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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

Expected within-community epidemic size assuming that communities were approximately random networks and maintained their empirical within-community degree distributions (colored lines; primary y-axis). The epidemic threshold for each community (ie, R0 value for which transmission is sustained) is lowest for community III, followed by communities I and II. The frequency of multiwave epidemics depended on R0 and is highest when R0=2.4 (gray line; secondary y-axis).
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figure3: Expected within-community epidemic size assuming that communities were approximately random networks and maintained their empirical within-community degree distributions (colored lines; primary y-axis). The epidemic threshold for each community (ie, R0 value for which transmission is sustained) is lowest for community III, followed by communities I and II. The frequency of multiwave epidemics depended on R0 and is highest when R0=2.4 (gray line; secondary y-axis).

Mentions: R0 is related to the likelihood and extent of a sustained outbreak [37]. R0 depends on the transmissibility of the pathogen, host recovery, and the structure of the host contact network [38]. Assuming that within-community contacts are approximately random and using a percolation-based model [39], we estimated that a disease with a global R0 equal to 1 had local R0 values of 0.8, 0.4, and 1.6 in communities I, II, and III, respectively (when considering only within-community edges) and exhibited considerable variability in epidemiological vulnerability across communities (Figure 3).


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)

Expected within-community epidemic size assuming that communities were approximately random networks and maintained their empirical within-community degree distributions (colored lines; primary y-axis). The epidemic threshold for each community (ie, R0 value for which transmission is sustained) is lowest for community III, followed by communities I and II. The frequency of multiwave epidemics depended on R0 and is highest when R0=2.4 (gray line; secondary y-axis).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

figure3: Expected within-community epidemic size assuming that communities were approximately random networks and maintained their empirical within-community degree distributions (colored lines; primary y-axis). The epidemic threshold for each community (ie, R0 value for which transmission is sustained) is lowest for community III, followed by communities I and II. The frequency of multiwave epidemics depended on R0 and is highest when R0=2.4 (gray line; secondary y-axis).
Mentions: R0 is related to the likelihood and extent of a sustained outbreak [37]. R0 depends on the transmissibility of the pathogen, host recovery, and the structure of the host contact network [38]. Assuming that within-community contacts are approximately random and using a percolation-based model [39], we estimated that a disease with a global R0 equal to 1 had local R0 values of 0.8, 0.4, and 1.6 in communities I, II, and III, respectively (when considering only within-community edges) and exhibited considerable variability in epidemiological vulnerability across communities (Figure 3).

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