Methods and Measures for the Description of Epidemiologic Contact Networks

Riolo, Christopher S.; Koopman, James S.; Chick, Stephen E.

doi:10.1093/jurban/78.3.446

Cited by 50 publications

(38 citation statements)

References 31 publications

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“…This set consists of all vertices that can have been the source of an infection infecting i. Riolo et al [128] points out the size of i's source set-i's source count-as an important quantity. Moody [108] gives another definition of the reachability of a vertex that increases in proportion to the count of time respecting paths.…”

Section: B Time-respecting Paths and Reachabilitymentioning

confidence: 99%

“…Riolo et al [128] present a version of line graphs adding some more temporal information. What they call a transmission graph thus goes beyond modeling the contact structure and also incorporates the disease dynamics via a parameter δ that quantifies the combined incubation time and duration of a disease.…”

Section: Transmission Graphsmentioning

confidence: 99%

“…Furthermore, paths in transmission graphs do not have to be time respecting. Riolo et al [128] define some different versions of their transmission graphs cutting the contact sequence in various ways, but essentially the idea is outlined above.…”

Section: Transmission Graphsmentioning

confidence: 99%

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Temporal networks

2012

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A great variety of systems in nature, society and technology-from the web of sexual contacts to the Internet, from the nervous system to power grids-can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via email, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks. The study of temporal networks is very interdisciplinary in nature. Reflecting this, even the object of study has many names

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Section: B Time-respecting Paths and Reachabilitymentioning

confidence: 99%

Section: Transmission Graphsmentioning

confidence: 99%

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Temporal networks

2012

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“…In other words, the mechanisms driving the formation of contacts at the time-scale of minutes are different than those at the time scale of months [675]. This is of particular importance when studying the spreading of infectious diseases [230,593,596,684,[686][687][688][689][690][691][692][693][694][695][696][697][698][699][700][701][702][703][704]. Indeed, contacts dynamics that affect the spreading of transmissible illnesses are those unfolding at comparable time-scale respect to the disease [219,652,676,686].…”

Section: Measuring and Understanding Close Proximity Interactionsmentioning

confidence: 99%

Statistical physics of vaccination

et al. 2016

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Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination-one of the most important preventive measures of modern times-is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.

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“…Riolo et al 13 provide an example of cutting-edge modeling tools that can use network concepts and data together with characteristics of agent/host interaction (e.g., duration of infectious disease and, by extension, primary infection vs. lessinfectious periods for HIV) to study patterns of disease transmission in hypothe-sized communities with given parameters. Such models rely, in part, on the somewhat tautological aspect of network logic for sexually transmitted infections or blood-borne infections among IDUs.…”

Section: Network and Health: Five Papers That Show Some Of The Promimentioning

confidence: 99%