Impact of spatially constrained sampling of temporal contact networks on the evaluation of the epidemic risk

Vestergaard, Christian; Valdano, Eugenio; Génois, Mathieu; Poletto, Chiara; Colizza, Vittoria; Barrat, Alain

doi:10.1017/s0956792516000309

Cited by 12 publications

(10 citation statements)

References 48 publications

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“…This strategy also had very good performance for estimation of the statistics of temporal paths and epidemics for all network data sets but the sexual contacts data set. These results likely explain the high performance of recently proposed methods to reconstruct temporal networks [55,56]. That is, the temporal patterns extracted from a small sample of the temporal network are sufficient to generate larger temporal networks with realistic temporal properties.…”

Section: Discussionmentioning

confidence: 83%

Sampling of temporal networks: Methods and biases

2017

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Temporal networks have been increasingly used to model a diversity of systems that evolve in time; for example, human contact structures over which dynamic processes such as epidemics take place. A fundamental aspect of real-life networks is that they are sampled within temporal and spatial frames. Furthermore, one might wish to subsample networks to reduce their size for better visualization or to perform computationally intensive simulations. The sampling method may affect the network structure and thus caution is necessary to generalize results based on samples. In this paper, we study four sampling strategies applied to a variety of real-life temporal networks. We quantify the biases generated by each sampling strategy on a number of relevant statistics such as link activity, temporal paths and epidemic spread. We find that some biases are common in a variety of networks and statistics, but one strategy, uniform sampling of nodes, shows improved performance in most scenarios. Given the particularities of temporal network data and the variety of network structures, we recommend that the choice of sampling methods be problem oriented to minimize the potential biases for the specific research questions on hand. Our results help researchers to better design network data collection protocols and to understand the limitations of sampled temporal network data.

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Section: Discussionmentioning

confidence: 83%

Sampling of temporal networks: Methods and biases

2017

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“…Another important application of dynamical systems on networks concerns the spread of biological contagions, as illustrated in the paper by Vestergaard et al [32], which examines the impact of spatially constrained sampling of temporal contact networks when evaluating the risk of epidemics. In addition to discussing a fascinating application, this paper considers several important issues in networks, such as data sampling (which is ubiquitous in the study of networks), time-dependent interactions, and spatial constraints.…”

Section: The Articles In This Issuementioning

confidence: 99%

Network analysis and modelling: Special issue ofEuropean Journal of Applied Mathematics

Porter

Bianconi²

2016

Eur. J. Appl. Math

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“…On the other hand, wearable sensors do not yield information on the type of behavioural interactions and they do not register contacts with individuals not wearing any sensor, such as very young individuals or members outside the group. The quality of the collected data might also depend on infrastructure constraints and potential technical failures, so that sampling issues must also be considered carefully [20,40].…”

Section: Introductionmentioning

confidence: 99%

Measuring social networks in primates: wearable sensors versus direct observations

et al. 2020

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Network analysis represents a valuable and flexible framework to understand the structure of individual interactions at the population level in animal societies. The versatility of network representations is moreover suited to different types of datasets describing these interactions. However, depending on the data collection method, different pictures of the social bonds between individuals could a priori emerge. Understanding how the data collection method influences the description of the social structure of a group is thus essential to assess the reliability of social studies based on different types of data. This is however rarely feasible, especially for animal groups, where data collection is often challenging. Here, we address this issue by comparing datasets of interactions between primates collected through two different methods: behavioural observations and wearable proximity sensors. We show that, although many directly observed interactions are not detected by the sensors, the global pictures obtained when aggregating the data to build interaction networks turn out to be remarkably similar. Moreover, sensor data yield a reliable social network over short time scales and can be used for long-term studies, showing their important potential for detailed studies of the evolution of animal social groups.

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Impact of spatially constrained sampling of temporal contact networks on the evaluation of the epidemic risk

Cited by 12 publications

References 48 publications

Sampling of temporal networks: Methods and biases

Sampling of temporal networks: Methods and biases

Network analysis and modelling: Special issue ofEuropean Journal of Applied Mathematics

Measuring social networks in primates: wearable sensors versus direct observations

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