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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

Map of hotspot locations with points on map represented by pie charts indicating the relative contributions of each community to the total visits recorded at that hotspot. Community III was primarily concentrated in the Gay Village neighborhood of the Ville-Marie borough of Montreal, whereas communities I and II primarily occupied the high-traffic commercial areas on either side of the Plateau-Mont-Royal neighborhood; all 3 communities coincided downtown. Each grid square is colored to represent the locally dominant community. Squares with no hotspots are colored to represent the dominant community at the nearest hotspot.
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figure2: Map of hotspot locations with points on map represented by pie charts indicating the relative contributions of each community to the total visits recorded at that hotspot. Community III was primarily concentrated in the Gay Village neighborhood of the Ville-Marie borough of Montreal, whereas communities I and II primarily occupied the high-traffic commercial areas on either side of the Plateau-Mont-Royal neighborhood; all 3 communities coincided downtown. Each grid square is colored to represent the locally dominant community. Squares with no hotspots are colored to represent the dominant community at the nearest hotspot.

Mentions: We analyzed the network of more than 600,000 physically proximal contacts between 103,425 users of a free public Wi-Fi hotspot system in Montreal, Canada (hereafter the Montreal network) to examine the effects of ordinary urban contact patterns on epidemic wave dynamics. We used an established heuristic method [28] to divide the Montreal network into 1420 distinct communities (Figure 1; also see Figure S3 in Multimedia Appendix 2). The 3 largest communities together contained 82,228 of 103,425 (79.50%) users in the network, with 38,569 (community I), 28,101 (community II), and 15,558 (community III) users. The mean degree (number of contacts) per user in each of these communities was 13.4 (SD 40.2), 8.3 (SD 23.7), and 26.7 (SD 76.4), respectively, compared with a mean of 4.6 (SD 9.3) for the 21,197 (20.50%) remaining users. The communities exhibited distinct geographic signatures corresponding to large mixed commercial and residential areas in the city, with considerable overlap occurring in Downtown Montreal (Figure 2).


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)

Map of hotspot locations with points on map represented by pie charts indicating the relative contributions of each community to the total visits recorded at that hotspot. Community III was primarily concentrated in the Gay Village neighborhood of the Ville-Marie borough of Montreal, whereas communities I and II primarily occupied the high-traffic commercial areas on either side of the Plateau-Mont-Royal neighborhood; all 3 communities coincided downtown. Each grid square is colored to represent the locally dominant community. Squares with no hotspots are colored to represent the dominant community at the nearest hotspot.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

figure2: Map of hotspot locations with points on map represented by pie charts indicating the relative contributions of each community to the total visits recorded at that hotspot. Community III was primarily concentrated in the Gay Village neighborhood of the Ville-Marie borough of Montreal, whereas communities I and II primarily occupied the high-traffic commercial areas on either side of the Plateau-Mont-Royal neighborhood; all 3 communities coincided downtown. Each grid square is colored to represent the locally dominant community. Squares with no hotspots are colored to represent the dominant community at the nearest hotspot.
Mentions: We analyzed the network of more than 600,000 physically proximal contacts between 103,425 users of a free public Wi-Fi hotspot system in Montreal, Canada (hereafter the Montreal network) to examine the effects of ordinary urban contact patterns on epidemic wave dynamics. We used an established heuristic method [28] to divide the Montreal network into 1420 distinct communities (Figure 1; also see Figure S3 in Multimedia Appendix 2). The 3 largest communities together contained 82,228 of 103,425 (79.50%) users in the network, with 38,569 (community I), 28,101 (community II), and 15,558 (community III) users. The mean degree (number of contacts) per user in each of these communities was 13.4 (SD 40.2), 8.3 (SD 23.7), and 26.7 (SD 76.4), respectively, compared with a mean of 4.6 (SD 9.3) for the 21,197 (20.50%) remaining users. The communities exhibited distinct geographic signatures corresponding to large mixed commercial and residential areas in the city, with considerable overlap occurring in Downtown Montreal (Figure 2).

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