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Epidemic Wave Dynamics Attributable to Urban Community Structure: A Theoretical Characterization of Disease Transmission in a Large Network.

Hoen AG, Hladish TJ, Eggo RM, Lenczner M, Brownstein JS, Meyers LA - J. Med. Internet Res. (2015)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computational Epidemiology Group, Children's Hospital Informatics Program, Boston Children's Hospital, Boston, MA, United States.

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.

No MeSH data available.


Related in: MedlinePlus

The frequency of multiwave epidemics at varying values of R0 and network shuffling with warm colors indicating a higher proportion of multiwave epidemics and cool colors indicating a low proportion of multiwave epidemics (frequency values indicated on contours). Frequencies were calculated across all epidemics (top left) and stratified by starting community (top right and bottom). Each pixel is based on 81,920 simulated epidemics originating in the specified community (10 simulations on each of 8192 uniquely shuffled networks).
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figure6: The frequency of multiwave epidemics at varying values of R0 and network shuffling with warm colors indicating a higher proportion of multiwave epidemics and cool colors indicating a low proportion of multiwave epidemics (frequency values indicated on contours). Frequencies were calculated across all epidemics (top left) and stratified by starting community (top right and bottom). Each pixel is based on 81,920 simulated epidemics originating in the specified community (10 simulations on each of 8192 uniquely shuffled networks).

Mentions: A modest amount of network shuffling (<3% of edges) almost entirely eliminated multiwave epidemics (Figure 6), whereas it minimally impacted the extent of community structure according to standard metrics including Q (Figure 7) and the numbers of edges linking distinct communities (see Figure S5 in Multimedia Appendix 2). This suggests that the critical driver of 2-peaked epidemics is not the number but the nature of intermodule contacts. We hypothesized that epidemiological synchrony arises when locally popular users in one community tended to be connected to locally popular users in another (between-community degree assortativity) and multiwave epidemics can occur only when communities lack such connectivity.


Epidemic Wave Dynamics Attributable to Urban Community Structure: A Theoretical Characterization of Disease Transmission in a Large Network.

Hoen AG, Hladish TJ, Eggo RM, Lenczner M, Brownstein JS, Meyers LA - J. Med. Internet Res. (2015)

The frequency of multiwave epidemics at varying values of R0 and network shuffling with warm colors indicating a higher proportion of multiwave epidemics and cool colors indicating a low proportion of multiwave epidemics (frequency values indicated on contours). Frequencies were calculated across all epidemics (top left) and stratified by starting community (top right and bottom). Each pixel is based on 81,920 simulated epidemics originating in the specified community (10 simulations on each of 8192 uniquely shuffled networks).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4526984&req=5

figure6: The frequency of multiwave epidemics at varying values of R0 and network shuffling with warm colors indicating a higher proportion of multiwave epidemics and cool colors indicating a low proportion of multiwave epidemics (frequency values indicated on contours). Frequencies were calculated across all epidemics (top left) and stratified by starting community (top right and bottom). Each pixel is based on 81,920 simulated epidemics originating in the specified community (10 simulations on each of 8192 uniquely shuffled networks).
Mentions: A modest amount of network shuffling (<3% of edges) almost entirely eliminated multiwave epidemics (Figure 6), whereas it minimally impacted the extent of community structure according to standard metrics including Q (Figure 7) and the numbers of edges linking distinct communities (see Figure S5 in Multimedia Appendix 2). This suggests that the critical driver of 2-peaked epidemics is not the number but the nature of intermodule contacts. We hypothesized that epidemiological synchrony arises when locally popular users in one community tended to be connected to locally popular users in another (between-community degree assortativity) and multiwave epidemics can occur only when communities lack such connectivity.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computational Epidemiology Group, Children's Hospital Informatics Program, Boston Children's Hospital, Boston, MA, United States.

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.

No MeSH data available.


Related in: MedlinePlus