Evidence map›Paper›PMID 31513578›Full record

ArticlePLoS computational biology2019

Long-term dynamics of measles in London: Titrating the impact of wars, the 1918 pandemic, and vaccination.

Alexander D Becker, Amy Wesolowski, Ottar N Bjørnstad, Bryan T Grenfell

Abstract readHistorical Article
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Toward AI foundation models for epidemics: Promise, challenges, and paths forward.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  2. Article
  3. Article
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  5. Article
  6. Review
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  8. 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 · 2020
    Article
  9. Article
  10. Article
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Alexander D BeckerDepartment of Ecology and Evolutionary Biology, Princeton, New Jersey, United States of America.ORCID 0000-0001-6524-0513
Amy WesolowskiDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.ORCID 0000-0001-6320-3575
Ottar N BjørnstadCenter for Infectious Disease Dynamics, Pennsylvania State University, University Park, Pennsylvania, United States of America.
Bryan T GrenfellDepartment of Ecology and Evolutionary Biology, Princeton, New Jersey, United States of America.

Funding

Disease Emergence and Elimination: Using Mobility Data to Inform Spatial Disease DynamicsDP2LM013102 · NLM · JOHNS HOPKINS UNIVERSITY · PI WESOLOWSKI, AMY · 2018 to 2018
$2.5M
Scientific and Technical Computing CoreP2CHD047879 · NICHD · PRINCETON UNIVERSITY · PI SALGANIK, MATTHEW J. · 2014 to 2018
$2.0M
NICHD NIH HHS P2C HD047879NLM NIH HHS DP2 LM013102Wellcome Trust
6 · The paper itself

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.

Indexed as

MeaslesComputational BiologyHistory, 20th CenturyHumansIncidenceInfluenza, HumanLondonPandemicsVaccinationWorld War IWorld War II

Identifiers

PMID31513578
PMCPMC6742223

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.