Dataset Information

General methodology for inferring failure-spreading dynamics in networks.

ABSTRACT: A generic modeling framework to infer the failure-spreading process based on failure times of individual nodes is proposed and tested in four simulation studies: one for cascading failures in interdependent power and transportation networks, one for influenza epidemics, one benchmark test case for congestion cascade in a transportation network, and one benchmark test case for cascading power outages. Four general failure-spreading mechanisms-external, temporal, spatial, and functional-are quantified to capture what drives the spreading of failures. With the failure time of each node given, the proposed methodology demonstrates remarkable capability of inferring the underlying general failure-spreading mechanisms and accurately reconstructing the failure-spreading process in all four simulation studies. The analysis of the two benchmark test cases also reveals the robustness of the proposed methodology: It is shown that a failure-spreading process embedded by specific failure-spreading mechanisms such as flow redistribution can be captured with low uncertainty by our model. The proposed methodology thereby presents a promising channel for providing a generally applicable framework for modeling, understanding, and controlling failure spreading in a variety of systems.

SUBMITTER: Guan X

PROVIDER: S-EPMC6126715 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

General methodology for inferring failure-spreading dynamics in networks.

Guan Xiangyang X Chen Cynthia C

Proceedings of the National Academy of Sciences of the United States of America 20180815 35

A generic modeling framework to infer the failure-spreading process based on failure times of individual nodes is proposed and tested in four simulation studies: one for cascading failures in interdependent power and transportation networks, one for influenza epidemics, one benchmark test case for congestion cascade in a transportation network, and one benchmark test case for cascading power outages. Four general failure-spreading mechanisms-external, temporal, spatial, and functional-are quanti ...[more]

PMID: 30111540

Similar Datasets

Project description:BackgroundSeveral studies have focused on stratifying patients according to their level of readmission risk, fueled in part by incentive programs in the U.S. that link readmission rates to the annual payment update by Medicare. Patient-specific predictions about readmission have not seen widespread use because of their limited accuracy and questions about the efficacy of using measures of risk to guide clinical decisions. We construct a predictive model for readmissions for congestive heart failure (CHF) and study how its predictions can be used to perform patient-specific interventions. We assess the cost-effectiveness of a methodology that combines prediction and decision making to allocate interventions. The results highlight the importance of combining predictions with decision analysis.MethodsWe construct a statistical classifier from a retrospective database of 793 hospital visits for heart failure that predicts the likelihood that patients will be rehospitalized within 30 days of discharge. We introduce a decision analysis that uses the predictions to guide decisions about post-discharge interventions. We perform a cost-effectiveness analysis of 379 additional hospital visits that were not included in either the formulation of the classifiers or the decision analysis. We report the performance of the methodology and show the overall expected value of employing a real-time decision system.FindingsFor the cohort studied, readmissions are associated with a mean cost of $13,679 with a standard error of $1,214. Given a post-discharge plan that costs $1,300 and that reduces 30-day rehospitalizations by 35%, use of the proposed methods would provide an 18.2% reduction in rehospitalizations and save 3.8% of costs.ConclusionsClassifiers learned automatically from patient data can be joined with decision analysis to guide the allocation of post-discharge support to CHF patients. Such analyses are especially valuable in the common situation where it is not economically feasible to provide programs to all patients.

Project description:The detection of causal interactions is of great importance when inferring complex ecosystem functional and structural networks for basic and applied research. Convergent cross mapping (CCM) based on nonlinear state-space reconstruction made substantial progress about network inference by measuring how well historical values of one variable can reliably estimate states of other variables. Here we investigate the ability of a developed optimal information flow (OIF) ecosystem model to infer bidirectional causality and compare that to CCM. Results from synthetic datasets generated by a simple predator-prey model, data of a real-world sardine-anchovy-temperature system and of a multispecies fish ecosystem highlight that the proposed OIF performs better than CCM to predict population and community patterns. Specifically, OIF provides a larger gradient of inferred interactions, higher point-value accuracy and smaller fluctuations of interactions and [Formula: see text]-diversity including their characteristic time delays. We propose an optimal threshold on inferred interactions that maximize accuracy in predicting fluctuations of effective [Formula: see text]-diversity, defined as the count of model-inferred interacting species. Overall OIF outperforms all other models in assessing predictive causality (also in terms of computational complexity) due to the explicit consideration of synchronization, divergence and diversity of events that define model sensitivity, uncertainty and complexity. Thus, OIF offers a broad ecological information by extracting predictive causal networks of complex ecosystems from time-series data in the space-time continuum. The accurate inference of species interactions at any biological scale of organization is highly valuable because it allows to predict biodiversity changes, for instance as a function of climate and other anthropogenic stressors. This has practical implications for defining optimal ecosystem management and design, such as fish stock prioritization and delineation of marine protected areas based on derived collective multispecies assembly. OIF can be applied to any complex system and used for model evaluation and design where causality should be considered as non-linear predictability of diverse events of populations or communities.

Dataset Information

General methodology for inferring failure-spreading dynamics in networks.

Publications

General methodology for inferring failure-spreading dynamics in networks.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets