A closed form for Jacobian reconstruction from time series and its application as an early warning signal in network dynamics

The Jacobian matrix of a dynamical system describes its response to perturbations. Conversely, one can estimate the Jacobian matrix by carefully monitoring how the system responds to environmental noise. We present a closed-form analytical solution for the calculation of a system’s Jacobian from a time series. Being able to access the Jacobian enables a broad range of mathematical analyses by which deeper insights into the system can be gained. Here we consider in particular the computation of the leading Jacobian eigenvalue as an early warning signal for critical transitions. To illustrate this approach, we apply it to ecological meta-foodweb models, which are strongly nonlinear dynamical multi-layer networks. Our analysis shows that accurate results can be obtained, although the data demand of the method is still high.

An Early Warning Signal (EWS) Model for Predicting Financial Crisis in Emerging African Economies

The devastating effects of the global financial crisis (GFC) have led to a renewed, global interest in the development of an early warning signal (EWS) model. The purpose of the EWS model is to alert policymakers and other stakeholders to the possibility of the occurrence of a crisis. This study estimates a EWS model for predicting the financial crisis in four emerging African economies using a multinomial logit model and a data set covering the period of January 1980 to December 2017. The result of the study suggests that emerging African economies are more likely to face financial crisis as debts continue to rise without a corresponding capacity to withstand capital flow reversal as well as excessive foreign exchange risk due to currency exposure. The result further indicates that rising debt exposure raises the likelihood of the economies remaining in a state of crisis. This result confirms the significance of a financial stability framework that addresses the issues confronting Africa’s emerging economies such as rising debt profile, liquidity and currency risk exposure.

STATISTICAL PROCESS CONTROL IMPLEMENTATION AS EARLY WARNING SIGNAL FOR SAFETY INTERVENTION IMPROVEMENT AT MINING OPERATION

As the frequency, severity, and costs of safety risks continue to become a challenge for mining industry, the company understood that the existing safety analytic does not provide adequate information, as it has been relying predominantly on collecting and evaluating aggregated data of lagging indicators about past accidents. This method has been negatively driving the organization to carry out repetitive cycle of accident analysis and problem solving, and therefore, undertaking reactive responses. This paper investigated how statistical process control, in particular control charts, can be applied to hazards data, as the leading indicator of accidents, to detect statistically trends in safety process and safety behavior, aiming to control the safety process in real-time manner before the occurrence of accidents. The result showed that the latest iteration of control limits development in Phase 3 is suitable as the control chart for safety process in one of case study mine operation site. Furthermore, the implementation of control charts to hazards data not only it helps the organization to transition its safety analytic to leading indicator analysis, it enables the organization to control safety process in real-time practice and to carry out timely safety intervention long before the potential occurrence of severe accidents, in which within this case, the first early warning signal was triggered 49 days before the occurrence of the fatality accident.

A Simple Early Warning Signal for COVID-19

The paper provides some initial evidence that daily mortality rates (for any cause) by municipality or province can be used as a statistically reliable predictor of looming COVID-19 crises. Using recently published deaths figures for 1,689 Italian municipalities, we estimate the growth in daily mortality rates between the period 2015–2019 and 2020 by province. All provinces that experienced a major COVID-19 shock in mid-March 2020 had increases in mortality rates of 100% or above already in early February 2020. This increase was particularly strong for males and older people, two recognizable features of COVID-19. Using a panel fixed effect model, we show that the association between these early increases in mortality for any cause and the March 2020 COVID-19 shock is strong and significant. We conclude that the growth in mortality rates can be used as a statistically reliable predictor of COVID-19 crises.

Testing early-warning signals for the transition of ecological network properties in wetland complex

<p>Wetlands, which exist in both natural and man-made landscapes, play a critical role in providing various ecosystem services for both ecology and human-being. These services are affected not only by regional hydro-climatic and geologic conditions but also by human activities. On a landscape scale, wetlands form a complex spatial structure by their spatial distribution in a specific geological setting. Consequently, dispersal of inhabiting species between spatially distributed wetlands organizes ecological networks that are consisted of nodes (wetlands) and links (pathways of movement). In this study, we generated and analyzed the ecological networks by introducing deterministic (e.g., threshold distance) or stochastic (e.g., exponential kernel and heavy-tailed model) dispersal models. From these networks, we evaluated structural or functional characteristics including degree, efficiency, and clustering coefficient, all of which are affected by disturbances such as seasonal hydro-climatic conditions that change wetland surface area, and shocks that may remove nodes from the network (e.g., human activities for land development). Specifically, by using the characteristics of the corresponding ecological networks, we analyzed (1) their network robustness by simulating the removal of nodes selected by their degree or area; and (2) the change of variance as the early-warning signal to predict where critical point may occur in global network characteristics affected by disturbances. The results showed that there was not a clear relationship between network robustness and wetland size for node removal. However, when nodes were removed in the order of degree, the network fragmented rapidly. Also, we observed that the variance of network characteristics in the time-series increased in drier hydro-climatic conditions for all the three network models we tested. This result indicates a possibility of using increasing variance as the early-warning signal for detecting a critical transition in network characteristics as the hydro-climatic condition becomes dry. In sum, the observed characteristics of ecological networks are vulnerable to target attack on hubs (structurally important nodes) or drought. Also, the resilience of a wetlandscape can be low after hubs were destroyed or in a dry season causing the fragmentation of habitats. Implications of these results for modeling ecological networks depending on hydrologic systems and influenced by human activities will provide a new decision-making process, especially for restoring and conservation purposes.</p>