PT  - JOURNAL ARTICLE
AU  - T. Alex Perkins
AU  - Isabel Rodriguez-Barraquer
AU  - Carrie Manore
AU  - Amir S. Siraj
AU  - Guido España
AU  - Christopher M. Barker
AU  - Michael A. Johansson
AU  - Robert C. Reiner
TI  - Heterogeneous local dynamics revealed by classification analysis of spatially disaggregated time series data
AID  - 10.1101/276006
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 276006
4099  - http://biorxiv.org/content/early/2018/07/28/276006.short
4100  - http://biorxiv.org/content/early/2018/07/28/276006.full
AB  - Time series data provide a crucial window into ecological 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 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 cumulative incidence curves, which showed that variability in epidemic duration, the length of the epidemic tail, and consistency with a normal distribution function 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 ecological drivers. Overall, this diversity of temporal patterns at local scales underscores the value of spatially disaggregated time series data.