Detection Of Early Warning Signals Research Articles

Analysis of long transients and detection of early warning signals of extinction in a class of predator-prey models exhibiting bistable behavior.

In this paper, we develop a method of analyzing long transient dynamics in a class of predator-prey models with two species of predators competing explicitly for their common prey, where the prey evolves on a faster timescale than the predators. In a parameter regime near a singular zero-Hopf bifurcation of the coexistence equilibrium state, we assume that the system under study exhibits bistability between a periodic attractor that bifurcates from the singular Hopf point and another attractor, which could be a periodic attractor or a point attractor, such that the invariant manifolds of the coexistence equilibrium point play central roles in organizing the dynamics. To find whether a solution that starts in a vicinity of the coexistence equilibrium approaches the periodic attractor or the other attractor, we reduce the equations to a suitable normal form, and examine the basin boundary near the singular Hopf point. A key component of our study includes an analysis of the long transient dynamics, characterized by their rapid oscillations with a slow variation in amplitude, by applying a moving average technique. We obtain a set of necessary and sufficient conditions on the initial values of a solution near the coexistence equilibrium to determine whether it lies in the basin of attraction of the periodic attractor. As a result of our analysis, we devise a method of identifying early warning signals, significantly in advance, of a future crisis that could lead to extinction of one of the predators. The analysis is applied to the predator-prey model considered in Sadhu (Discrete Contin Dyn Syst B 26:5251-5279, 2021) and we find that our theory is in good agreement with the numerical simulations carried out for this model.

Early detection of Hopf bifurcation in a solid rocket motor via transfer learning

Hopf bifurcation, a prevalent phenomenon in solid rocket motors (SRMs), signifies a critical transition from a fixed point to a limit cycle. The detection of early warning signals (EWSs) for Hopf bifurcation is significant for preventing or mitigating potentially dangerous self-excited states. However, conventional data-driven EWSs are hindered by the lack of a consistent threshold, yielding mainly qualitative judgments when solely pre-bifurcation data are available. In this study, we introduce a transfer learning (TL) framework designed to estimate the system growth rate as an EWS utilizing pre-bifurcation data. The framework is initially trained on the correlation between dynamical features and growth rate within a source domain, generated by a reduced-order model proposed by Culick. Subsequently, it is applied to the target domain from the SRM system. This TL-based EWS exhibits remarkable sensitivity when applied to the SRM system, providing consistent threshold values for quantitative predictions based on pre-bifurcation data exclusively. Our findings present a promising path for detecting the EWSs of Hopf bifurcations in SRMs and affirm the feasibility and tremendous potential of utilizing TL in scenarios where real data are limited.

Multicentre, interventional, single-arm study protocol of telemonitored circadian rhythms and patient-reported outcomes for improving mFOLFIRINOX safety in patients with pancreatic cancer (MultiDom, NCT04263948)

IntroductionCircadian clocks regulate cellular proliferation and drug effects. Tolerability and/or efficacy of anticancer therapies have been improved by their administration according to circadian rhythms, while being predicted by circadian robustness....

EU need to protect its environment from toxic per- and polyfluoroalkyl substances

The Environmental Protection Agencies (EPAs) of Denmark, Sweden, Norway, Germany and the Netherlands submitted a proposal to the European Chemical Agency (ECHA) in February 2023 calling for a ban in the use of toxic industrial chemicals per- and polyfluoroalkyl substances (PFAS). These chemicals are highly toxic causing elevated cholesterol, immune suppression, reproductive failure, cancer and neuro-endocrine disruption in humans and wildlife being a significant threat to biodiversity and human health. The main reason for the submitted proposal is recent findings of significant flaws in the transition to PFAS replacements that is leading to a widespread pollution. Denmark was the first country banning PFAS, and now other EU countries support the restrictions of these carcinogenic, endocrine disruptive and immunotoxic chemicals. The proposed plan is among the most extensive received by the ECHA for 50 years. Denmark is now the first EU country to initiate the establishment of groundwater parks to try and protect its drinking water. These parks are areas free of agricultural activities and nutritious sewage sludge to secure drinking water free of xenobiotic including PFAS. The PFAS pollution also reflects the lack of comprehensive spatial and temporal environmental monitoring programs in the EU. Such monitoring programs should include key indicator species across ecosystems of livestock, fish and wildlife, to facilitate detection of early ecological warning signals and sustain public health. Simultaneously with inferring a total PFAS ban, the EU should also push for more persistent, bioaccumulative and toxic (PBT) PFAS substances to be listed on the Stockholm Convention (SC) Annex A such as PFOS (perfluorooctane sulfonic acid) that is currently listed on the SCs Annex B. The combination of these regulative restrictions combined with groundwater parks and pan-European biomonitoring programs, would pave the way forward for a cleaner environment to sustain health across the EU.

Early Warning Signals in Phase Space: Geometric Resilience Loss Indicators From Multiplex Cumulative Recurrence Networks.

The detection of Early Warning Signals (EWS) of imminent phase transitions, such as sudden changes in symptom severity could be an important innovation in the treatment or prevention of disease or psychopathology. Recurrence-based analyses are known for their ability to detect differences in behavioral modes and order transitions in extremely noisy data. As a proof of principle, the present paper provides an example of a recurrence network based analysis strategy which can be implemented in a clinical setting in which data from an individual is continuously monitored for the purpose of making decisions about diagnosis and intervention. Specifically, it is demonstrated that measures based on the geometry of the phase space can serve as Early Warning Signals of imminent phase transitions. A publicly available multivariate time series is analyzed using so-called cumulative Recurrence Networks (cRN), which are recurrence networks with edges weighted by recurrence time and directed towards previously observed data points. The results are compared to previous analyses of the same data set, benefits, limitations and future directions of the analysis approach are discussed.

Low-Temperature Hydrothermal Systems Response to Rainfall Forcing: An Example From Temperature Time Series of Fumaroles at La Soufrière de Guadeloupe Volcano

Volcanoes with highly-developed and shallow hydrothermal systems may be subject to sudden increases of their surface steam emission at vents in response to either deep forcing (e.g. increase of heat flux coming from the magma chamber) or external forcing (e.g. sudden decrease of atmospheric pressure or variation of meteoric water input). Because the vent plumbing has a limited heat and mass transfer capacity, the rise of steam pressure accompanying the increase of flux may destabilize the system in order to augment its net transfer capacity. This reorganization may, for instance, take the form of an enlargement of existing conduits and vents or to the creation of new ones. In such a case, local and extremely dangerous blast phenomena are likely to occur with devastating consequences several hundreds of meters around. Even volcanoes with a moderate activity and considered safe by the local population are exposed to such abrupt and dangerous events. The detection of early warning signals through temperature monitoring in the vents is of a primary importance and a main difficulty is to correctly interpret temperature jumps in order to reduce false alarms. We analyze time series of the temperature measured in three fumaroles located at the top of La Soufrière volcano in Guadeloupe, which are characterized by their relatively low temperature around 99°C, slightly above the boiling temperature of water at this altitude. Thanks to the long duration of the records from January to August 2017 and to their short 1-s sampling interval, a multiscale analysis can be performed over several orders of magnitude. We show that, despite their complex and sometimes erratic appearance, the temperature variations observed in the vents contain components highly correlated with rain input variations. Some remarkable patterns recurrently appear at different periods and we show that the main temperature variations of more than 10°C are related to the rainfall intensity. Our results illustrate the importance of external forcing on the otherwise complex and possibly chaotic dynamics of the shallow hydrothermal system of La Soufrière. They also reveal that a careful analysis of rainfall forcing must be done to be able to draw any conclusion concerning changes caused by the underlying hydrothermal system.

Toward an Early Warning System for Monitoring Asylum-Related Migration Flows in Europe

Asylum-related migration is highly complex, uncertain, and volatile, which precludes using standard model-based predictions to inform policy and operational decisions. At the same time, asylum's potentially high societal impacts on receiving countries and the resource implications of asylum processes call for more proactive approaches for assessing current and future migration flows. In this article, we propose an alternative approach to asylum modeling, based on the detection of early warning signals by using models originating from statistical control theory. Our empirical analysis of several asylum flows into Europe in 2010–2016 demonstrates the approach's utility and potential in aiding the management of mixed migration flows, while also shedding more light on the work needed to make better use of the “big data” and scenario-based methods for comprehensive and systematic examination of risk, uncertainty, and emerging trends.

Probabilistic early warning signals.

Ecological communities and other complex systems can undergo abrupt and long‐lasting reorganization, a regime shift, when deterministic or stochastic factors bring them to the vicinity of a tipping point between alternative states. Such changes can be large and often arise unexpectedly. However, theoretical and experimental analyses have shown that changes in correlation structure, variance, and other standard indicators of biomass, abundance, or other descriptive variables are often observed prior to a state shift, providing early warnings of an anticipated transition. Natural systems manifest unknown mixtures of ecological and environmental processes, hampered by noise and limited observations. As data quality often cannot be improved, it is important to choose the best modeling tools available for the analysis.We investigate three autoregressive models and analyze their theoretical differences and practical performance. We formulate a novel probabilistic method for early warning signal detection and demonstrate performance improvements compared to nonprobabilistic alternatives based on simulation and publicly available experimental time series.The probabilistic formulation provides a novel approach to early warning signal detection and analysis, with enhanced robustness and treatment of uncertainties. In real experimental time series, the new probabilistic method produces results that are consistent with previously reported findings.Robustness to uncertainties is instrumental in the common scenario where mechanistic understanding of the complex system dynamics is not available. The probabilistic approach provides a new family of robust methods for early warning signal detection that can be naturally extended to incorporate variable modeling assumptions and prior knowledge.

Characterizing and predicting person-specific, day-to-day, fluctuations in walking behavior.

Despite the positive health effect of physical activity, one third of the world's population is estimated to be insufficiently active. Prior research has mainly investigated physical activity on an aggregate level over short periods of time, e.g., during 3 to 7 days at baseline and a few months later, post-intervention. To develop effective interventions, we need a better understanding of the temporal dynamics of physical activity. We proposed here an approach to studying walking behavior at "high-resolution" and by capturing the idiographic and day-to-day changes in walking behavior. We analyzed daily step count among 151 young adults with overweight or obesity who had worn an accelerometer for an average of 226 days (~25,000 observations). We then used a recursive partitioning algorithm to characterize patterns of change, here sudden behavioral gains and losses, over the course of the study. These behavioral gains or losses were defined as a 30% increase or reduction in steps relative to each participants' median level of steps lasting at least 7 days. After the identification of gains and losses, fluctuation intensity in steps from each participant's individual time series was computed with a dynamic complexity algorithm to identify potential early warning signals of sudden gains or losses. Results revealed that walking behavior change exhibits discontinuous changes that can be described as sudden gains and losses. On average, participants experienced six sudden gains or losses over the study. We also observed a significant and positive association between critical fluctuations in walking behavior, a form of early warning signals, and the subsequent occurrence of sudden behavioral losses in the next days. Altogether, this study suggests that walking behavior could be well understood under a dynamic paradigm. Results also provide support for the development of "just-in-time adaptive" behavioral interventions based on the detection of early warning signals for sudden behavioral losses.

Linkages between Climate, Radial Growth and Defoliation in Abies pinsapo Forests from Southern Spain

Systematic forest networks of health monitoring have been established to follow changes in tree vigor and mortality. These networks often lack long-term growth data, but they could be complemented with tree ring data, since both defoliation and radial growth are proxies of changes in tree vigor. For instance, a severe water shortage should reduce growth and increase tree defoliation in drought-prone areas. However, the effects of climatic stress and drought on growth and defoliation could also depend on tree age. To address these issues, we compared growth and defoliation data with recent climate variability and drought severity in Abies pinsapo old and young trees sampled in Southern Spain, where a systematic health network (Andalucía Permanent Plot Network) was established. Our aims were: (i) to assess the growth sensitivity of old and young A. pinsapo trees and (ii) to test if relative changes in radial growth were related with recent defoliation, for instance, after severe droughts. We also computed the resilience indices to quantify how old and young trees recovered growth after recent droughts. Wet-cool conditions during the prior autumn and the current early summer improved the growth of old trees, whereas late-spring wet conditions enhanced the growth of young trees. Old trees were more sensitive to wet and sunny conditions in the early summer than young trees. Old and young trees were more responsive to the Standardized Precipitation-Evapotranspiration Index drought index of June–July and July–August calculated at short (one–three months) and mid (three–six months) time scales, respectively. Old trees presented a higher resistance to a severe drought in 1995 than young trees. A positive association was found between stand defoliation and relative growth. Combining monitoring and tree ring networks is useful for the detection of early warning signals of dieback in similar drought-prone forests.

Detection of early warning signals for overruns in IS projects: linguistic analysis of business case language

ABSTRACT Many Information Systems (IS) projects fail to be completed within budget and on schedule. A contributing factor is the so-called planning fallacy in which people tend to underestimate the resources required to complete a project. In this paper, we propose that signals of the planning fallacy can be detected in a project’s business case. We investigated whether language usage in business cases can serve as an early warning signal for overruns in IS projects. Drawing on two theoretical perspectives – the Linguistic Category Model (LCM) and Construal Level Theory (CLT) – two sets of rival hypotheses were tested concerning the relationship between project overruns and whether the language usage in a business case is abstract or concrete. A linguistic analysis of the business cases of large IS projects in the Netherlands suggests that concrete language usage in the business case is associated with bigger budget and schedule overruns. For researchers, our study contributes to the existing literature on the importance of language usage. For practitioners, our study provides an early warning indicator for overruns.

New Insights in Computational Methods for Pharmacovigilance: E-Synthesis, a Bayesian Framework for Causal Assessment.

Today’s surge of big data coming from multiple sources is raising the stakes that pharmacovigilance has to win, making evidence synthesis a more and more robust approach in the field. In this scenario, many scholars believe that new computational methods derived from data mining will effectively enhance the detection of early warning signals for adverse drug reactions, solving the gauntlets that post-marketing surveillance requires. This article highlights the need for a philosophical approach in order to fully realize a pharmacovigilance 2.0 revolution. A state of the art on evidence synthesis is presented, followed by the illustration of E-Synthesis, a Bayesian framework for causal assessment. Computational results regarding dose-response evidence are shown at the end of this article.

User rating activity within KIWI: A technology for public health event monitoring and early warning signal detection.

ObjectivesTo review user signal rating activity within the Canadian Network for Public Health Intelligence’s (CNPHI’s) Knowledge Integration using Web-based Intelligence (KIWI) technology by answering the following questions: (1) who is rating, (2) how are users rating, and (3) how well are users rating?MethodsKIWI rating data was extracted from the CNPHI platform. Zoonotic & Emerging program signals with first rating occurring between January 1, 2016 and December 31, 2017 were included. Krippendorff’s alpha was used to estimate inter-rater reliability between users. A z-test was used to identify whether users tended to rate within 95% confidence interval (versus outside) the average community rating.ResultsThe 37 users who rated signals represented 20 organizations. 27.0% (n = 10) of users rated ≥10% of all rated signals, and their inter-rater reliability estimate was 72.4% (95% CI: 66.5-77.9%). Five users tended to rate significantly outside of the average community rating. An average user rated 58.4% of the time within the signal’s 95% CI. All users who significantly rated within the average community rating rated outside the 95% CI at least once.DiscussionA diverse community of raters participated in rating the signals. Krippendorff’s Alpha estimate revealed moderate reliability for users who rated ≥10% of signals. It was observed that inter-rater reliability increased for users with more experience rating signals.ConclusionsDiversity was observed between user ratings. It is hypothesized that rating diversity is influenced by differences in user expertise and experience, and that the number of times a user rates within and outside of a signal’s 95% CI can be used as a proxy for user expertise. The introduction of a weighted rating algorithm within KIWI that takes this into consideration could be beneficial.

No evidence of critical slowing down in two endangered Hawaiian honeycreepers.

There is debate about the current population trends and predicted short-term fates of the endangered forest birds, Hawai`i Creeper (Loxops mana) and Hawai`i `Ākepa (L. coccineus). Using long-term population size estimates, some studies report forest bird populations as stable or increasing, while other studies report signs of population decline or impending extinction associated with introduced Japanese White-eye (Zosterops japonicus) increase. Reliable predictors of impending population collapse, well before the collapse begins, have been reported in simulations and microcosm experiments. In these studies, statistical indicators of critical slowing down, a phenomenon characterized by longer recovery rates after population size perturbation, are reported to be early warning signals of an impending regime shift observable prior to the tipping point. While the conservation applications of these metrics are commonly discussed, early warning signal detection methods are rarely applied to population size data from natural populations, so their efficacy and utility in species management remain unclear. We evaluated two time series of state-space abundance estimates (1987–2012) from Hakalau Forest National Wildlife Refuge, Hawai`i to test for evidence of early warning signals of impending population collapse for the Hawai`i Creeper and Hawai`i `Ākepa. We looked for signals throughout the time series, and prior to 2000, when white-eye abundance began increasing. We found no evidence for either species of increasing variance, autocorrelation, or skewness, which are commonly reported early warning signals. We calculated linear rather than ordinary skewness because the latter is biased, particularly for small sample sizes. Furthermore, we identified break-points in trends over time for both endangered species, indicating shifts in slopes away from strongly increasing trends, but they were only weakly supported by Bayesian change-point analyses (i.e., no step-wise changes in abundance). The break-point and change-point test results, in addition to the early warning signal analyses, support that the two populations do not appear to show signs of critical slowing down or decline.

On the use of evolutionary time series analysis for segmenting paleoclimate data

Recent studies propose that different dynamical systems, such as climate, ecological and financial systems, among others, present critical transition points named to as tipping points (TPs). Climate TPs can severely affect millions of lives on Earth so that an active scientific community is working on finding early warning signals. This paper deals with the development of a time series segmentation algorithm for paleoclimate data in order to find segments sharing common statistical patterns. The proposed algorithm uses a clustering-based approach for evaluating the solutions and six statistical features, most of which have been previously considered in the detection of early warning signals in paleoclimate TPs. Due to the limitations of classical statistical methods, we propose the use of a genetic algorithm to automatically segment the series, together with a method to compare the segmentations. The final segments provided by the algorithm are used to construct a prediction model, whose promising results show the importance of segmentation for improving the understanding of a time series.

Sparse Pathway-Induced Dynamic Network Biomarker Discovery for Early Warning Signal Detection in Complex Diseases.

In many complex diseases, the transition process from the healthy stage to the catastrophic stage does not occur gradually. Recent studies indicate that the initiation and progression of such diseases are comprised of three steps including healthy stage, pre-disease stage, and disease stage. It has been demonstrated that a certain set of trajectories can be observed in the genetic signatures at the molecular level, which might be used to detect the pre-disease stage and to take necessary medical interventions. In this paper, we propose two optimization-based algorithms for extracting the dynamic network biomarkers responsible for catastrophic transition into the disease stage, and to open new horizons to reverse the disease progression at an early stage through pinpointing molecular signatures provided by high-throughput microarray data. The first algorithm relies on meta-heuristic intelligent search to characterize dynamic network biomarkers represented as a complete graph. The second algorithm induces sparsity on the adjacency matrix of the genes by taking into account the biological signaling and metabolic pathways, since not all the genes in the ineractome are biologically linked. Comprehensive numerical and meta-analytical experiments verify the effectiveness of the results of the proposed approaches in terms of network size, biological meaningfulness, and verifiability.

KIWI: A technology for public health event monitoring and early warning signal detection.

Objectives: To introduce the Canadian Network for Public Health Intelligence’s new Knowledge Integration using Web-based Intelligence (KIWI) technology, and to pefrom preliminary evaluation of the KIWI technology using a case study. The purpose of this new technology is to support surveillance activities by monitoring unstructured data sources for the early detection and awareness of potential public health threats.Methods: A prototype of the KIWI technology, adapted for zoonotic and emerging diseases, was piloted by end-users with expertise in the field of public health and zoonotic/emerging disease surveillance. The technology was assessed using variables such as geographic coverage, user participation, and others; categorized by high-level attributes from evaluation guidelines for internet based surveillance systems. Special attention was given to the evaluation of the system’s automated sense-making algorithm, which used variables such as sensitivity, specificity, and predictive values. Event-based surveillance evaluation was not applied to its full capacity as such an evaluation is beyond the scope of this paper.Results: KIWI was piloted with user participation = 85.0% and geographic coverage within monitored sources = 83.9% of countries. The pilots, which focused on zoonotic and emerging diseases, lasted a combined total of 65 days and resulted in the collection of 3243 individual information pieces (IIP) and 2 community reported events (CRE) for processing. Ten sources were monitored during the second phase of the pilot, which resulted in 545 anticipatory intelligence signals (AIS). KIWI’s automated sense-making algorithm (SMA) had sensitivity = 63.9% (95% CI: 60.2-67.5%), specificity = 88.6% (95% CI: 87.3-89.8%), positive predictive value = 59.8% (95% CI: 56.1-63.4%), and negative predictive value = 90.3% (95% CI: 89.0-91.4%).Discussion: Literature suggests the need for internet based monitoring and surveillance systems that are customizable, integrated into collaborative networks of public health professionals, and incorporated into national surveillance activities. Results show that the KIWI technology is well posied to address some of the suggested challenges. A limitation of this study is that sample size for pilot participation was small for capturing overall readiness of integrating KIWI into regular surveillance activities.Conclusions: KIWI is a customizable technology developed within an already thriving collaborative platform used by public health professionals, and performs well as a tool for discipline-specific event monitoring and early warning signal detection.

Strategic Role of Dashboard Application in Enhancing Crisis Management Capabilities in Organizations Field study on Jordanian cellular companies

This study aims to investigate the impact of Dashboard to detect necessary early warning signals and readiness in Jordanian communications companies. The study’s population consists of all managers and users of Dashboard in two Jordanian cellular operators: Orange and Umnieh, totaling (84) employees. The study employed comprehensive survey on all members of the researched topic, and found that there is a significant effect of Dashboard on the detection of necessary early warning signals and preparing for them.

Body size distributions signal a regime shift in a lake ecosystem.

Communities of organisms, from mammals to microorganisms, have discontinuous distributions of body size. This pattern of size structuring is a conservative trait of community organization and is a product of processes that occur at multiple spatial and temporal scales. In this study, we assessed whether body size patterns serve as an indicator of a threshold between alternative regimes. Over the past 7000 years, the biological communities of Foy Lake (Montana, USA) have undergone a major regime shift owing to climate change. We used a palaeoecological record of diatom communities to estimate diatom sizes, and then analysed the discontinuous distribution of organism sizes over time. We used Bayesian classification and regression tree models to determine that all time intervals exhibited aggregations of sizes separated by gaps in the distribution and found a significant change in diatom body size distributions approximately 150 years before the identified ecosystem regime shift. We suggest that discontinuity analysis is a useful addition to the suite of tools for the detection of early warning signals of regime shifts.

Threat identification, and risk assessment and monitoring as a form of early warning

Early warning signal detection can make a huge difference in the ability of the system, society and community to deal with imminent crises and disasters, not only at national but also at regional and global level. Timely detection of signals that precede an adverse event provides an opportunity to avoid this event or to at least mitigate its consequences. Accurate, timely and properly formulated early warning can prevent the loss of human lives and alleviate the economic and financial impact of disasters. The initial and most important processes which provide the data necessary for making the decision to send an early warning are threat identification, and assessment and monitoring of risk for the observed system, society, or community. These three processes provide a framework for the analysis of scenarios with adverse outcomes and for the assessment of the consequences of alternative choices, including a scenario in which measures are not taken. Despite the growing awareness of the importance of the timely threat identification and the increase of the capacity for urgent response, the creation of an effective early warning system, in the conditions of global financial crisis and the different national economic standing, still remains a global challenge.