ArticlePLoS computational biology2022
Performance of early warning signals for disease re-emergence: A case study on COVID-19 data.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Integrated Deep Learning Surveillance of Unknown Pathogens with Pandemic Potential Using Pneumonia of Unknown Etiology.Pathogens (Basel, Switzerland) · 2026Article
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- A method to identify positive tipping points to accelerate low-carbon transitions and actions to trigger them.Sustainability science · 2026Article
- Identifying COVID-19 peaks using early warning signals.PLoS computational biology · 2025Article
- Quantifying Landscape and Flux from Single-Cell Omics: Unraveling the Physical Mechanisms of Cell Function.JACS Au · 2025Review
- Deep learning for disease outbreak prediction: a parallel LSTM-CNN model.Journal of the Royal Society, Interface · 2025Article
- Combining machine learning and dynamic system techniques to early detection of respiratory outbreaks in routinely collected primary healthcare records.BMC medical research methodology · 2025Article
- Early detection of disease outbreaks and non-outbreaks using incidence data: A framework using feature-based time series classification and machine learning.PLoS computational biology · 2025Article
- Early warning signals do not predict a warming-induced experimental epidemic.PLOS global public health · 2025Article
- An early warning indicator trained on stochastic disease-spreading models with different noises.Journal of the Royal Society, Interface · 2024Article
- Application of early warning signs to physiological contexts: a comparison of multivariate indices in patients on long-term hemodialysis.Frontiers in network physiology · 2024Article
- COVID-19 Pandemic Risk Assessment: Systematic Review.Risk management and healthcare policy · 2024Review
- Attractor landscapes: a unifying conceptual model for understanding behaviour change across scales of observation.Health psychology review · 2023Article
- Systematic analysis and optimization of early warning signals for critical transitions using distribution data.iScience · 2023Article
- The convergence epidemic volatility index (cEVI) as an alternative early warning tool for identifying waves in an epidemic.Infectious Disease Modelling · 2023Article
- The potential of resilience indicators to anticipate infectious disease outbreaks, a systematic review and guide.PLOS global public health · 2023Article
- Estimating the basic reproduction number from noisy daily data.Journal of theoretical biology · 2022Article
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4 authors.
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Abstract
Developing measures for rapid and early detection of disease re-emergence is important to perform science-based risk assessment of epidemic threats. In the past few years, several early warning signals (EWS) from complex systems theory have been introduced to detect impending critical transitions and extend the set of indicators. However, it is still debated whether they are generically applicable or potentially sensitive to some dynamical characteristics such as system noise and rates of approach to critical parameter values. Moreover, testing on empirical data has, so far, been limited. Hence, verifying EWS performance remains a challenge. In this study, we tackle this question by analyzing the performance of common EWS, such as increasing variance and autocorrelation, in detecting the emergence of COVID-19 outbreaks in various countries. Our work illustrates that these EWS might be successful in detecting disease emergence when some basic assumptions are satisfied: a slow forcing through the transitions and not-fat-tailed noise. In uncertain cases, we observe that noise properties or commensurable time scales may obscure the expected early warning signals. Overall, our results suggest that EWS can be useful for active monitoring of epidemic dynamics, but that their performance is sensitive to certain features of the underlying dynamics. Our findings thus pave a connection between theoretical and empirical studies, constituting a further step towards the application of EWS indicators for informing public health policies.
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