scholarly journals Heterogeneous node responses to multi-type epidemics on networks

Author(s):  
S. Moore ◽  
T. Rogers

Having knowledge of the contact network over which an infection is spreading opens the possibility of making individualized predictions for the likelihood of different nodes to become infected. When multiple infective strains attempt to spread simultaneously we may further ask which strain, or strains, are most likely to infect a particular node. In this article we investigate the heterogeneity in likely outcomes for different nodes in two models of multi-type epidemic spreading processes. For models allowing co-infection we derive message-passing equations whose solution captures how the likelihood of a given node receiving a particular infection depends on both the position of the node in the network and the interaction between the infection types. For models of competing epidemics in which co-infection is impossible, a more complicated analysis leads to the simpler result that node vulnerability factorizes into a contribution from the network topology and a contribution from the infection parameters.

Author(s):  
Gerrit Großmann ◽  
Michael Backenköhler ◽  
Verena Wolf

AbstractIn the recent COVID-19 pandemic, computer simulations are used to predict the evolution of the virus propagation and to evaluate the prospective effectiveness of non-pharmaceutical interventions. As such, the corresponding mathematical models and their simulations are central tools to guide political decision-making. Typically, ODE-based models are considered, in which fractions of infected and healthy individuals change deterministically and continuously over time.In this work, we translate an ODE-based COVID-19 spreading model from literature to a stochastic multi-agent system and use a contact network to mimic complex interaction structures. We observe a large dependency of the epidemic’s dynamics on the structure of the underlying contact graph, which is not adequately captured by existing ODE-models. For instance, existence of super-spreaders leads to a higher infection peak but a lower death toll compared to interaction structures without super-spreaders. Overall, we observe that the interaction structure has a crucial impact on the spreading dynamics, which exceeds the effects of other parameters such as the basic reproduction number R0. We conclude that deterministic models fitted to COVID-19 outbreak data have limited predictive power or may even lead to wrong conclusions while stochastic models taking interaction structure into account offer different and probably more realistic epidemiological insights.


2019 ◽  
Vol 33 (09) ◽  
pp. 1950069 ◽  
Author(s):  
Bo Song ◽  
Yu-Rong Song ◽  
Guo-Ping Jiang ◽  
Ling-Ling Xia

When virus spreads in a network, individuals may adaptively change their contact relationships to reduce risk of infection. In order to describe the interaction between a time varying network topology and the dynamics of the nodes, here we propose a susceptible–infected–susceptible (SIS) model on a weighted adaptive heterogeneous network where the dynamics of and on the networks are described. Rewiring strategies are designed to inhibit the epidemic spreading. Our results show that network topology and individual behaviors have significant influence on virus spreading in a weighted adaptive heterogeneous network. The adaptive rewiring process during the dynamics can effectively inhibit virus spreading. Large dispersion of weights can significantly decrease the final epidemic size. Moreover, our proposed rewiring strategies based on the individual spontaneous behaviors are effective for the inhibition of epidemics.


2013 ◽  
Vol 325 ◽  
pp. 12-21 ◽  
Author(s):  
Junling Ma ◽  
P. van den Driessche ◽  
Frederick H. Willeboordse

2017 ◽  
Vol 114 (39) ◽  
pp. E8138-E8146 ◽  
Author(s):  
Andrey Y. Lokhov ◽  
David Saad

The effective use of limited resources for controlling spreading processes on networks is of prime significance in diverse contexts, ranging from the identification of “influential spreaders” for maximizing information dissemination and targeted interventions in regulatory networks, to the development of mitigation policies for infectious diseases and financial contagion in economic systems. Solutions for these optimization tasks that are based purely on topological arguments are not fully satisfactory; in realistic settings, the problem is often characterized by heterogeneous interactions and requires interventions in a dynamic fashion over a finite time window via a restricted set of controllable nodes. The optimal distribution of available resources hence results from an interplay between network topology and spreading dynamics. We show how these problems can be addressed as particular instances of a universal analytical framework based on a scalable dynamic message-passing approach and demonstrate the efficacy of the method on a variety of real-world examples.


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