ArticlePLoS computational biology2019
Long-term dynamics of measles in London: Titrating the impact of wars, the 1918 pandemic, and vaccination.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed.
- Toward AI foundation models for epidemics: Promise, challenges, and paths forward.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Bias in the estimated reporting fraction due to vaccination in the time-series SIR model.PloS one · 2025Article
- Deep neural networks for endemic measles dynamics: Comparative analysis and integration with mechanistic models.PLoS computational biology · 2024Article
- Increasing intensity of enterovirus outbreaks projected with climate change.Nature communications · 2024Article
- Comparing and linking machine learning and semi-mechanistic models for the predictability of endemic measles dynamics.PLoS computational biology · 2022Article
- Development and dissemination of infectious disease dynamic transmission models during the COVID-19 pandemic: what can we learn from other pathogens and how can we move forward?The Lancet. Digital health · 2021Review
- Transforming public health using value lens and extended partner networks.Learning health systems · 2021Article
- The impact of COVID-19 nonpharmaceutical interventions on the future dynamics of endemic infections.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Patterns of smallpox mortality in London, England, over three centuries.PLoS biology · 2020Article
- How can contemporary climate research help understand epidemic dynamics? Ensemble approach and snapshot attractors.Journal of the Royal Society, Interface · 2020Article
- Coexisting attractors in the context of cross-scale population dynamics: measles in London as a case study.Proceedings. Biological sciences · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
A key question in ecology is the relative impact of internal nonlinear dynamics and external perturbations on the long-term trajectories of natural systems. Measles has been analyzed extensively as a paradigm for consumer-resource dynamics due to the oscillatory nature of the host-pathogen life cycle, the abundance of rich data to test theory, and public health relevance. The dynamics of measles in London, in particular, has acted as a prototypical test bed for such analysis using incidence data from the pre-vaccination era (1944-1967). However, during this timeframe there were few external large-scale perturbations, limiting an assessment of the relative impact of internal and extra demographic perturbations to the host population. Here, we extended the previous London analyses to include nearly a century of data that also contains four major demographic changes: the First and Second World Wars, the 1918 influenza pandemic, and the start of a measles mass vaccination program. By combining mortality and incidence data using particle filtering methods, we show that a simple stochastic epidemic model, with minimal historical specifications, can capture the nearly 100 years of dynamics including changes caused by each of the major perturbations. We show that the majority of dynamic changes are explainable by the internal nonlinear dynamics of the system, tuned by demographic changes. In addition, the 1918 influenza pandemic and World War II acted as extra perturbations to this basic epidemic oscillator. Our analysis underlines that long-term ecological and epidemiological dynamics can follow very simple rules, even in a non-stationary population subject to significant perturbations and major secular changes.
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