scholarly journals Spatial Correlation as an Early Warning Signal of Regime Shifts in a Multiplex Disease-Behaviour Network

2017 ◽  
Author(s):  
Peter C. Jentsch ◽  
Madhur Anand ◽  
Chris T. Bauch
Keyword(s):  
Spatial Correlation ◽  
Early Warning ◽  
Regime Shift ◽  
Tipping Point ◽  
Regime Shifts ◽  
Critical Slowing Down ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Network Layer ◽  
Slowing Down

AbstractEarly warning signals of sudden regime shifts are a widely studied phenomenon for their ability to quantify a system’s proximity to a tipping point to a new and contrasting dynamical regime. However, this effect has been little studied in the context of the complex interactions between disease dynamics and vaccinating behaviour. Our objective was to determine whether critical slowing down (CSD) occurs in a multiplex network that captures opinion propagation on one network layer and disease spread on a second network layer. We parameterized a network simulation model to represent a hypothetical self-limiting, acute, vaccine-preventable infection with shortlived natural immunity. We tested five different network types: random, lattice, small-world, scale-free, and an empirically derived network. For the first four network types, the model exhibits a regime shift as perceived vaccine risk moves beyond a tipping point from full vaccine acceptance and disease elimination to full vaccine refusal and disease endemicity. This regime shift is preceded by an increase in the spatial correlation in non-vaccinator opinions beginning well before the bifurcation point, indicating CSD. The early warning signals occur across a wide range of parameter values. However, the more gradual transition exhibited in the empirically-derived network underscores the need for further research before it can be determined whether trends in spatial correlation in real-world social networks represent critical slowing down. The potential upside of having this monitoring ability suggests that this is a worthwhile area for further research.

scholarly journals Early warning signals of malaria resurgence in Kericho, Kenya

2020 ◽  
Vol 16 (3) ◽  
pp. 20190713 ◽  
Author(s):  
Mallory J. Harris ◽  
Simon I. Hay ◽  
John M. Drake
Keyword(s):  
Early Warning ◽  
Disease Transmission ◽  
Empirical Test ◽  
Disease Incidence ◽  
Critical Slowing Down ◽  
Summary Statistics ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Slowing Down ◽  
Imported Case

Campaigns to eliminate infectious diseases could be greatly aided by methods for providing early warning signals of resurgence. Theory predicts that as a disease transmission system undergoes a transition from stability at the disease-free equilibrium to sustained transmission, it will exhibit characteristic behaviours known as critical slowing down, referring to the speed at which fluctuations in the number of cases are dampened, for instance the extinction of a local transmission chain after infection from an imported case. These phenomena include increases in several summary statistics, including lag-1 autocorrelation, variance and the first difference of variance. Here, we report the first empirical test of this prediction during the resurgence of malaria in Kericho, Kenya. For 10 summary statistics, we measured the approach to criticality in a rolling window to quantify the size of effect and directions. Nine of the statistics increased as predicted and variance, the first difference of variance, autocovariance, lag-1 autocorrelation and decay time returned early warning signals of critical slowing down based on permutation tests. These results show that time series of disease incidence collected through ordinary surveillance activities may exhibit characteristic signatures prior to an outbreak, a phenomenon that may be quite general among infectious disease systems.

scholarly journals Deep learning for early warning signals of regime shifts

2021 ◽  
Author(s):  
Thomas Bury ◽  
Raman Sujith ◽  
Induja Pavithran ◽  
Marten Scheffer ◽  
Timothy Lenton ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Warning ◽  
Normal Forms ◽  
Learning Algorithm ◽  
Tipping Point ◽  
Regime Shifts ◽  
Tipping Points ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Deep Learning Algorithm

Many natural systems exhibit regime shifts where slowly changing environmental conditions suddenly shift the system to a new and sometimes very different state. As the tipping point is approached, the dynamics of complex and varied systems all simplify down to a small number of possible 'normal forms' that determine how the new regime will look. Indicators such as increasing lag-1 autocorrelation and variance provide generic early warning signals (EWS) by detecting how dynamics slow down near the tipping point. But they do not indicate what type of new regime will emerge. Here we develop a deep learning algorithm that can detect EWS in systems it was not explicitly trained on, by exploiting information about normal forms and scaling behaviour of dynamics near tipping points that are common to many dynamical systems. The algorithm detects EWS in 268 empirical and model time series from ecology, thermoacoustics, climatology, and epidemiology with much greater sensitivity and specificity than generic EWS. It can also predict the normal form that will characterize the oncoming regime shift. Such approaches can help humans better manage regime shifts. The algorithm also illustrates how a universe of possible models can be mined to recognize naturally-occurring tipping points.

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 Early-Warning Signals for the Onsets of Greenland Interstadials and the Younger Dryas–Preboreal Transition

2016 ◽  
Vol 29 (11) ◽  
pp. 4047-4056 ◽  
Author(s):  
Martin Rypdal
Keyword(s):  
Time Scales ◽  
Early Warning ◽  
High Frequency ◽  
Characteristic Time ◽  
Younger Dryas ◽  
Tipping Point ◽  
Warning Signals ◽  
Correlation Times ◽  
Early Warning Signals ◽  
Slowing Down

Abstract The climate system approaches a tipping point if the prevailing climate state loses stability, making a transition to a different state possible. A result from the theory of randomly driven dynamical systems is that the reduced stability in the vicinity of a tipping point is accompanied by increasing fluctuation levels and longer correlation times (critical slowing down) and can in principle serve as early-warning signals of an upcoming tipping point. This study demonstrates that the high-frequency band of the δ18O variations in the North Greenland Ice Core Project displays fluctuation levels that increase as one approaches the onset of an interstadial (warm) period. Similar results are found for the locally estimated Hurst exponent for the high-frequency fluctuations, signaling longer correlation times. The observed slowing down is found to be even stronger in the Younger Dryas, suggesting that both the Younger Dryas–Preboreal transition and the onsets of the Greenland interstadials are preceded by decreasing stability of the climate state. It is also verified that the temperature fluctuations during the stadial periods can be approximately modeled as a scale-invariant persistent noise, which can be approximated as an aggregation of processes that respond to perturbations on certain characteristic time scales. The results are consistent with the hypothesis that both the onsets of the Greenland interstadials and the Younger Dryas–Preboreal transition are caused by tipping points in dynamical processes with characteristic time scales on the order of decades and that the variability of other processes on longer time scales masks the early-warning signatures in the δ18O signal.

scholarly journals Early-Warning Signals of Drought-Flood State Transition over the Dongting Lake Basin Based on the Critical Slowing Down Theory

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1082
Author(s):  
Hao Wu ◽  
Wei Hou ◽  
Dongdong Zuo ◽  
Pengcheng Yan ◽  
Yuxing Zeng
Keyword(s):  
Early Warning ◽  
State Transition ◽  
Dongting Lake ◽  
Critical Slowing Down ◽  
Lake Basin ◽  
Autocorrelation Coefficient ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Technological Support ◽  
Slowing Down

In this study, the standardized precipitation index (SPI) data in Hunan Province from 1961 to 2020 is adopted. Based on the critical slowing down theory, the moving t-test is firstly used to determine the time of drought-flood state transition in the Dongting Lake basin. Afterwards, by means of the variance and autocorrelation coefficient that characterize the phenomenon of critical slowing down, the early-warning signals indicating the drought-flood state in the Dongting Lake basin are explored. The results show that an obvious drought-to-flood (flood-to-drought) event occurred around 1993 (2003) in the Dongting Lake basin in recent 60 years. The critical slowing down phenomena of the increases in the variance and autocorrelation coefficient, which are detected 5–10 years in advance, can be considered as early-warning signals indicating the drought-flood state transition. Through the studies on the drought-flood state and related early-warning signals for the Dongting Lake basin, the reliabilities of the variance and autocorrelation coefficient-based early-warning signals for abrupt changes are demonstrated. It is expected that the wide application of this method could provide important scientific and technological support for disaster prevention and mitigation in the Dongting Lake basin, and even in the middle and lower reaches of the Yangtze River.

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 Robustness of early warning signals for catastrophic and non-catastrophic transitions

2017 ◽  
Author(s):  
Partha Sharathi Dutta ◽  
Yogita Sharma ◽  
Karen C. Abbott
Keyword(s):  
Early Warning ◽  
Regime Shift ◽  
Regime Shifts ◽  
Stable Equilibrium Point ◽  
Perturbation Scheme ◽  
Correlated Noise ◽  
Warning Signals ◽  
Saddle Node Bifurcation ◽  
Early Warning Signals ◽  
Saddle Node

AbstractEarly warning signals (EWS) are statistical indicators that a rapid regime shift may be forthcoming. Their development has given ecologists hope of predicting rapid regime shifts before they occur. Accurate predictions, however, rely on the signals being appropriate to the system in question. Most of the EWS commonly applied in ecology have been studied in the context of one specific type of regime shift (the type brought on by a saddle-node bifurcation, at which one stable equilibrium point collides with an unstable equilibrium and disappears) under one particular perturbation scheme (temporally uncorrelated noise that perturbs the net population growth rate in a density independent way). Whether and when these EWS can be applied to other ecological situations remains relatively unknown, and certainly underappreciated. We study a range of models with different types of dynamical transitions (including rapid regime shifts) and several perturbation schemes (density-dependent uncorrelated or temporally-correlated noise) and test the ability of EWS to warn of an approaching transition. We also test the sensitivity of our results to the amount of available pre-transition data and various decisions that must be made in the analysis (i.e. the rolling window size and smoothing bandwidth used to compute the EWS). We find that EWS generally work well to signal an impending saddle-node bifurcation, regardless of the autocorrelation or intensity of the noise. However, EWS do not reliably appear as expected for other types of transition. EWS were often very sensitive to the length of the pre-transition time series analyzed, and usually less sensitive to other decisions. We conclude that the EWS perform well for saddle-node bifurcation in a range of noise environments, but different methods should be used to predict other types of regime shifts. As a consequence, knowledge of the mechanism behind a possible regime shift is needed before EWS can be used to predict it.

Alternative stable states, tipping points, and early warning signals of ecological transitions

2020 ◽  
pp. 263-284
Author(s):  
John M. Drake ◽  
Suzanne M. O’Regan ◽  
Vasilis Dakos ◽  
Sonia Kéfi ◽  
Pejman Rohani
Keyword(s):  
Early Warning ◽  
Alternative Stable States ◽  
Intrinsic Noise ◽  
Point Theory ◽  
Ecological Systems ◽  
Tipping Point ◽  
Stable States ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Theoretical Predictions

Ecological systems are prone to dramatic shifts between alternative stable states. In reality, these shifts are often caused by slow forces external to the system that eventually push it over a tipping point. Theory predicts that when ecological systems are brought close to a tipping point, the dynamical feedback intrinsic to the system interact with intrinsic noise and extrinsic perturbations in characteristic ways. The resulting phenomena thus serve as “early warning signals” for shifts such as population collapse. In this chapter, we review the basic (qualitative) theory of such systems. We then illustrate the main ideas with a series of models that both represent fundamental ecological ideas (e.g. density-dependence) and are amenable to mathematical analysis. These analyses provide theoretical predictions about the nature of measurable fluctuations in the vicinity of a tipping point. We conclude with a review of empirical evidence from laboratory microcosms, field manipulations, and observational studies.

Sign in / Sign up

Export Citation Format

Share Document