Incidence data provide a crucial window into the dynamics of emerging infectious diseases, yet their utility may be limited by the highly spatially aggregated form in which they are often presented. Despite these possible limitations, spatially aggregated incidence data of emerging infectious diseases are often used to estimate key epidemiological quantities, such as R0, that have a strong influence on public perception of epidemic dynamics and policy responses thereto. To determine the extent to which spatially aggregated data may be misleading about spatially disaggregated dynamics, we characterized differences in the temporal dynamics of Zika virus (ZIKV) in Colombia at three distinct scales of spatial aggregation: national, departmental, and municipality scales.
Results/Conclusions
A combination of latent class analysis of subnational incidence time series and simulation analyses of a mechanistic model suggest that subnational units tend to fall into one of three categories: “sources” of transmission that sustain robust epidemics, “sinks” of transmission in which incidence largely reflects importation from elsewhere, and “chimeras” that are comprised of constituent sources and sinks at lower scales. National-level incidence patterns are consistent with our proposed chimera category, as are incidence patterns in a number of departments. Only at the municipality scale do dynamics appear to be more commonly consistent with either source-like or sink-like incidence patterns. This demonstrates that national-scale patterns are not sufficient for understanding local dynamics, underscoring the value of spatially disaggregated incidence data and the importance of locally tailored strategies for responding to emerging infectious diseases.