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

Publication date: Available online 22 July 2019Source: EpidemicsAuthor(s): T. Alex Perkins, Isabel Rodriguez-Barraquer, Carrie Manore, Amir S. Siraj, Guido España, Christopher M. Barker, Michael A. Johansson, Robert C. ReinerAbstractTime 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 as...

Source: Epidemics - July 23, 2019 Category: Epidemiology Source Type: research