2 datasets found
  1. Long-term dynamics of measles in London: Titrating the impact of wars, the...

    • plos.figshare.com
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    Updated Jun 1, 2023
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    Alexander D. Becker; Amy Wesolowski; Ottar N. Bjørnstad; Bryan T. Grenfell (2023). Long-term dynamics of measles in London: Titrating the impact of wars, the 1918 pandemic, and vaccination [Dataset]. http://doi.org/10.1371/journal.pcbi.1007305
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    docxAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Alexander D. Becker; Amy Wesolowski; Ottar N. Bjørnstad; Bryan T. Grenfell
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    London
    Description

    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.

  2. H

    Deaths from all causes in Western Europe by month, 1914-1918, from Bunle, H....

    • dataverse.harvard.edu
    Updated Aug 24, 2020
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    Alexander More (2020). Deaths from all causes in Western Europe by month, 1914-1918, from Bunle, H. (1954). Le Mouvement naturel de la population dans le monde de 1906 à 1936. Paris, Institut national d’études démographiques, pp. 432-438. [Dataset]. http://doi.org/10.7910/DVN/GW0DGF
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 24, 2020
    Dataset provided by
    Harvard Dataverse
    Authors
    Alexander More
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    Europe de l’Ouest, Paris
    Description

    Dataset title: Deaths from all causes in Western Europe by month, 1914-1918 Related publication: More, A. F. et al. (2020). The impact of a six-year climate anomaly on the ‘Spanish Flu’ Pandemic and WWI. GeoHealth, American Geophysical Union. Figures 2 and 3. Dataset source: Bunle, H. (1954). Le Mouvement naturel de la population dans le monde de 1906 à 1936. Paris, Institut national d’études démographiques, pp. 432-438. N.B. Please cite the original source if you use this dataset. N.B. Please note that Bunle did not publish mortality statistics for Belgium, Bulgaria, and several other countries for the period 1914-20 due to his inability to find reliable sources, as indicated in his footnotes and on p. 12. This dataset includes countries of western Europe with the most reliable data. Units: Thousands of deaths. Each monthly figure should be multiplied by 1000 to obtain the total deaths for a specific month. Each year is divided in 12 monthly entries, with decimals increasing by 0.083 (1/12) for each month.

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Alexander D. Becker; Amy Wesolowski; Ottar N. Bjørnstad; Bryan T. Grenfell (2023). Long-term dynamics of measles in London: Titrating the impact of wars, the 1918 pandemic, and vaccination [Dataset]. http://doi.org/10.1371/journal.pcbi.1007305
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Long-term dynamics of measles in London: Titrating the impact of wars, the 1918 pandemic, and vaccination

Explore at:
16 scholarly articles cite this dataset (View in Google Scholar)
docxAvailable download formats
Dataset updated
Jun 1, 2023
Dataset provided by
PLOShttp://plos.org/
Authors
Alexander D. Becker; Amy Wesolowski; Ottar N. Bjørnstad; Bryan T. Grenfell
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Area covered
London
Description

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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