Abstract
Epidemic spreading is a widely studied process due to its importance and possibly grave consequences for society. While the classical context of epidemic spreading refers to pathogens transmitted among humans or animals, it is straightforward to apply similar ideas to the spread of information (e.g., a rumor) or the spread of computer viruses. This paper addresses the question of how to optimally select nodes for monitoring in a network of timestamped contact events between individuals. We consider three optimization objectives: the detection likelihood, the time until detection, and the population that is affected by an outbreak. The optimization approach we use is based on a simple greedy approach and has been proposed in a seminal paper focusing on information spreading and water contamination. We extend this work to the setting of disease spreading and present its application with two example networks: a timestamped network of sexual contacts and a network of animal transports between farms. We apply the optimization procedure to a large set of outbreak scenarios that we generate with a susceptible-infectious-recovered model. We find that simple heuristic methods that select nodes with high degree or many contacts compare well in terms of outbreak detection performance with the (greedily) optimal set of nodes. Furthermore, we observe that nodes optimized on past periods may not be optimal for outbreak detection in future periods. However, seasonal effects may help in determining which past period generalizes well to some future period. Finally, we demonstrate that the detection performance depends on the simulation settings. In general, if we force the simulator to generate larger outbreaks, the detection performance will improve, as larger outbreaks tend to occur in the more connected part of the network where the top monitoring nodes are typically located. A natural progression of this work is to analyze how a representative set of outbreak scenarios can be generated, possibly taking into account more realistic propagation models.
Highlights
Spreading processes in networks have been extensively studied in the literature.Spreading can happen in a wide variety of contexts, including animal or human epidemic spreading (Rocha et al 2011; Bajardi et al 2012), computer virus spreading (Pastor-Satorras and Vespignani 2001), and misinformation spreading over social media (Budak et al 2011)
We were restricted to the first of the objectives proposed in Leskovec et al (2007), namely the optimization of the detection likelihood. We avoid this restriction by replicating the approach proposed by Leskovec et al By applying their method to two empirical timestamped contact networks and comparing the results with simple heuristic strategies that select nodes based on their centrality, we provide further empirical evidence for the greedy optimization method in the context of outbreak detection
We focus on the same three objectives as in Leskovec et al (2007): maximizing the detection likelihood, minimizing the time until detection, and minimizing the population affected by an outbreak
Summary
Spreading processes in networks have been extensively studied in the literature.Spreading can happen in a wide variety of contexts, including animal or human epidemic spreading (Rocha et al 2011; Bajardi et al 2012), computer virus spreading (Pastor-Satorras and Vespignani 2001), and misinformation spreading over social media (Budak et al 2011). We find that simple heuristic methods that select nodes with high degree or many contacts compare well in terms of outbreak detection performance with the (greedily) optimal set of nodes.
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