Heterogeneous local dynamics revealed by classification analysis of spatially disaggregated time series data

T. Alex Perkins, Isabel Rodriguez-Barraquer, Carrie Manore, Amir S. Siraj, Guido España, Christopher M. Barker, Michael A. Johansson, Robert C. Reiner

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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.

Original languageEnglish (US)
Article number100357
StateAccepted/In press - Jan 1 2019
Externally publishedYes


  • Emerging disease
  • Spatial dynamics
  • Spatial heterogeneity
  • Time series data
  • Vector-borne disease

ASJC Scopus subject areas

  • Parasitology
  • Epidemiology
  • Microbiology
  • Public Health, Environmental and Occupational Health
  • Virology
  • Infectious Diseases


Dive into the research topics of 'Heterogeneous local dynamics revealed by classification analysis of spatially disaggregated time series data'. Together they form a unique fingerprint.

Cite this