Heterogeneous local dynamics revealed by classification analysis of spatially disaggregated time series data
Heterogeneous local dynamics revealed by classification analysis of spatially disaggregated time series data
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通过空间分解时间序列数据的分类分析揭示异质局部动态
DOI：
10.1016/j.epidem.2019.100357
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发表时间：
2019
期刊：
影响因子：
3.8
通讯作者：
Reiner, Robert C.
中科院分区：
文献类型：
--
作者：
Perkins, T. Alex;Rodriguez-Barraquer, Isabel;Manore, Carrie;Siraj, Amir S.;España, Guido;Barker, Christopher M.;Johansson, Michael A.;Reiner, Robert C.
时间序列数据提供了了解传染病动态的重要窗口，但它们的用途往往受到它们呈现的空间聚集形式的限制。在处理时间序列数据时，违反空间聚集尺度下的同质动力学的隐含假设可能会影响对潜在过程的推断。我们在哥伦比亚2015-2016年寨卡疫情的背景下测试了这一假设，在国家、部门和市政尺度上提供了每周病例报告的时间序列。首先，我们进行了描述性分析，表明部门级疫情高峰的时间因三个月而异，部门级对随时间变化的繁殖数R(T)的估计显示出与国家级估计不同的模式。其次，我们应用分类算法对比例累积发病曲线的六个特征进行分类，结果表明，疫情持续时间、疫情尾部长度以及与累积正态密度曲线的一致性对区分群体的贡献最大。第三，我们将这种分类算法应用于用随机传输模型模拟的数据，结果表明，分组分配与模拟的基本复制数R0的差异是一致的。这一结果以及基于观察数据的传播的空间驱动因素和组分配之间的关联表明，分类算法能够检测与发病模式的潜在驱动因素的差异相关联的时间模式的差异。总体而言，地方尺度上时间模式的多样性突出了按空间分列的时间序列数据的价值。
Time series data provide a crucial window into infectious disease dynamics, yet their utility is often limited by the spatially aggregated form in which they are presented. When working with time series data, violating the implicit assumption of homogeneous dynamics below the scale of spatial aggregation could bias inferences about underlying processes. We tested this assumption in the context of the 2015–2016 Zika epidemic in Colombia, where time series of weekly case reports were available at national, departmental, and municipal scales. First, we performed a descriptive analysis, which showed that the timing of departmental-level epidemic peaks varied by three months and that departmental-level estimates of the time-varying reproduction number,R(t), showed patterns that were distinct from a national-level estimate. Second, we applied a classification algorithm to six features of proportional cumulative incidence curves, which showed that variability in epidemic duration, the length of the epidemic tail, and consistency with a cumulative normal density curve made the greatest contributions to distinguishing groups. Third, we applied this classification algorithm to data simulated with a stochastic transmission model, which showed that group assignments were consistent with simulated differences in the basic reproduction number,R0. This result, along with associations between spatial drivers of transmission and group assignments based on observed data, suggests that the classification algorithm is capable of detecting differences in temporal patterns that are associated with differences in underlying drivers of incidence patterns. Overall, this diversity of temporal patterns at local scales underscores the value of spatially disaggregated time series data.
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影响因子：
9.8
作者：
Siraj AS;Rodriguez-Barraquer I;Barker CM;Tejedor-Garavito N;Harding D;Lorton C;Lukacevic D;Oates G;Espana G;Kraemer MUG;Manore C;Johansson MA;Tatem AJ;Reiner RC;Perkins TA
通讯作者：
Perkins TA
空间和时间尺度之间的登革率多样性：局部结构以及宿主种群规模的影响。
DOI：
10.1126/science.aaj9384
发表时间：
2017-03-24
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Salje H;Lessler J;Maljkovic Berry I;Melendrez MC;Endy T;Kalayanarooj S;A-Nuegoonpipat A;Chanama S;Sangkijporn S;Klungthong C;Thaisomboonsuk B;Nisalak A;Gibbons RV;Iamsirithaworn S;Macareo LR;Yoon IK;Sangarsang A;Jarman RG;Cummings DA
通讯作者：
Cummings DA
影响因子：
4.8
作者：
Hastings, Alan
通讯作者：
Hastings, Alan
S1 数据集“寨卡病毒在岛屿人群中的传播动态：2013-14 法属波利尼西亚疫情的建模分析”
影响因子：
3.8
作者：
A. Kucharski;S. Funk;R. Eggo;H. Mallet;W. Edmunds;E. Nilles
通讯作者：
E. Nilles
影响因子：
28.3
作者：
Perkins, T. Alex;Siraj, Amir S.;Tatem, Andrew J.
通讯作者：
Tatem, Andrew J.