Containing epidemic outbreaks by message-passing techniques (original) (raw)
Related papers
Optimal vaccine allocation to control epidemic outbreaks in arbitrary networks
52nd IEEE Conference on Decision and Control, 2013
We consider the problem of controlling the propagation of an epidemic outbreak in an arbitrary contact network by distributing vaccination resources throughout the network. We analyze a networked version of the Susceptible-Infected-Susceptible (SIS) epidemic model when individuals in the network present different levels of susceptibility to the epidemic. In this context, controlling the spread of an epidemic outbreak can be written as a spectral condition involving the eigenvalues of a matrix that depends on the network structure and the parameters of the model. We study the problem of finding the optimal distribution of vaccines throughout the network to control the spread of an epidemic outbreak. We propose a convex framework to find cost-optimal distribution of vaccination resources when different levels of vaccination are allowed. We also propose a greedy approach with quality guarantees for the case of all-or-nothing vaccination. We illustrate our approaches with numerical simulations in a real social network.
2018
Epidemic propagation is controlled conventionally by vaccination or by quarantine. These methods have been widely applied for different compartmental ODE models of epidemic propagation. When epidemic spread is considered on a network, then it is natural to control the propagation process by changing the network structure. Namely, SI links, connecting a susceptible individual to an infected one, can be deleted. This would lead to a disconnected network, which is not realistic, hence new SS links can be created in order to keep the network well connected. Thus it seems to be promising to drive the process to a target with no infection and a prescribed average degree by deleting SI links and creating SS links in an appropriate way. It was shown previously that this can be done for the pairwise ODE approximation of SIS epidemic propagation. In this paper this is extended to the original stochastic process by using the control signals computed from the ODE approximation.
Optimising control of disease spread on networks
Acta Physica Polonica B, 2005
We consider models for control of epidemics on local, global, small-world and scale-free networks, with only partial information accessible about the status of individuals and their connections. The effectiveness of local (e.g. ring vaccination or culling) vs global (e.g. random vaccination) control measures is evaluated, with the aim of minimising the total cost of an epidemic. The costs include direct costs of treating infected individuals as well as costs of treatment. We first consider a random (global) vaccination strategy designed to stop any potential outbreak. We show that if the costs of the preventive vaccination are ignored, the optimal strategy is to vaccinate the whole population, although most of the resources are wasted on preventing a small number of cases. If the vaccination costs are included, or if a local strategy (within a certain neighbourhood of a symptomatic individual) is chosen, there is an optimum number of treated individuals. Inclusion of non-local contacts ('small-worlds' or scale-free networks) increases the levels of preventive (random) vaccination and radius of local treatment necessary for stopping the outbreak at a minimal cost. The number of initial foci also influences our choice of optimal strategy. The size of epidemics and the number of treated individuals increase for outbreaks that are initiated from a larger number of initial foci, but the optimal radius of local control actually decreases. The results are important for designing control strategies based on cost effectiveness.
Optimal resource allocation for containing epidemics on time-varying networks
2015 49th Asilomar Conference on Signals, Systems and Computers, 2015
This paper studies the Susceptible-Infected-Susceptible (SIS) epidemic model on time-varying interaction graphs in contrast to the majority of other works which only consider static graphs. After presenting the mean-field model and characterizing its stability properties, we formulate and solve an optimal resource allocation problem. More specifically, we first assume that a cost can be paid to reduce the amount of interactions certain nodes can have with others (e.g., by imposing travel restrictions between certain cities). Then, given a budget, we are interested in optimally allocating the budget to best combat the undesired epidemic. We show how this problem can be equivalently formulated as a geometric program and solved in polynomial time. Simulations illustrate our results.
Local immunization program for susceptible-infected-recovered network epidemic model
Chaos (Woodbury, N.Y.), 2016
The immunization strategies through contact tracing on the susceptible-infected-recovered framework in social networks are modelled to evaluate the cost-effectiveness of information-based vaccination programs with particular focus on the scenario where individuals belonging to a specific set can get vaccinated due to the vaccine shortages and other economic or humanity constraints. By using the block heterogeneous mean-field approach, a series of discrete-time dynamical models is formulated and the condition for epidemic outbreaks can be established which is shown to be not only dependent on the network structure but also closely related to the immunization control parameters. Results show that increasing the immunization strength can effectively raise the epidemic threshold, which is different from the predictions obtained through the susceptible-infected-susceptible network framework, where epidemic threshold is independent of the vaccination strength. Furthermore, a significant d...
Vaccination allocation in large dynamic networks
Journal of Big Data, 2017
Event propagation over a network is a complex and frequently studied human phenomena . Historical examples of information exchange between humans in a social network, past and present, can be seen during colonial expansion of the British Empire [4], memes passed between friends on any assortment of social networks on the internet [1] and transmission of human or animal pathogens such as the virus H1N1 over airways . In the information age computer networks can mirror these types of information exchange, with event information being passed along from node to node when network neighbors communicate through various network protocols .
Suppression of epidemic spreading process on multiplex networks via active immunization
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2019
Spatial epidemic spreading, a fundamental dynamical process upon complex networks, attracts huge research interest during the past few decades. To suppress the spreading of epidemic, a couple of e ective methods have been proposed, including node vaccination. Under such a scenario, nodes are immunized passively and fail to reveal the mechanisms of active activity. Here, we suggest one novel model of an observer node, which can identify infection through interacting with infected neighbors and inform the other neighbors for vaccination, on multiplex networks, consisting of epidemic spreading layer and information spreading layer. In detail, the epidemic spreading layer supports susceptibleinfected-recovered process, while observer nodes will be selected according to several algorithms derived from percolation theory. Numerical simulation results show that the algorithm based on large degree performs better than random placement, while the algorithm based on nodes' degree in the information spreading layer performs the best (i.e., the best suppression e cacy is guaranteed when placing observer nodes based on nodes' degree in the information spreading layer). With the help of state probability transition equation, the above phenomena can be validated accurately. Our work thus may shed new light into understanding control of empirical epidemic control.
An Efficient Immunization Strategy Using Overlapping Nodes and Its Neighborhoods
Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18, 2018
When an epidemic occurs, it is often impossible to vaccinate the entire population due to limited amount of resources. Therefore, it is of prime interest to identify the set of influential spreaders to immunize, in order to minimize both the cost of vaccine resource and the disease spreading. While various strategies based on the network topology have been introduced, few works consider the influence of the community structure in the epidemic spreading process. Nowadays, it is clear that many real-world networks exhibit an overlapping community structure, in which nodes are allowed to belong to more than one community. Previous work shows that the numbers of communities to which a node belongs is a good measure of its epidemic influence. In this work, we address the effect of nodes in the neighborhood of the overlapping nodes on epidemics spreading. The proposed immunization strategy provides highly connected neighbors of overlapping nodes in the network to immunize. The whole process requires information only at the node level and is well suited to large-scale networks. Extensive experiments on four real-world networks of diverse nature have been performed. Comparisons with alternative local immunization strategies using the fraction of the Largest Connected Component (LCC) after immunization,show that the proposed method is much more efficient. Additionally, it compares favorably to global measures such as degree and betweenness centrality.
Immunization against Infection Propagation in Heterogeneous Networks
2014 IEEE 13th International Symposium on Network Computing and Applications, 2014
Modeling spreading processes for infections has been a widely researched area owing to its application in variety of domains especially epidemic spread and worm propagation. Until recently, infection propagation models usually inspired by epidemic spreading, solely relied upon the underlying network properties without taking into account the variation in node specific properties, such as its ability to spread infection or recover from an infection. Owing to this fact, these models have been agnostic to the effects such node heterogeneity might have in the overall infection (or immunization) process. In this paper, we incorporate node properties in a well-known Susceptible Infected Recovered Susceptible (SIRS) model for infection propagation, and propose new heuristics to curb the spread of infection in heterogeneous networks. The proposed heuristics are validated against various network topologies, including a real-world example of an email exchange network.