scholarly journals Transient indicators of tipping points in infectious diseases

2020 ◽  
Vol 17 (170) ◽  
pp. 20200094
Author(s):  
Suzanne M. O’Regan ◽  
Eamon B. O’Dea ◽  
Pejman Rohani ◽  
John M. Drake
Keyword(s):  
Infectious Diseases ◽  
Vaccine Uptake ◽  
Critical Slowing Down ◽  
Critical Threshold ◽  
Continuous Systems ◽  
Dominant Eigenvalue ◽  
Tipping Points ◽  
Critical Transition ◽  
Slowing Down ◽  
Critical Transitions

The majority of known early warning indicators of critical transitions rely on asymptotic resilience and critical slowing down. In continuous systems, critical slowing down is mathematically described by a decrease in magnitude of the dominant eigenvalue of the Jacobian matrix on the approach to a critical transition. Here, we show that measures of transient dynamics, specifically, reactivity and the maximum of the amplification envelope, also change systematically as a bifurcation is approached in an important class of models for epidemics of infectious diseases. Furthermore, we introduce indicators designed to detect trends in these measures and find that they reliably classify time series of case notifications simulated from stochastic models according to levels of vaccine uptake. Greater attention should be focused on the potential for systems to exhibit transient amplification of perturbations as a critical threshold is approached, and should be considered when searching for generic leading indicators of tipping points. Awareness of this phenomenon will enrich understanding of the dynamics of complex systems on the verge of a critical transition.

scholarly journals Critical slowing down associated with critical transition and risk of collapse in crypto-currency

2020 ◽  
Vol 7 (3) ◽  
pp. 191450 ◽  
Author(s):  
Chengyi Tu ◽  
Paolo D'Odorico ◽  
Samir Suweis
Keyword(s):  
Standard Deviation ◽  
Mean Field ◽  
Window Size ◽  
High Variability ◽  
Critical Slowing Down ◽  
Critical Transition ◽  
Slowing Down ◽  
Rolling Window ◽  
Stochastic Transition ◽  
Crypto Currency

The year 2017 saw the rise and fall of the crypto-currency market, followed by high variability in the price of all crypto-currencies. In this work, we study the abrupt transition in crypto-currency residuals, which is associated with the critical transition (the phenomenon of critical slowing down) or the stochastic transition phenomena. We find that, regardless of the specific crypto-currency or rolling window size, the autocorrelation always fluctuates around a high value, while the standard deviation increases monotonically. Therefore, while the autocorrelation does not display the signals of critical slowing down, the standard deviation can be used to anticipate critical or stochastic transitions. In particular, we have detected two sudden jumps in the standard deviation, in the second quarter of 2017 and at the beginning of 2018, which could have served as the early warning signals of two major price collapses that have happened in the following periods. We finally propose a mean-field phenomenological model for the price of crypto-currency to show how the use of the standard deviation of the residuals is a better leading indicator of the collapse in price than the time-series' autocorrelation. Our findings represent a first step towards a better diagnostic of the risk of critical transition in the price and/or volume of crypto-currencies.

scholarly journals Resilience indicators: prospects and limitations for early warnings of regime shifts

2015 ◽  
Vol 370 (1659) ◽  
pp. 20130263 ◽  
Author(s):  
Vasilis Dakos ◽  
Stephen R. Carpenter ◽  
Egbert H. van Nes ◽  
Marten Scheffer
Keyword(s):  
Early Warning ◽  
Temporal Correlation ◽  
Warning Signal ◽  
Regime Shifts ◽  
Tipping Points ◽  
Small Perturbations ◽  
Slowing Down ◽  
Critical Transitions ◽  
Wide Range ◽  
Resilience Indicators

In the vicinity of tipping points—or more precisely bifurcation points—ecosystems recover slowly from small perturbations. Such slowness may be interpreted as a sign of low resilience in the sense that the ecosystem could easily be tipped through a critical transition into a contrasting state. Indicators of this phenomenon of ‘critical slowing down (CSD)’ include a rise in temporal correlation and variance. Such indicators of CSD can provide an early warning signal of a nearby tipping point. Or, they may offer a possibility to rank reefs, lakes or other ecosystems according to their resilience. The fact that CSD may happen across a wide range of complex ecosystems close to tipping points implies a powerful generality. However, indicators of CSD are not manifested in all cases where regime shifts occur. This is because not all regime shifts are associated with tipping points. Here, we review the exploding literature about this issue to provide guidance on what to expect and what not to expect when it comes to the CSD-based early warning signals for critical transitions.

Re-thinking noise-induced tipping

2021 ◽  
Author(s):  
Julian Newman ◽  
Peter Ashwin
Keyword(s):  
Early Warning ◽  
Gaussian White Noise ◽  
External Noise ◽  
Critical Slowing Down ◽  
Critical Threshold ◽  
Geophysical Fluid Dynamics ◽  
Internal Dynamics ◽  
Slowing Down ◽  
The Earth ◽  
Joint Dynamics

<p>When modelling potential tipping elements of the earth system, one conventionally distinguishes "bifurcation-induced" and "noise-induced" tipping. The former occurs when an internal system parameter slowly crosses a critical threshold and external noise is negligible. The latter arises from forcing by noise well before a critical threshold for the internal dynamics is reached. The former comes with early warning signals, due to "critical slowing down" in the internal dynamics; but the latter occurs randomly without warning. However, these descriptions typically assume that the noise is Gaussian white noise, which arises as a limit of fast-timescale chaotic driving. We will instead consider, through a simple discrete-time prototype, finite-timescale bounded chaotic driving; this is a more suitable description of the subgrid forcing of turbulent geophysical fluid dynamics than uncorrelated noise. We will see that the phenomenon previously known as "noise-induced tipping" now corresponds to a deterministic bifurcation-induced tipping of the joint dynamics of the tipping element and the driving. Although "critical slowing down" does not occur in this bifurcation, early warning and near-exact prediction of the tipping event may still be possible. We also discuss the phenomenon of "noise-induced" prevention or delay of a tipping event, which cannot occur under conventional memoryless noise.</p>

scholarly journals Anticipating critical transitions in epithelial-hybrid-mesenchymal cell-fate determination

2019 ◽  
Author(s):  
Sukanta Sarkar ◽  
Sudipta Kumar Sinha ◽  
Herbert Levine ◽  
Mohit Kumar Jolly ◽  
Partha Sharathi Dutta
Keyword(s):  
Drug Resistance ◽  
Cell Fate ◽  
Early Warning ◽  
Cancer Metastasis ◽  
Critical Slowing Down ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Slowing Down ◽  
Tumor Relapse ◽  
Critical Transitions

AbstractIn the vicinity of a tipping point, critical transitions occur when small changes in an input condition causes sudden, large and often irreversible changes in the state of a system. Many natural systems ranging from ecosystems to molecular biosystems are known to exhibit critical transitions in their response to stochastic perturbations. In diseases, an early prediction of upcoming critical transitions from a healthy to a disease state by using early warning signals is of prime interest due to potential application in forecasting disease onset. Here, we analyze cell-fate transitions between different phenotypes (epithelial, hybrid epithelial/mesenchymal (E/M) and mesenchymal states) that are implicated in cancer metastasis and chemoresistance. These transitions are mediated by a mutually inhibitory feedback loop microRNA-200/ZEB driven by the levels of transcription factor SNAIL. We find that the proximity to tipping points enabling these transitions among different phenotypes can be captured by critical slowing down based early warning signals, calculated from the trajectory of ZEB mRNA level. Further, the basin stability analysis reveals the unexpectedly large basin of attraction for a hybrid E/M phenotype. Finally, we identified mechanisms that can potentially elude the transition to a hybrid E/M phenotype. Overall, our results unravel the early warning signals that can be used to anticipate upcoming epithelial-hybrid-mesenchymal transitions. With the emerging evidence about the hybrid E/M phenotype being a key driver of metastasis, drug resistance, and tumor relapse; our results suggest ways to potentially evade these transitions, reducing the fitness of cancer cells and restricting tumor aggressiveness.Significance StatementEpithelial-hybrid-mesenchymal transitions play critical roles in cancer metastasis, drug resistance, and tumor relapse. Recent studies have proposed that cells in a hybrid epithelial/mesenchymal phenotype may be more aggressive than those on either end of the spectrum. However, no biomarker to predict upcoming transitions has been identified. Here, we show that critical slowing down based early warning signals can detect sudden transitions among epithelial, hybrid E/M, and mesenchymal phenotypes. Importantly, our results highlight how stable a hybrid E/M phenotype can be, and how can a transition to this state be avoided. Thus, our study provides valuable insights into restricting cellular plasticity en route metastasis.

scholarly journals Lack of Critical Slowing Down Suggests that Financial Meltdowns Are Not Critical Transitions, yet Rising Variability Could Signal Systemic Risk

PLoS ONE ◽  
2016 ◽  
Vol 11 (1) ◽  
pp. e0144198 ◽  
Author(s):  
Vishwesha Guttal ◽  
Srinivas Raghavendra ◽  
Nikunj Goel ◽  
Quentin Hoarau
Keyword(s):  
Systemic Risk ◽  
Critical Slowing Down ◽  
Slowing Down ◽  
Critical Transitions

scholarly journals Anticipating Critical Transitions in Psychological Systems using Early Warning Signals: Theoretical and Practical Considerations

2020 ◽  
Author(s):  
Fabian Dablander ◽  
Anton Pichler ◽  
Arta Cika ◽  
Andrea Bacilieri
Keyword(s):  
Early Warning ◽  
Stable State ◽  
Critical Slowing Down ◽  
Future Research ◽  
Frequency Noise ◽  
Alternative Stable State ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Slowing Down ◽  
Critical Transitions

Many real-world systems can exhibit sudden shifts from one stable state to another, and the theory of dynamical systems points to the existence of generic early warning signals that precede such shifts. Recently, psychologists have begun to conceptualize mental disorders such as depression as an alternative stable state, and suggested that early warning signals based on the phenomenon of critical slowing down might be useful for predicting sudden transitions into depression or other psychiatric disorders. Harnessing the potential of early warning signals requires us to understand their limitations as well as the factors influencing their performance in practice. In this paper, we (a) review limitations of early warning signals based on critical slowing down to better understand when they can and cannot occur, and (b) study the conditions under which early warning signals may anticipate critical transitions in online-monitoring settings by simulating from a bistable dynamical system, varying crucial features such as sampling frequency, noise intensity, and speed of approaching the tipping point. We find that, in sharp contrast to their reputation of being generic or model-agnostic, whether early warning signals occur or not strongly depends on the specifics of the system. We also find that they are very sensitive to noise, potentially limiting their utility in practical applications. We discuss the implications of our findings and provide suggestions and recommendations for future research.

scholarly journals Critical dynamics in population vaccinating behavior

2017 ◽  
Vol 114 (52) ◽  
pp. 13762-13767 ◽  
Author(s):  
A. Demetri Pananos ◽  
Thomas M. Bury ◽  
Clara Wang ◽  
Justin Schonfeld ◽  
Sharada P. Mohanty ◽  
...  
Keyword(s):  
Critical Phenomenon ◽  
Coupled System ◽  
The United States ◽  
Tipping Point ◽  
Machine Learning Algorithms ◽  
Vaccine Uptake ◽  
Critical Slowing Down ◽  
Dynamical Regime ◽  
Vaccine Refusal ◽  
Slowing Down

Vaccine refusal can lead to renewed outbreaks of previously eliminated diseases and even delay global eradication. Vaccinating decisions exemplify a complex, coupled system where vaccinating behavior and disease dynamics influence one another. Such systems often exhibit critical phenomena—special dynamics close to a tipping point leading to a new dynamical regime. For instance, critical slowing down (declining rate of recovery from small perturbations) may emerge as a tipping point is approached. Here, we collected and geocoded tweets about measles–mumps–rubella vaccine and classified their sentiment using machine-learning algorithms. We also extracted data on measles-related Google searches. We find critical slowing down in the data at the level of California and the United States in the years before and after the 2014–2015 Disneyland, California measles outbreak. Critical slowing down starts growing appreciably several years before the Disneyland outbreak as vaccine uptake declines and the population approaches the tipping point. However, due to the adaptive nature of coupled behavior–disease systems, the population responds to the outbreak by moving away from the tipping point, causing “critical speeding up” whereby resilience to perturbations increases. A mathematical model of measles transmission and vaccine sentiment predicts the same qualitative patterns in the neighborhood of a tipping point to greatly reduced vaccine uptake and large epidemics. These results support the hypothesis that population vaccinating behavior near the disease elimination threshold is a critical phenomenon. Developing new analytical tools to detect these patterns in digital social data might help us identify populations at heightened risk of widespread vaccine refusal.

Detecting risk of regime shifts in ecosystems

2020 ◽  
Author(s):  
Juan Rocha
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Terrestrial Ecosystems ◽  
Regime Shifts ◽  
Critical Slowing Down ◽  
Small Disturbance ◽  
Slowing Down ◽  
Critical Transitions ◽  
The World ◽  
Universal Indicator

<div> <div> <div> <p>Ecosystems around the world are at riks of critical transitions due to increasing anthropogenic preasures and climate change. However, it is not clear where the risks are higher, or where ecosystems are more vulnerable. When a dynamic system is close to a threshold, it leaves a statistical signature on its time series known as critical slowing down. It takes longer to recover after a small disturbance, which translates into increases in variance, autocorrelation, and skewness or flickering. Here I measure critical slowing down on primary production proxies for marine and terrestrial ecosystems globally. Slowness is an indicator of potential instabilities and a proxy of resilience. While slowness is not a universal indicator for critical transitions, it can be used for detection of potential regime shifts.</p> </div> </div> </div>

scholarly journals Catching Critical Transition in Engineered Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jin Huang ◽  
Tianchuang Meng ◽  
Yangdong Deng ◽  
Fanling Huang
Keyword(s):  
Early Warning ◽  
Multiple Scales ◽  
Stable State ◽  
Critical Threshold ◽  
Supporting Evidence ◽  
Critical Transition ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Critical Transitions ◽  
Engineered Systems

A variety of engineered systems can encounter critical transitions where the system suddenly shifts from one stable state to another at a critical threshold. The critical transition has aroused vital concerns for its potentially disastrous impacts. We validate an often taken-for-granted hypothesis that the failure of engineered systems can be attributed to the respective critical transitions and show how early warning signals are closely associated with critical transitions. We demonstrate that it is feasible to use early warning signals to predict system failures. Our findings open a new path to forecast failures of engineered systems with a generic method and provide supporting evidence for the universal existence of critical transition in dynamical systems at multiple scales.

scholarly journals Critical slowing down in a dynamic duopoly

2015 ◽  
Vol 36 ◽  
pp. 1560012
Author(s):  
M. G. O. Escobido ◽  
N. Hatano
Keyword(s):  
Complex Systems ◽  
Dynamic Model ◽  
Early Warning ◽  
Critical Slowing Down ◽  
Economic Systems ◽  
Volterra System ◽  
Quantity Competition ◽  
Slowing Down ◽  
Critical Transitions ◽  
Dynamic Duopoly

Anticipating critical transitions is very important in economic systems as it can mean survival or demise of firms under stressful competition. As such identifying indicators that can provide early warning to these transitions are very crucial. In other complex systems, critical slowing down has been shown to anticipate critical transitions. In this paper, we investigate the applicability of the concept in the heterogeneous quantity competition between two firms. We develop a dynamic model where the duopoly can adjust their production in a logistic process. We show that the resulting dynamics is formally equivalent to a competitive Lotka-Volterra system. We investigate the behavior of the dominant eigenvalues and identify conditions that critical slowing down can provide early warning to the critical transitions in the dynamic duopoly.

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