Epidemic reemergence in adaptive complex networks

Abstract

The dynamic nature of system gives rise to dynamical features of epidemic spreading, such as oscillation and bistability. In this paper, by studying the epidemic spreading in growing networks, in which susceptible nodes may adaptively break the connections with infected ones yet avoid getting isolated, we reveal a new phenomenon -epidemic reemergence, where the number of infected nodes is incubated at a low level for a long time and then bursts up for a short time. The process may repeat several times before the infection finally vanishes. Simulation results show that all the three factors, namely the network growth, the connection breaking and the isolation avoidance, are necessary for epidemic reemergence to happen. We present a simple theoretical analysis to explain the process of reemergence in detail. Our study may offer some useful insights helping explain the phenomenon of repeated epidemic explosions.

Coupled disease–behavior dynamics on complex networks: A review

“…Besides the phenomena of oscillation and bistability, another dynamical feature, epidemic reemergence, also attracts great interest in a current study [122], where susceptible individuals adaptively break connections with infected neighbors yet avoid being isolated in a growing network. Under such operations, the authors observed that the number of infected agents stays at a low level for a long time, and then suddenly erupts to high level before declining to a low level again.…”

“…The probability of switching can be regarded as a measurement of strength of . We refer to [122], from where this figure has been adapted, for further details.…”

“…Example Refs social network ER network SF network multilayer networks awareness changing disease outbreak or (and) individual prevention measures [79,78,80,82,110,81,111,112,113] square lattice random network SF network urban network learning rules deciding disease status [85,84,93,117] square lattice ER network SF network adaptive network multilayer networks social network social/self-motivated protection mechanisms impacting disease status or (and) topology structure [124,79,130,128,125,126,129,133,83,87,89,91,115,116,122,127,132] time-varying network independent evolution of both disease and behavior processes [138,139] adaptive network multilayer networks social network topology properties determining disease status or (and) individual behavior [93,117,118,131,134,141,142] connection and dynamics process evolve according to their respective rules [135,136,137]. For example, Summin et al recently explored how to lower the number of vaccin...…”

“…Along this line of research, the most commonly used approach is adaptive networks [119,120,121,122], where nodes frequently adjust their connections according to the environment or states of neighboring nodes. Time-varying networks (also named temporal networks) provide another framework for the activity-driven changing of connection topology [31,123,32].…”

Statistical physics of vaccination

“…For example, susceptible people may break their contacts with infected partners, which can significantly alter the structure of the contact network, thus influencing the pathway of the epidemic spreading [52,[334][335][336][352][353][354][355]. Both SIS and SIR models have been studied on adaptive networks in which susceptible nodes rewire their links adaptively from infected neighbors towards other non-infected nodes [52,334].…”