scholarly journals Echo chambers as early warning signals of widespread vaccine refusal in social-epidemiological networks

2020 ◽  
Author(s):  
Brendon Phillips ◽  
Chris T. Bauch
Keyword(s):  
Infectious Disease ◽  
Early Warning ◽  
Social Contact ◽  
Contact Dynamics ◽  
Clustering Coefficient ◽  
Vaccine Uptake ◽  
Disease Spread ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Echo Chambers

AbstractSudden shifts in population health and vaccination rates occur as the dynamics of some epidemiological models go through a critical point; literature shows that this is sometimes foreshadowed by early warning signals (EWS). We investigate different structural measures of a network as candidate EWS of infectious disease outbreaks and changes in popular vaccine sentiment. We construct a multiplex disease model coupling infectious disease spread and social contact dynamics. We find that the number and mean size of echo chambers predict transitions in the infection dynamics, as do opinion-based communities. Graph modularity also gives early warnings, though the clustering coefficient shows no significant pre-outbreak changes. Change point tests applied to the EWS show decreasing efficacy as social norms strengthen. Therefore, many measures of social network connectivity can predict approaching critical changes in vaccine uptake and aggregate health, thereby providing valuable tools for improving public health.

scholarly journals Critical transitions in the hydrological system: Early-warning signals and network analysis

2021 ◽  
Author(s):  
Xueli Yang ◽  
Zhi-Hua Wang ◽  
Chenghao Wang
Keyword(s):  
Network Analysis ◽  
Early Warning ◽  
Clustering Coefficient ◽  
Hydrological Processes ◽  
Modular Organization ◽  
Precipitation Pattern ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Critical Transitions ◽  
Hydrological System

Abstract. In this study, we identified the critical transitions of hydrological processes including precipitation and potential evapotranspiration by analysing their early-warning signals and system-based network structures. The statistical early-warning signals are manifest in increasing trends of autocorrelation and variance in the hydrology system ranging from regional to global scales, prior to climate shifts in the 1970s and 1990s in agreement with observations. We further extended the conventional statistics-based measures of early-warning signals to system-based network analysis in urban areas across the contiguous United States. The topology of urban precipitation network features hub-periphery (clustering) and modular organization, with strong intra-regional connectivity and inter-regional gateways (teleconnection). We found that several network parameters (mean correlation coefficient, density, and clustering coefficient) gradually increased prior to the critical transition in the 1990s, signifying the enhanced synchronization among urban precipitation pattern. These topological parameters not only can serve as novel system-based early-warning signals to critical transitions in hydrological processes, but also shed new lights on structure-dynamic interactions in the complex hydrological system.

scholarly journals Efficacy of early warning signals and spectral periodicity for predicting transitions in bipolar patients: An actigraphy study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yoram K. Kunkels ◽  
Harriëtte Riese ◽  
Stefan E. Knapen ◽  
Rixt F. Riemersma - van der Lek ◽  
Sandip V. George ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Early Warning ◽  
Type I ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Research Fields ◽  
Mood Episode ◽  
Final Sample ◽  
Bipolar Patients ◽  
Direction Opposite

AbstractEarly-warning signals (EWS) have been successfully employed to predict transitions in research fields such as biology, ecology, and psychiatry. The predictive properties of EWS might aid in foreseeing transitions in mood episodes (i.e. recurrent episodes of mania and depression) in bipolar disorder (BD) patients. We analyzed actigraphy data assessed during normal daily life to investigate the feasibility of using EWS to predict mood transitions in bipolar patients. Actigraphy data of 15 patients diagnosed with BD Type I collected continuously for 180 days were used. Our final sample included eight patients that experienced a mood episode, three manic episodes and five depressed episodes. Actigraphy data derived generic EWS (variance and kurtosis) and context-driven EWS (autocorrelation at lag-720) were used to determine if these were associated to upcoming bipolar episodes. Spectral analysis was used to predict changes in the periodicity of the sleep/wake cycle. The study procedures were pre-registered. Results indicated that in seven out of eight patients at least one of the EWS did show a significant change-up till four weeks before episode onset. For the generic EWS the direction of change was always in the expected direction, whereas for the context-driven EWS the observed effect was often in the direction opposite of what was expected. The actigraphy data derived EWS and spectral analysis showed promise for the prediction of upcoming transitions in mood episodes in bipolar patients. Further studies into false positive rates are suggested to improve effectiveness for EWS to identify upcoming bipolar episode onsets.

Early warning signals using AVaRs of infinitely divisible GARCH models — evidence from stock index markets

2015 ◽  
Vol 47 (43) ◽  
pp. 4630-4652 ◽  
Author(s):  
Chia-Chien Chang ◽  
Te-Chung Hu ◽  
Chiu-Fen Kao ◽  
Ya-Chi Chang
Keyword(s):  
Early Warning ◽  
Stock Index ◽  
Garch Models ◽  
Infinitely Divisible ◽  
Warning Signals ◽  
Early Warning Signals

scholarly journals Early warning signals in plant disease outbreaks

2019 ◽  
Vol 393 ◽  
pp. 12-19 ◽  
Author(s):  
S. Orozco-Fuentes ◽  
G. Griffiths ◽  
M.J. Holmes ◽  
R. Ettelaie ◽  
J. Smith ◽  
...  
Keyword(s):  
Early Warning ◽  
Plant Disease ◽  
Disease Outbreaks ◽  
Warning Signals ◽  
Early Warning Signals

scholarly journals Relation between stability and resilience determines the performance of early warning signals under different environmental drivers

2015 ◽  
Vol 112 (32) ◽  
pp. 10056-10061 ◽  
Author(s):  
Lei Dai ◽  
Kirill S. Korolev ◽  
Jeff Gore
Keyword(s):  
Early Warning ◽  
Alternative Stable States ◽  
Basin Of Attraction ◽  
Environmental Drivers ◽  
Ecological Communities ◽  
Stable States ◽  
Warning Signals ◽  
Early Warning Signals ◽  
The Stability ◽  
Stability And Resilience

Shifting patterns of temporal fluctuations have been found to signal critical transitions in a variety of systems, from ecological communities to human physiology. However, failure of these early warning signals in some systems calls for a better understanding of their limitations. In particular, little is known about the generality of early warning signals in different deteriorating environments. In this study, we characterized how multiple environmental drivers influence the dynamics of laboratory yeast populations, which was previously shown to display alternative stable states [Dai et al., Science, 2012]. We observed that both the coefficient of variation and autocorrelation increased before population collapse in two slowly deteriorating environments, one with a rising death rate and the other one with decreasing nutrient availability. We compared the performance of early warning signals across multiple environments as “indicators for loss of resilience.” We find that the varying performance is determined by how a system responds to changes in a specific driver, which can be captured by a relation between stability (recovery rate) and resilience (size of the basin of attraction). Furthermore, we demonstrate that the positive correlation between stability and resilience, as the essential assumption of indicators based on critical slowing down, can break down in this system when multiple environmental drivers are changed simultaneously. Our results suggest that the stability–resilience relation needs to be better understood for the application of early warning signals in different scenarios.

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