使用随机动力系统捕获流行病的时变驱动因素
Capturing the time-varying drivers of an epidemic using stochastic dynamical systems
摘要：
流行病的模型往往使用非线性动力系统，通过部分和噪声数据观察。在本文中，我们考虑随机扩展，以捕捉未知的影响（变化的行为，公共干预，季节性效应等）。这些模型分配扩散过程的时变参数，我们的推理过程是基于适当调整的自适应粒子MCMC算法。所提出的计算方法的性能进行了验证，模拟数据和所采用的模型被应用到2009年H1N1流感大流行在英格兰。除了估计有效接触率轨迹外，还实时应用该方法为相关公共卫生决策提供证据。本文还介绍了具有年龄结构的扩散驱动SEIR模型。
英文摘要：
Epidemics are often modelled using non-linear dynamical systems observed through partial and noisy data. In this paper, we consider stochastic extensions in order to capture unknown influences (changing behaviors, public interventions, seasonal effects etc). These models assign diffusion processes to the time-varying parameters, and our inferential procedure is based on a suitably adjusted adaptive particle MCMC algorithm. The performance of the proposed computational methods is validated on simulated data and the adopted model is applied to the 2009 H1N1 pandemic in England. In addition to estimating the effective contact rate trajectories, the methodology is applied in real time to provide evidence in related public health decisions. Diffusion driven SEIR-type models with age structure are also introduced.
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