POWER-LAW MODELS FOR INFECTIOUS DISEASE SPREAD
POWER-LAW MODELS FOR INFECTIOUS DISEASE SPREAD
复制标题
DOI：
10.1214/14-aoas743
复制
发表时间：
2014-09-01
影响因子：
1.8
通讯作者：
Held, Leonhard
中科院分区：
文献类型：
--
作者：
Meyer, Sebastian;Held, Leonhard
人类的短时间旅行行为可以用关于距离的幂定律来描述。我们将这些信息纳入传染病监测数据的时空模型中，以更好地捕捉疾病传播的动态。扩展了以前建立的两个模型类，这两个模型类都将疾病风险相加地分解为地方性和流行性成分：用于个体水平数据的时空点过程模型和用于聚集计数数据的多变量时间序列模型。在这两个框架中，空间相互作用的幂律衰减被嵌入到流行病分量中，并使用(惩罚)似然推理与所有其他未知参数联合估计。鉴于点过程模型中的幂定律可以基于欧几里德距离，提出了一种新的计算数据的公式，其中幂定律依赖于离散空间单元的邻域顺序。通过重新分析德国侵袭性脑膜炎双球菌病个体病例(2002-2008年)和计算德国南部140个行政区的流感数据(2001-2008年)，对新方法的绩效进行了调查。在这两个应用中，幂定律大大改善了模型的拟合和预测，并相当接近于替代的定性公式，其中距离和邻域顺序分别被视为一个因素。在R包监控中的实施允许该方法应用于其他环境。
Short-time human travel behaviour can be described by a power law with respect to distance. We incorporate this information in space-time models for infectious disease surveillance data to better capture the dynamics of disease spread. Two previously established model classes are extended, which both decompose disease risk additively into endemic and epidemic components: a spatio-temporal point process model for individual-level data and a multivariate time-series model for aggregated count data. In both frameworks, a power-law decay of spatial interaction is embedded into the epidemic component and estimated jointly with all other unknown parameters using (penalised) likelihood inference. Whereas the power law can be based on Euclidean distance in the point process model, a novel formulation is proposed for count data where the power law depends on the order of the neighbourhood of discrete spatial units. The performance of the new approach is investigated by a reanalysis of individual cases of invasive meningococcal disease in Germany (2002-2008) and count data on influenza in 140 administrative districts of Southern Germany (2001-2008). In both applications, the power law substantially improves model fit and predictions, and is reasonably close to alternative qualitative formulations, where distance and order of neighbourhood, respectively, are treated as a factor. Implementation in the R package surveillance allows the approach to be applied in other settings.