RT Journal Article
SR Electronic
T1 Heterogeneous local dynamics revealed by classification analysis of spatially disaggregated time series data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 276006
DO 10.1101/276006
A1 T. Alex Perkins
A1 Isabel Rodriguez-Barraquer
A1 Carrie Manore
A1 Amir S. Siraj
A1 Guido España
A1 Christopher M. Barker
A1 Michael A. Johansson
A1 Robert C. Reiner
YR 2019
UL http://biorxiv.org/content/early/2019/06/25/276006.abstract
AB 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.