title: Evolution of scaling emergence in large-scale spatial epidemic spreading.
abstract: BACKGROUND
  Zipf's law and Heaps' law are two representatives of the scaling concepts, which play a significant role in the study of complexity science. The coexistence of the Zipf's law and the Heaps' law motivates different understandings on the dependence between these two scalings, which has still hardly been clarified.
  
  
  RESULTS
  In this article, we observe an evolution process of the scalings: the Zipf's law and the Heaps' law are naturally shaped to coexist at the initial time, while the crossover comes with the emergence of their inconsistency at the larger time before reaching a stable state, where the Heaps' law still exists with the disappearance of strict Zipf's law. Such findings are illustrated with a scenario of large-scale spatial epidemic spreading, and the empirical results of pandemic disease support a universal analysis of the relation between the two laws regardless of the biological details of disease. Employing the United States domestic air transportation and demographic data to construct a metapopulation model for simulating the pandemic spread at the U.S. country level, we uncover that the broad heterogeneity of the infrastructure plays a key role in the evolution of scaling emergence.
  
  
  CONCLUSIONS
  The analyses of large-scale spatial epidemic spreading help understand the temporal evolution of scalings, indicating the coexistence of the Zipf's law and the Heaps' law depends on the collective dynamics of epidemic processes, and the heterogeneity of epidemic spread indicates the significance of performing targeted containment strategies at the early time of a pandemic disease.
authors: Adaptive Networks and Control Lab, Department of Electronic Engineering, Fudan University, Shanghai, People's Republic of China.
authors: Lin
authors: L
authors: Wang
authors: Xiang
authors: X
authors: Li
authors: Yi-Qing
authors: YQ
authors: Zhang
authors: Yan
authors: Y
authors: Zhang
authors: Kan
authors: K
authors: Zhang
mesh_terms: D003710:Demography; D058872:Epidemics; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype / Q000472:pathogenicity; D007251:Influenza, Human / Q000453:epidemiology / Q000635:transmission; D008962:Models, Theoretical; D058873:Pandemics; D013997:Time Factors; D014186:Transportation