epidemic wave dynamics attributable to urban community structure a theoretical characterization CORD-Papers-2022-06-02 (Version 1)

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 structureaggregation into highly intraconnected and loosely interconnected social groupswithin 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 networks 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.
Published: 2015-07-08
Journal: J Med Internet Res
DOI: 10.2196/jmir.3720
DOI_URL: http://doi.org/10.2196/jmir.3720
Author Name: Hoen Anne G
Author link: https://covid19-data.nist.gov/pid/rest/local/author/hoen_anne_g
Author Name: Hladish Thomas J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/hladish_thomas_j
Author Name: Eggo Rosalind M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/eggo_rosalind_m
Author Name: Lenczner Michael
Author link: https://covid19-data.nist.gov/pid/rest/local/author/lenczner_michael
Author Name: Brownstein John S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/brownstein_john_s
Author Name: Meyers Lauren Ancel
Author link: https://covid19-data.nist.gov/pid/rest/local/author/meyers_lauren_ancel
license: cc-by
license_url: https://creativecommons.org/licenses/by/4.0/
source_x: PMC
source_x_url: https://www.ncbi.nlm.nih.gov/pubmed/
pubmed_id: 26156032
pubmed_id_url: https://www.ncbi.nlm.nih.gov/pubmed/26156032
pmcid: PMC4526984
pmcid_url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4526984
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4526984/
has_full_text: TRUE
Keywords Extracted from PMC Text: Île Sans Fil (ÎSF self-loops TP 28,101 Downtown Montreal (Figure 2 network contact networks Figure 6 Montreal [42] cell Tcb R0=7.5 A-B 15,558 single-wave 44.60 R0=1.9 within- Figures 3 and 4) 103,425 nodes self-edges ÎSF database T=1–(1–Tcb)d Montreal network ÎSF Multimedia Appendix 1 within-community contacts node C-D Q. Figures 4 and 5 proximal contacts chain-binomial [30] ÎSF network volunteers 's Multimedia Appendix 2 high-degree individuals susceptible-infectious-recovered percolation-based Neal [40 nodes " insular US 2-peaked (Figure 9 392/446 ~1.01f human 38,569 stubs within-community edges
Extracted PMC Text Content in Record: First 5000 Characters:Epidemics of infectious diseases are frequently characterized by multiple waves of infection [1-3]. Notably, the 1918 influenza pandemic spread through several US and European cities in multiple waves with local variation in the frequency and timing of individual epidemic peaks [4-8]. Predicting when and where disease will resurge is critical to effective prevention and control. However, the drivers and dynamics of multiwave epidemics are unclear. For influenza pandemics, possible explanations include antigenic drift [8-12], waning immunity [13], changing environmental conditions [12,14,15], and social distancing behavior [15-17]. Community structure—aggregation into highly intraconnected but loosely interconnected groups—is a common feature of social contact networks [18] that can potentially drive multiwave epidemics as a disease spreads through one group before emerging in another. However, community structure has been neglected as a possible explanation for multiwave influenza pandemics, in part because it is difficult to detect and estimate [19]. Most studies describing routine human contact patterns have relied on diary- or questionnaire-based surveys [20] or specially deployed wireless sensors [21] and, thus, rarely yield data sufficient for inferring large-scale aggregations. Social networks estimated from electronic "contacts" (ie, cell phones, social networking websites) have been shown to exhibit community structure at larger scales [22-26], but do not capture the physical interactions through which diseases spread. However, the ubiquity of community structure across these networks suggests that it may be a general hallmark of social networks. Here, we address the hypothesis that contact patterns in a large, empirical, urban contact network are sufficient to generate multiwave epidemics for pandemic influenza-like diseases in the absence of any temporal changes in the hosts, pathogen, or environment. We find that the fate of an epidemic in such a network—whether and when multiple waves occur—depends not only on community structure but also, critically, the presence or absence of bridge superspreaders who forge connections between communities. Direct links between the popular members of different communities synchronize outbreaks; the occasional absence of such bridges provides the epidemiological separation underlying multiwave epidemics. Interactions between strangers can serve as critical transmission routes for respiratory diseases such as influenza, yet they are difficult to capture in traditional sociological surveys. Using data indicating the physical proximity of more than 100,000 Wi-Fi hotspots users, we characterize the structure of an urban extrasocial interaction network and assess its epidemiological implications. Île Sans Fil (ÎSF) is a not-for-profit organization established in 2004 in Montreal, Canada, that operates a system of public Internet hotspots. Hotspots are located in cafes, community and recreation centers, salons, markets, and other small businesses and public places. They are maintained by ÎSF staff and volunteers with the Internet connection provided by the establishment. We analyzed the database of all connections to the system of 352 hotspots between August 2004 and March 2010. Raw data from the ÎSF database consisted of 2.18 million connection records. Each record included an anonymized user ID, latitude and longitude coordinates for each ÎSF hotspot location, connection and disconnection times, and the unique media access control address for the user's wireless device. The data reported in this paper are available from the Community Resource for Archiving Wireless Data at Dartmouth (CRAWDAD) archive [27]. We built a contact network by interpreting each individual user as a node and concurrent ÎSF usage at the same hotspot as an edge. This preliminary network contained 114,810 nodes and 1.2 million edges. It contained both self-loops (users connecting multiple devices at once) and parallel edges (pairs of users with multiple overlapping hotspot visits) that we removed to produce a nonredundant network with 637,430 edges. We analyzed the largest connected component of this network, which consisted of 103,425 nodes and 630,893 edges. Modularity (Q) quantifies the extent of community structure in a network relative to a comparable random network. Given a network and a particular partitioning of the nodes into communities, Q is defined as the number of edges contained within communities minus the number of edges expected to fall within communities if the edges were distributed randomly (preserving the degrees of all nodes), normalized for network size. Q ranges from zero for randomly connected networks to greater than 0.3 for networks with substantial community structure [28]. We used a heuristic method to divide the Montreal network into a set of communities that maximized Q using an algorithm [28] that initially assigned each node to its own community and then iterati
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