这些发现有助于制定有效的大流行规划和防控措施，特别是在疫情爆发初期。
