Modelling disease outbreaks in realistic urban social networks

By Stephen G. Eubank, H. Guclu, V. S. Anil Kumar, Madhav V. Marathe, Aravind Srinivasan, Zoltan Toroczkai, Nan Wang. Nature, 2004

2,170 citations recorded by the source corpus.

Subfields: Modeling and Simulation, Public Health, Environmental and Occupational Health, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/1413dbfbbae1b59d52656db3dc48a4ee278e082f

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Citations in the corpus

Role of ventilation in airborne transmission of infectious agents in the built environment - a multidisciplinary systematic review. (2007, background): “Moreover, the dense contact networks that characterize urban cities form an ideal basis for rapid and uncontrolled disease propagation, especially by the airborne route of spread (Eubank et al., 2004; Weiss and McMichael, 2004).”
Coupled disease–behavior dynamics on complex networks: A review (2015, background): “Upon these empirically networks, mass diseasebehavior models can be considered to analyze the efficiency of existing or novel proposed prevention measures and provide constructive viewpoint for policy makers of public health [149] [150] [151] [152] [153] [154] .”
Statistical physics of vaccination (2016, methodology): “For example approaches informed with data from CDRs have been used to map and control the spreading of Malaria [762, 763], Ebola [764], and ILIs [250, 765].”
Decline in Mobility: Public Transport in Poland in the time of the COVID-19 Pandemic (2020, background): “Nevertheless, it is proved that social distancing measures are important in order to contain an emerging disease (Eubank et al. 2004).”
Human disease classification in the postgenomic era: A complex systems approach to human pathobiology (2007, methodology): “Network analysis has been used to characterize the spread of epidemics (Pastor-Satorras and Vespignani, 2001; Eubank et al, 2004; Madar et al, 2004) and to determine ways to control them (Pastor-Satorras and Vespignani, 2001; Dezso and Barabasi, 2002; Madar et al, 2004).”
Systems Science Methods in Public Health (2012, positive): “Agentbased models of epidemics have helped move epidemiology beyond the traditional S-I-R model and have demonstrated the importance of examining the role of social networks, transportation systems, local geography, and diverse behavioral responses to changing contexts on the spread of disease (45, ”
Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks (2009, background): “Yetalthoughmanystudiesmodelthedynamics ofviralspreadingbyusingassumptionsaboutsusceptibilityrates, transitionprobabilities,andtheirrelationshipstonetworkstructure, fewlarge-scaleempiricalobservationsofnetworkedcontagions existtovalidatetheseassumptions(16–18).”
Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees (2011, background): “Knowledge of contact patterns is crucial for building and informing computational models of infectious disease transmission [14-23].”
Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases (2008, methodology): “For instance, household size, class size, transport statistics, and workplace size distribution have been used in recent models to define the contact structure [2, 3, 33, 34] .”
Emerging pathogens: the epidemiology and evolution of species jumps (2005, neutral): “Recent work using agent-based simulation models points the way to the efficient design of surveillance systems based on an understanding of the contact network within the host population(s) [49] .”
Modelling transmission and control of the COVID-19 pandemic in Australia (2020, background): “Stochastic agent-based discrete-time simulation models such as these have been established as useful tools for tracing fine-grained effects of heterogeneous intervention policies in diverse epidemic and pandemic settings [18, 19, 10, 20, 9, 21, 22, 23, 24, 25] , including for policy advice currently”
Projecting social contact matrices in 152 countries using contact surveys and demographic data (2017, background): “Research in social networks has shown that transmissibility, and hence the effectiveness of many interventions, is determined by the intensity of human-to-human interactions [12] .”