Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees

By Juliette Stehlé, N. Voirin, Alain Barrat, Ciro Cattuto, Vittoria Colizza, Lorenzo Isella, C. Régis, Jean-François Pinton, N. Khanafer, W. van den Broeck and 1 more. BMC Medicine, 2011

397 citations recorded by the source corpus.

BackgroundThe spread of infectious diseases crucially depends on the pattern of contacts between individuals. Knowledge of these patterns is thus essential to inform models and computational efforts. However, there are few empirical studies available that provide estimates of the number and duration of contacts between social groups. Moreover, their space and time resolutions are limited, so that data are not explicit at the person-to-person level, and the dynamic nature of the contacts is disregarded. In this study, we aimed to assess the role of data-driven dynamic contact patterns between individuals, and in particular of their temporal aspects, in shaping the spread of a simulated epidemic in the population.MethodsWe considered high-resolution data about face-to-face interactions between the attendees at a conference, obtained from the deployment of an infrastructure based on radiofrequency identification (RFID) devices that assessed mutual face-to-face proximity. The spread of epidemics along these interactions was simulated using an SEIR (Susceptible, Exposed, Infectious, Recovered) model, using both the dynamic network of contacts defined by the collected data, and two aggregated versions of such networks, to assess the role of the data temporal aspects.ResultsWe show that, on the timescales considered, an aggregated network taking into account the daily duration of contacts is a good approximation to the full resolution network, whereas a homogeneous representation that retains only the topology of the contact network fails to reproduce the size of the epidemic.ConclusionsThese results have important implications for understanding the level of detail needed to correctly inform computational models for the study and management of real epidemics.Please see related article BMC Medicine, 2011, 9:88

Subfields: Epidemiology, Modeling and Simulation, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/fb3587b7abca589772b4646544e87e38a4c4fa78

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Citations in the corpus

Contact Patterns among High School Students (2014, background): “The narrow character of the degree distribution of empirical human contacts has been observed as well in other contexts [15,21,22,24].”
Projecting social contact matrices in 152 countries using contact surveys and demographic data (2017, background): “Triadic contacts—where A contacts B contacts C contacts A—are important for disease propagation in contact network models [12,61] and other methods to measure these, such as radio frequency tagging [61,62], or more sophisticated structural models accounting temporal presence within the household [63”
The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study (2020, neutral): “Distinguishing between repeated and new contacts is important for disease propagation in contact network models; 35,36 more sophisticated methods that account for temporal presence within the household 37 would be needed to characterise higher degrees of contact.”
Modeling the epidemic dynamics and control of COVID-19 outbreak in China (2020, background): “COVID-19 has three features that make it hard to describe with the existing epidemic models including SIR, SEIR etc [7, 8] .”
Digital Epidemiology (2012, background): “Models based on recorded sequences of human contacts can inform the design of containment measures and of targeted immunization strategies [27] and marks an important departure from the static representation of contact networks [28].”