Epidemic Extinction and Control in Heterogeneous Networks

Hindes, Jason; Schwartz, Ira B.

doi:10.1103/physrevlett.117.028302

Cited by 77 publications

(106 citation statements)

References 26 publications

Supporting

Mentioning

105

Contrasting

Order By: Relevance

“…However, population with heterogeneous connected networks may take different paths to extinction and thus control of infection. A recent paper by Hindes and Schwartz (2016) discussed such issues on heterogeneous random networks under various configuration [272].…”

Section: Vaccinations Over Networkmentioning

confidence: 99%

Statistical physics of vaccination

et al. 2016

View full text Add to dashboard Cite

Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination-one of the most important preventive measures of modern times-is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.

show abstract

Section: Vaccinations Over Networkmentioning

confidence: 99%

Statistical physics of vaccination

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Also in the scope of infectious diseases in human contact networks, one expects a finite number of interaction links [69]. Moreover, there is an increasing number of studies that identify aspects of heterogeneous network topology that affect collective network dynamics in general [70][71][72], or specifically collective dynamics in neural networks [35,[73][74][75][76][77] and for infectious diseases [78][79][80]. We expect that the branching-process bias remains a general leading-order effect in heterogeneous network topologies.…”

Section: Discussionmentioning

confidence: 99%

Description of spreading dynamics by microscopic network models and macroscopic branching processes can differ due to coalescence

et al. 2020

View full text Add to dashboard Cite

Spreading processes are conventionally monitored on a macroscopic level by counting the number of incidences over time. The spreading process can then be modeled either on the microscopic level, assuming an underlying interaction network, or directly on the macroscopic level, assuming that microscopic contributions are negligible. The macroscopic characteristics of both descriptions are commonly assumed to be identical. In this work, we show that these characteristics of microscopic and macroscopic descriptions can be different due to coalescence, i.e., a node being activated at the same time by multiple sources. In particular, we consider a (microscopic) branching network (probabilistic cellular automaton) with annealed connectivity disorder, record the macroscopic activity, and then approximate this activity by a (macroscopic) branching process. In this framework, we analytically calculate the effect of coalescence on the collective dynamics. We show that coalescence leads to a universal non-linear scaling function for the conditional expectation value of successive network activity. This allows us to quantify the difference between the microscopic model parameter and established macroscopic estimates. To overcome this difference, we propose a non-linear estimator that correctly infers the model branching parameter for all system sizes.

show abstract

“…Our results are general for escape through a saddle. However, our methods can be further generalized to rare events induced by non-Gaussian noise in other dynamical processes in networks including: extinction [57,58], switching [59], and more general oscillator transitions [60,61].…”

Section: Discussionmentioning

confidence: 99%

Network desynchronization by non-Gaussian fluctuations

2019

Self Cite

View full text Add to dashboard Cite

Many networks must maintain synchrony despite the fact that they operate in noisy environments. Important examples are stochastic inertial oscillators, which are known to exhibit fluctuations with broad tails in many applications, including electric power networks with renewable energy sources. Such non-Gaussian fluctuations can result in rare network desynchronization. Here we build a general theory for inertial oscillator network desynchronization by non-Gaussian noise. We compute the rate of desynchronization and show that higher-moments of noise enter at specific powers of coupling: either speeding up or slowing down the rate exponentially depending on how noise statistics match the statistics of a network's slowest mode. Finally, we use our theory to introduce a technique that drastically reduces the effective description of network desynchronization. Most interestingly, when instability is associated with a single edge, the reduction is to one stochastic oscillator.

show abstract

Epidemic Extinction and Control in Heterogeneous Networks

Cited by 77 publications

References 26 publications

Statistical physics of vaccination

Statistical physics of vaccination

Description of spreading dynamics by microscopic network models and macroscopic branching processes can differ due to coalescence

Network desynchronization by non-Gaussian fluctuations

Contact Info

Product

Resources

About