Defence Science and Technology Organisation, 506 Lorimer Street, Melbourne, VIC 3207, Australia.
Math Biosci. 2012 Nov;240(1):12-9. doi: 10.1016/j.mbs.2012.05.010. Epub 2012 Jun 13.
The paper presents a method for syndromic surveillance of an epidemic outbreak due to an emerging disease, formulated in the context of stochastic nonlinear filtering. The dynamics of the epidemic is modeled using a stochastic compartmental epidemiological model with inhomogeneous mixing. The syndromic (typically non-medical) observations of the number of infected people (e.g. visits to pharmacies, sale of certain products, absenteeism from work/study, etc.) are assumed available for monitoring and prediction of the epidemic. The state of the epidemic, including the number of infected people and the unknown parameters of the model, are estimated via a particle filter. The numerical results indicate that the proposed framework can provide useful early prediction of the epidemic peak if the uncertainty in prior knowledge of model parameters is not excessive.
本文提出了一种基于随机非线性滤波的新发传染病爆发综合征监测方法。该方法采用具有非均匀混合的随机房室流行性病学模型来模拟传染病的动态。综合征（通常是非医学的）观察到的感染人数（例如去药店、购买某些产品、旷工/旷课等）被假定可用于监测和预测传染病。通过粒子滤波器对传染病的状态（包括感染人数和模型的未知参数）进行估计。数值结果表明，如果模型参数先验知识的不确定性不太大，则该框架可以为传染病高峰期提供有用的早期预测。
