ArticleNature communications2019
On the predictability of infectious disease outbreaks.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers.
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Who cites it
89 citing papers in PubMed.
- Quantifying the Dynamics of Network Detachment Across Scientific, Technological, Commercial, and Pharmacological Domains.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A real-time forecasting framework for emerging infectious diseases affecting animal populations.PLoS computational biology · 2026Article
- Unifying network connectivity from geodesics to random walks via the random cluster model.Nature communications · 2026Article
- Forecastability of infectious disease time series: are some seasons and pathogens intrinsically more difficult to forecast?PLoS computational biology · 2026Article
- Endemic-epidemic modeling of the COVID-19 pandemic in South Africa.BMC infectious diseases · 2026Article
- Real-time dynamic prediction of HFMD transmission using SEIRQ-ARIMA hybrid model optimized by multi-stage ABC-GWO algorithm.Scientific reports · 2026Article
- Uncovering heterogeneous intercommunity disease transmission from neutral allele frequency time series.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Detection of pre-transition phases during biological development using single-sample network entropy (SNE).NPJ systems biology and applications · 2025Article
- Spread of the plague in Venice, 1630-1631: epidemic entropy in a "natural experiment".medRxiv : the preprint server for health sciences · 2025Article
- Predictability of infectious disease outbreak severity: Chikungunya as a case study.Science advances · 2025Article
- The dynamics ofmSphere · 2025Article
- Revealing unseen dynamical regimes of ecosystems from population time-series data.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Leveraging multivariate information for community detection in functional brain networks.Communications biology · 2025Article
- Quantum-Like Approaches Unveil the Intrinsic Limits of Predictability in Compartmental Models.Entropy (Basel, Switzerland) · 2024Article
- Predictability of human mobility during the COVID-19 pandemic in the United States.PNAS nexus · 2024Article
- Evolutionary Invasion Analysis of Modern Epidemics Highlights the Context-Dependence of Virulence Evolution.Bulletin of mathematical biology · 2024Article
- Purpose-oriented review of public health surveillance systems: use of surveillance systems and recent advances.BMJ public health · 2024Review
- Duality between predictability and reconstructability in complex systems.Nature communications · 2024Article
- District-Level Forecast of Achieving Trachoma Elimination as a Public Health Problem By 2030: An Ensemble Modelling Approach.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2024Article
- Differences in COVID-19 cyclicity and predictability among U.S. counties and states reflect the effectiveness of protective measures.Scientific reports · 2023Article
29 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Infectious disease outbreaks recapitulate biology: they emerge from the multi-level interaction of hosts, pathogens, and environment. Therefore, outbreak forecasting requires an integrative approach to modeling. While specific components of outbreaks are predictable, it remains unclear whether fundamental limits to outbreak prediction exist. Here, adopting permutation entropy as a model independent measure of predictability, we study the predictability of a diverse collection of outbreaks and identify a fundamental entropy barrier for disease time series forecasting. However, this barrier is often beyond the time scale of single outbreaks, implying prediction is likely to succeed. We show that forecast horizons vary by disease and that both shifting model structures and social network heterogeneity are likely mechanisms for differences in predictability. Our results highlight the importance of embracing dynamic modeling approaches, suggest challenges for performing model selection across long time series, and may relate more broadly to the predictability of complex adaptive systems.
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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.