title: Epidemic Wave Dynamics Attributable to Urban Community Structure: A Theoretical Characterization of Disease Transmission in a Large Network.
abstract: BACKGROUND
  Multiple waves of transmission during infectious disease epidemics represent a major public health challenge, but the ecological and behavioral drivers of epidemic resurgence are poorly understood. In theory, community structure—aggregation into highly intraconnected and loosely interconnected social groups—within human populations may lead to punctuated outbreaks as diseases progress from one community to the next. However, this explanation has been largely overlooked in favor of temporal shifts in environmental conditions and human behavior and because of the difficulties associated with estimating large-scale contact patterns.
  
  
  OBJECTIVE
  The aim was to characterize naturally arising patterns of human contact that are capable of producing simulated epidemics with multiple wave structures.
  
  
  METHODS
  We used an extensive dataset of proximal physical contacts between users of a public Wi-Fi Internet system to evaluate the epidemiological implications of an empirical urban contact network. We characterized the modularity (community structure) of the network and then estimated epidemic dynamics under a percolation-based model of infectious disease spread on the network. We classified simulated epidemics as multiwave using a novel metric and we identified network structures that were critical to the network's ability to produce multiwave epidemics.
  
  
  RESULTS
  We identified robust community structure in a large, empirical urban contact network from which multiwave epidemics may emerge naturally. This pattern was fueled by a special kind of insularity in which locally popular individuals were not the ones forging contacts with more distant social groups.
  
  
  CONCLUSIONS
  Our results suggest that ordinary contact patterns can produce multiwave epidemics at the scale of a single urban area without the temporal shifts that are usually assumed to be responsible. Understanding the role of community structure in epidemic dynamics allows officials to anticipate epidemic resurgence without having to forecast future changes in hosts, pathogens, or the environment.
authors.affiliation: Computational Epidemiology Group, Children's Hospital Informatics Program, Boston Children's Hospital, Boston, MA, United States.
authors.forename: Anne G
authors.identifier: http://orcid.org/0000-0003-0035-8689
authors.initials: AG
authors.lastname: Hoen
authors.forename: Thomas J
authors.identifier: http://orcid.org/0000-0003-1819-6235
authors.initials: TJ
authors.lastname: Hladish
authors.forename: Rosalind M
authors.identifier: http://orcid.org/0000-0002-0362-6717
authors.initials: RM
authors.lastname: Eggo
authors.forename: Michael
authors.identifier: http://orcid.org/0000-0003-0603-743X
authors.initials: M
authors.lastname: Lenczner
authors.forename: John S
authors.identifier: http://orcid.org/0000-0001-8568-5317
authors.initials: JS
authors.lastname: Brownstein
authors.forename: Lauren Ancel
authors.identifier: http://orcid.org/0000-0002-5828-8874
authors.initials: LA
authors.lastname: Meyers
mesh_terms: D003141:Communicable Diseases / Q000453:epidemiology* / Q000635:transmission; D004196:Disease Outbreaks; D018562:Disease Transmission, Infectious / Q000706:statistics & numerical data*; D058872:Epidemics / Q000706:statistics & numerical data*; D006801:Humans; D008962:Models, Theoretical; D019037:Urban Health Services / Q000592:standards*
keywords: communicable diseases; epidemics; transmission