Detecting the Early Warning Signals of Disease Progression Based on Individual Difference Time Series Network

2019 ◽  
Vol 08 (01) ◽  
pp. 160-169
Author(s):  
小玲 关
Keyword(s):  
Time Series ◽  
Disease Progression ◽  
Individual Difference ◽  
Early Warning ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Difference Time

scholarly journals Change sign detection with differential MDL change statistics and its applications to COVID-19 pandemic analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kenji Yamanishi ◽  
Linchuan Xu ◽  
Ryo Yuki ◽  
Shintaro Fukushima ◽  
Chuan-hao Lin
Keyword(s):  
Time Series ◽  
Early Warning ◽  
Data Science ◽  
Minimum Description Length ◽  
Length Change ◽  
Warning Signals ◽  
Early Warning Signals ◽  
New Information ◽  
Long Time ◽  
Synthetic Datasets

AbstractWe are concerned with the issue of detecting changes and their signs from a data stream. For example, when given time series of COVID-19 cases in a region, we may raise early warning signals of an epidemic by detecting signs of changes in the data. We propose a novel methodology to address this issue. The key idea is to employ a new information-theoretic notion, which we call the differential minimum description length change statistics (D-MDL), for measuring the scores of change sign. We first give a fundamental theory for D-MDL. We then demonstrate its effectiveness using synthetic datasets. We apply it to detecting early warning signals of the COVID-19 epidemic using time series of the cases for individual countries. We empirically demonstrate that D-MDL is able to raise early warning signals of events such as significant increase/decrease of cases. Remarkably, for about $$64\%$$ 64 % of the events of significant increase of cases in studied countries, our method can detect warning signals as early as nearly six days on average before the events, buying considerably long time for making responses. We further relate the warning signals to the dynamics of the basic reproduction number R0 and the timing of social distancing. The results show that our method is a promising approach to the epidemic analysis from a data science viewpoint.

scholarly journals Early warning signals from the periphery

2021 ◽  
Author(s):  
Manfred Füllsack ◽  
Daniel Reisinger ◽  
Marie Kapeller ◽  
Georg Jäger
Keyword(s):  
Time Series ◽  
Social Interactions ◽  
Early Warning ◽  
Focus Attention ◽  
Warning Signals ◽  
Feedback Effects ◽  
Early Warning Signals ◽  
Agent Based ◽  
Critical Transitions ◽  
Micro Level

AbstractStudies on the possibility of predicting critical transitions with statistical methods known as early warning signals (EWS) are often conducted on data generated with equation-based models (EBMs). These models base on difference or differential equations, which aggregate a system’s components in a mathematical term and therefore do not allow for a detailed analysis of interactions on micro-level. As an alternative, we suggest a simple, but highly flexible agent-based model (ABM), which, when applying EWS-analysis, gives reason to (a) consider social interaction, in particular negative feedback effects, as an essential trigger of critical transitions, and (b) to differentiate social interactions, for example in network representations, into a core and a periphery of agents and focus attention on the periphery. Results are tested against time series from a networked version of the Ising-model, which is often used as example for generating hysteretic critical transitions.

scholarly journals Effect of time-series length and resolution on abundance- and trait-based early warning signals of population declines

2019 ◽  
Author(s):  
A.A. Arkilanian ◽  
C.F. Clements ◽  
A. Ozgul ◽  
G. Baruah
Keyword(s):  
Time Series ◽  
Early Warning ◽  
Generation Time ◽  
Time Series Data ◽  
Series Data ◽  
Series Length ◽  
Warning Signals ◽  
Relative Time ◽  
Early Warning Signals ◽  
Population Collapse

AbstractNatural populations are increasingly threatened with collapse at the hands of anthropogenic effects. Predicting population collapse with the help of generic early warning signals (EWS) may provide a prospective tool for identifying species or populations at highest risk. However, pattern-to-process methods such as EWS have a multitude of challenges to overcome to be useful, including the low signal to noise ratio of ecological systems and the need for high quality time-series data. The inclusion of trait dynamics with EWS has been proposed as a more robust tool to predict population collapse. However, the length and resolution of available time series are highly variable from one system to another, especially when generation time is considered. As yet it remains unknown how this variability with regards to generation time will alter the efficacy of EWS. Here we take both a simulation- and experimental-based approach to assess the impacts of relative time-series length and resolution on the forecasting ability of EWS. We show that EWS’ performance decreases with decreasing length and resolution. Our simulations suggest a relative time-series length between ten and five generations and a resolution of half a generation are the minimum requirements for accurate forecasting by abundance-based EWS. However, when trait information is included alongside abundance-based EWS, we find positive signals at lengths and resolutions half of what was required without them. We suggest that, in systems where specific traits are known to affect demography, trait data should be monitored and included alongside abundance data to improve forecasting reliability.

scholarly journals Eigenstate Transition of Multi-Channel Time Series Data around Earthquakes

2021 ◽  
Vol 11 (23) ◽  
pp. 11407
Author(s):  
Akihisa Okada ◽  
Yoshiyuki Kaneda
Keyword(s):  
Time Series ◽  
Early Warning ◽  
Time Series Data ◽  
Series Data ◽  
Economic Damage ◽  
First Eigenvalue ◽  
Warning Signals ◽  
Early Warning Signals ◽  
The First Eigenvalue ◽  
The Matrix

To decrease human and economic damage owing to earthquakes, it is necessary to discover signals preceding earthquakes. We focus on the concept of “early warning signals” developed in bifurcation analysis, in which an increase in the variances of variables precedes its transition. If we can treat earthquakes as one of the transition phenomena that moves from one state to the other state, this concept is useful for detecting earthquakes before they start. We develop a covariance matrix from multi-channel time series data observed by an observatory on the seafloor and calculate the first eigenvalue and corresponding eigenstate of the matrix. By comparing the time dependence of the eigenstate to some past earthquakes, it is shown that the contribution from specific observational channels to the eigenstate increases before earthquakes, and there is a case in which the eigenvalue increases as predicted in early warning signals. This result suggests the first eigenvalue and eigenstate of multi-channel data are useful to identify signals preceding earthquakes.

scholarly journals Detection of early warning signals in paleoclimate data using a genetic time series segmentation algorithm

2014 ◽  
Vol 44 (7-8) ◽  
pp. 1919-1933 ◽  
Author(s):  
Athanasia Nikolaou ◽  
Pedro Antonio Gutiérrez ◽  
Antonio Durán ◽  
Isabelle Dicaire ◽  
Francisco Fernández-Navarro ◽  
...  
Keyword(s):  
Time Series ◽  
Early Warning ◽  
Segmentation Algorithm ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Time Series Segmentation ◽  
Paleoclimate Data

scholarly journals Early warning signals predict emergence of COVID-19 waves

2021 ◽  
Author(s):  
Duncan A O'Brien ◽  
Christopher F Clements
Keyword(s):  
Time Series ◽  
Early Warning ◽  
Sequential Analysis ◽  
Time Series Data ◽  
Disease Outbreaks ◽  
Series Data ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Policy Makers ◽  
The Uk

Early warning signals (EWSs) aim to predict changes in complex systems from phenomenological signals in time series data. These signals have recently been shown to precede the initial emergence of disease outbreaks, offering hope that policy makers can make predictive rather than reactive management decisions. Here, using daily COVID-19 case data in combination with a novel, sequential analysis, we show that composite EWSs consisting of variance, autocorrelation, and return rate not only pre-empt the initial emergence of COVID-19 in the UK by 14 to 29 days, but also the following wave six months later. We also predict there is a high likelihood of a third wave as of the data available on 9th June 2021. Our work suggests that in highly monitored disease time series such as COVID-19, EWSs offer the opportunity for policy makers to improve the accuracy of time critical decisions based solely upon surveillance data.

scholarly journals Early Warning Signals Based on Momentary Affect Dynamics can Expose Nearby Transitions in Depression: A Confirmatory Single-Subject Time-Series Study

2020 ◽  
Vol 6 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Marieke Wichers ◽  
Arnout C. Smit ◽  
Evelien Snippe
Keyword(s):  
Time Series ◽  
Early Warning ◽  
Change Point ◽  
Network Connectivity ◽  
Change Point Analysis ◽  
Single Subject ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Point Analysis ◽  
Increased Risk

Background: In complex systems early warning signals such as rising autocorrelation, variance and network connectivity are hypothesized to anticipate relevant shifts in a system. For direct evidence hereof in depression, designs are needed in which early warning signals and symptom transitions are prospectively assessed within an individual. Therefore, this study aimed to detect personalized early warning signals preceding the occurrence of a major symptom transition. Methods: Six single- subject time-series studies were conducted, collecting frequent observations of momentary affective states during a time-period when participants were at increased risk of a symptom transition. Momentary affect states were reported three times a day over three to six months (95-183 days). Depressive symptoms were measured weekly using the Symptom CheckList-90. Presence of sudden symptom transitions was assessed using change point analysis. Early warning signals were analysed using moving window techniques. Results: As change point analysis revealed a significant and sudden symptom transition in one participant in the studied period, early warning signals were examined in this person. Autocorrelation (r=0·51; p<2.2e-16), and variance (r=0·53; p<2.2e-16) in ‘feeling down’, and network connectivity (r=0·42; p<2.2e-16) significantly increased a month before this transition occurred. These early warnings also preceded the rise in absolute levels of ‘feeling down’ and the participant’s personal indication of risk for transition. Conclusions: This study replicated the findings of a previous study and confirmed the presence of rising early warning signals a month before the symptom transition occurred. Results show the potential of early warning signals to improve personalized risk assessment in the field of psychiatry.

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