scholarly journals Deciphering early-warning signals of the elimination and resurgence potential of SARS-CoV-2 from limited data at multiple scales

2020 ◽  
Author(s):  
Kris V Parag ◽  
Benjamin J Cowling ◽  
Christl A Donnelly
Keyword(s):  
Early Warning ◽  
Disease Transmission ◽  
Multiple Scales ◽  
Region Of Interest ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Transmission Potential ◽  
Incidence Data ◽  
The Difference ◽  
Low Incidence

AbstractInferring the transmission potential of an infectious disease during the low-incidence period following an epidemic wave is crucial for preparedness. In this period, necessarily scarce data hamper existing inference methods, blurring early-warning signals essential for discriminating between the likelihoods of resurgence versus elimination. Advanced insight into whether a region of interest will face elevating caseloads (requiring swift community-wide interventions) or achieve local elimination (allowing interventions to be relaxed or refocussed on controlling the importation of infections), can be the difference between decisive and ineffective policy. We propose a novel early-warning framework that formally maximises information extracted from low-incidence data to robustly infer the chances of sustained local transmission or elimination in real time, at any desired scale of investigation. Applying this framework, we decipher previously hidden disease-transmission signals from the prolonged low-incidence COVID-19 data of New Zealand, Hong Kong and Victoria state, Australia. We uncover how timely interventions averted dangerous, resurgent waves of COVID-19 and support official declarations of elimination. Across these locations, we obtain strong evidence for the effectiveness of rapid and adaptive COVID-19 responses.

scholarly journals Deciphering early-warning signals of SARS-CoV-2 elimination and resurgence from limited data at multiple scales

2021 ◽  
Vol 18 (185) ◽  
Author(s):  
Kris V. Parag ◽  
Benjamin J. Cowling ◽  
Christl A. Donnelly
Keyword(s):  
Early Warning ◽  
Disease Transmission ◽  
Multiple Scales ◽  
Limited Data ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Transmission Potential ◽  
Incidence Data ◽  
Low Incidence ◽  
Inference Methods

Inferring the transmission potential of an infectious disease during low-incidence periods following epidemic waves is crucial for preparedness. In such periods, scarce data may hinder existing inference methods, blurring early-warning signals essential for discriminating between the likelihoods of resurgence versus elimination. Advanced insight into whether elevating caseloads (requiring swift community-wide interventions) or local elimination (allowing controls to be relaxed or refocussed on case-importation) might occur can separate decisive from ineffective policy. By generalizing and fusing recent approaches, we propose a novel early-warning framework that maximizes the information extracted from low-incidence data to robustly infer the chances of sustained local transmission or elimination in real time, at any scale of investigation (assuming sufficiently good surveillance). Applying this framework, we decipher hidden disease-transmission signals in prolonged low-incidence COVID-19 data from New Zealand, Hong Kong and Victoria, Australia. We uncover how timely interventions associate with averting resurgent waves, support official elimination declarations and evidence the effectiveness of the rapid, adaptive COVID-19 responses employed in these regions.

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 Catching Critical Transition in Engineered Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jin Huang ◽  
Tianchuang Meng ◽  
Yangdong Deng ◽  
Fanling Huang
Keyword(s):  
Early Warning ◽  
Multiple Scales ◽  
Stable State ◽  
Critical Threshold ◽  
Supporting Evidence ◽  
Critical Transition ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Critical Transitions ◽  
Engineered Systems

A variety of engineered systems can encounter critical transitions where the system suddenly shifts from one stable state to another at a critical threshold. The critical transition has aroused vital concerns for its potentially disastrous impacts. We validate an often taken-for-granted hypothesis that the failure of engineered systems can be attributed to the respective critical transitions and show how early warning signals are closely associated with critical transitions. We demonstrate that it is feasible to use early warning signals to predict system failures. Our findings open a new path to forecast failures of engineered systems with a generic method and provide supporting evidence for the universal existence of critical transition in dynamical systems at multiple scales.

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
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