ArticlePLoS biology2020
Dynamical footprints enable detection of disease emergence.
Article in PLoS biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review.Frontiers in public health · 2022Pooled it
- Generative embedding of sparse data with a tabular foundation model for dengue anticipatory action: a machine learning approach.medRxiv : the preprint server for health sciences · 2026Article
- Early Warning Signals in Ecological Time-Series.Entropy (Basel, Switzerland) · 2026Review
- Interpretable early warnings using machine learning in an online game-experiment.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Deep learning for disease outbreak prediction: a parallel LSTM-CNN model.Journal of the Royal Society, Interface · 2025Article
- Future Issues in Global Health: Challenges and Conundrums.International journal of environmental research and public health · 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
- 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
- Data-driven prediction in dynamical systems: recent developments.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- Anticipating infectious disease re-emergence and elimination: a test of early warning signals using empirically based models.Journal of the Royal Society, Interface · 2022Article
- Performance of early warning signals for disease re-emergence: A case study on COVID-19 data.PLoS computational biology · 2022Article
- Overlapping timescales obscure early warning signals of the second COVID-19 wave.Proceedings. Biological sciences · 2022Article
- The epidemic volatility index, a novel early warning tool for identifying new waves in an epidemic.Scientific reports · 2021Article
- Early warning signals of infectious disease transitions: a review.Journal of the Royal Society, Interface · 2021Review
- Ignoring spatial heterogeneity in drivers of SARS-CoV-2 transmission in the US will impede sustained elimination.medRxiv : the preprint server for health sciences · 2021Article
- Transient indicators of tipping points in infectious diseases.Journal of the Royal Society, Interface · 2020Article
- Dynamical footprints enable detection of disease emergence.PLoS biology · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Developing methods for anticipating the emergence or reemergence of infectious diseases is both important and timely; however, traditional model-based approaches are stymied by uncertainty surrounding the underlying drivers. Here, we demonstrate an operational, mechanism-agnostic detection algorithm for disease (re-)emergence based on early warning signals (EWSs) derived from the theory of critical slowing down. Specifically, we used computer simulations to train a supervised learning algorithm to detect the dynamical footprints of (re-)emergence present in epidemiological data. Our algorithm was then challenged to forecast the slowly manifesting, spatially replicated reemergence of mumps in England in the mid-2000s and pertussis post-1980 in the United States. Our method successfully anticipated mumps reemergence 4 years in advance, during which time mitigation efforts could have been implemented. From 1980 onwards, our model identified resurgent states with increasing accuracy, leading to reliable classification starting in 1992. Additionally, we successfully applied the detection algorithm to 2 vector-transmitted case studies, namely, outbreaks of dengue serotypes in Puerto Rico and a rapidly unfolding outbreak of plague in 2017 in Madagascar. Taken together, these findings illustrate the power of theoretically informed machine learning techniques to develop early warning systems for the (re-)emergence of infectious diseases.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.