Two months after it was firstly reported, the novel coronavirus disease COVID-19 spread worldwide. However, the vast majority of reported infections until February occurred in China. To assess the ...effect of early travel restrictions adopted by the health authorities in China, we have implemented an epidemic metapopulation model that is fed with mobility data corresponding to 2019 and 2020. This allows to compare two radically different scenarios, one with no travel restrictions and another in which mobility is reduced by a travel ban. Our findings indicate that i) travel restrictions might be an effective measure in the short term, however, ii) they are ineffective when it comes to completely eliminate the disease. The latter is due to the impossibility of removing the risk of seeding the disease to other regions. Furthermore, our study highlights the importance of developing more realistic models of behavioral changes when a disease outbreak is unfolding.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract
Many collective phenomena such as epidemic spreading and cascading failures in socioeconomic systems on networks are caused by perturbations of the dynamics. How perturbations propagate ...through networks, impact and disrupt their functions may depend on the network, the type and location of the perturbation as well as the spreading dynamics. Previous work has analyzed the retardation effects of the nodes along the propagation paths, suggesting a few transient propagation "scaling” regimes as a function of the nodes’ degree, but regardless of motifs such as triangles. Yet, empirical networks consist of motifs enabling the proper functioning of the system. Here, we show that basic motifs along the propagation path jointly determine the previously proposed scaling regimes of distance-limited propagation and degree-limited propagation, or even cease their existence. Our results suggest a radical departure from these scaling regimes and provide a deeper understanding of the interplay of self-dynamics, interaction dynamics, and topological properties.
Understanding how interurban movements can modify the spatial distribution of the population is important for transport planning but is also a fundamental ingredient for epidemic modeling. We ...illustrate this on vacation trips for all transportation modes in China during the Lunar New Year and compare the results for 2019 with the ones for 2020 where travel bans were applied for mitigating the spread of a novel coronavirus (COVID-19). We first show that inter-urban travel flows are broadly distributed and display both large temporal and spatial fluctuations, making their modeling very difficult. When flows are larger, they appear to be more dispersed over a larger number of origins and destinations, creating
de facto
hubs that can spread an epidemic at a large scale. These movements quickly induce (in about a week for this case) a very strong population concentration in a small set of cities. We characterize quantitatively the return to the initial distribution by defining a pendular ratio which allows us to show that this dynamics is in general very slow and even stopped for the 2020 Lunar New Year due to travel restrictions. Travel restrictions obviously limit the spread of the diseases between different cities, but have thus the counter-effect of keeping high concentration in a small set of cities,
a priori
favoring intra-city spread, unless individual contacts are strongly limited. These results shed some light on the statistics of interurban movements and how they modify the national distribution of populations, a crucial ingredient for devising effective control strategies at a national level.
Key network motifs searching in complex networks is one of the crucial aspects of network analysis. There has been a series of insightful findings and valuable applications for various scenarios ...through the analysis of network structures. However, in dynamic systems, slight changes in the choice of dynamic equations and parameters can alter the significance of motifs. The known methods are insufficient to address this issue effectively. In this paper, we introduce a concept of perturbation energy based on the system’s Jacobian matrix, and define motif centrality for dynamic systems by seamlessly integrating network topology with dynamic equations. Through simulations, we observe that the key motifs obtained by the proposed energy method present better effective and accurate than them by integrating network topology methods, without significantly increasing algorithm complexity. The finding of key motifs can be used to apply for system control, such as formulating containment policies for the spread of epidemics and protecting fragile ecosystems. Additionally, it makes substantial contribution to a deeper understanding of concepts in physics, such as signal propagation and system’s stability.
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•A new concept of perturbation energy is proposed for various perturbations.•A new motif centrality based on dynamical system and network structure is proposed.•The proposed ranking algorithm is applied to spreading diseases, ecosystems, etc.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract
In the real world, many dynamic behaviors can be explained by the propagation of perturbations, such as the transfer of chemical signals and the spread of infectious diseases. Previous ...researchers have achieved excellent results in approximating the global propagation time, revealing the mechanism of signal propagation through multiple paths. However, the known frameworks rely on the extension of physical concepts rather than mathematically rigorous derivations. As a result, they may not perfectly predict time or explain the underlying physical significance in certain specific cases. In this paper, we propose a novel method for decomposing network topology, focusing on two modules: the tree-like module and the path-module. Subsequently, we introduce a new framework for signal propagation analysis, which can be applied to estimate the propagation time for two fundamental global topology modules and provide a rigorous proof for the propagation time in global topology. Compared to previous work, our results are not only more concise, clearly defined, efficient, but also are more powerful in predicting propagation time which outperforms some known results in some cases, for example, biochemical dynamics.Additionally, the proposed framework is based on information transfer pathways, which can be also applied to other physical fields, such as network stability, network controlling and network resilience.
Various disasters stem from minor perturbations, such as the spread of
infectious diseases, cascading failure in power grids, etc. Analyzing
perturbations is crucial for both theoretical and ...application fields. Previous
researchers have proposed basic propagation patterns for perturbation and
explored the impact of basic network motifs on the collective response to these
perturbations, However, the current framework is limited in its ability to
decouple interactions, and therefore cannot analyze more complex structures. In
this article, we establish an effective, robust and powerful propagation
framework under a general dynamic model. This framework reveals common and
dense network motifs that exert a critical influence on signal propagation,
often spanning orders of magnitude compared with conclusions generated by
previous work. Moreover, our framework provides a new approach to understand
the fundamental principles of complex systems and the negative feedback
mechanism, which is of great significance for research of system controlling
and network resilience.
Key network motifs searching in complex networks is one of the crucial aspects of network analysis. There has been a series of insightful findings and valuable applications for various scenarios ...through the analysis of network structures. However, in dynamic systems, slight changes in the choice of dynamic equations and parameters can alter the significance of motifs. The known methods are insufficient to address this issue effectively. In this paper, we introduce a concept of perturbation energy based on the system's Jacobian matrix, and define motif centrality for dynamic systems by seamlessly integrating network topology with dynamic equations. Through simulations, we observe that the key motifs obtained by the proposed energy method present better effective and accurate than them by integrating network topology methods, without significantly increasing algorithm complexity. The finding of key motifs can be used to apply for system control, such as formulating containment policies for the spread of epidemics and protecting fragile ecosystems. Additionally, it makes substantial contribution to a deeper understanding of concepts in physics, such as signal propagation and system's stability.
Understanding how interurban movements can modify the spatial distribution of the population is important for transport planning but is also a fundamental ingredient for epidemic modeling. We focus ...here on vacation trips (for all transportation modes) during the Chinese Lunar New Year and compare the results for 2019 with the ones for 2020 where travel bans were applied for mitigating the spread of a novel coronavirus (COVID-19). We first show that these travel flows are broadly distributed and display both large temporal and spatial fluctuations, making their modeling very difficult. When flows are larger, they appear to be more dispersed over a larger number of origins and destinations, creating de facto hubs that can spread an epidemic at a large scale. These movements quickly induce (in about a week) a very strong population concentration in a small set of cities. We characterize quantitatively the return to the initial distribution by defining a pendular ratio which allows us to show that this dynamics is very slow and even stopped for the 2020 Lunar New Year due to travel restrictions. Travel restrictions obviously limit the spread of the diseases between different cities, but have thus the counter-effect of keeping high concentration in a small set of cities, a priori favoring intra-city spread, unless individual contacts are strongly limited. These results shed some light on how interurban movements modify the national distribution of populations, a crucial ingredient for devising effective control strategies at a national level.
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