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

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

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.Author summary: The impact of intrinsic versus external drivers of transmission on long-term dynamics is an open question in complex systems studies. In particular, when and where dynamics become chaotic has crucial implications for control efforts. Here, we extended the well-studied London measles data to include nearly a century of novel data (1897–1991) that also contains five major demographic changes: the First and Second World Wars, the wartime evacuation of London, the 1918 influenza pandemic, and the start of a measles mass vaccination program. We found 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 further illustrated that the majority of dynamic changes are explainable by the internal nonlinear dynamics of the system, tuned by demographic changes. Notably however, the 1918 influenza pandemic and evacuation acted as external perturbations to this basic epidemic oscillator. Yet, in the wake of these massive shifts, the overall system remained stable (Lyapunov exponent

Suggested Citation

  • Alexander D Becker & Amy Wesolowski & Ottar N Bjørnstad & Bryan T Grenfell, 2019. "Long-term dynamics of measles in London: Titrating the impact of wars, the 1918 pandemic, and vaccination," PLOS Computational Biology, Public Library of Science, vol. 15(9), pages 1-14, September.
  • Handle: RePEc:plo:pcbi00:1007305
    DOI: 10.1371/journal.pcbi.1007305
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    References listed on IDEAS

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    1. Matthew J. Ferrari & Rebecca F. Grais & Nita Bharti & Andrew J. K. Conlan & Ottar N. Bjørnstad & Lara J. Wolfson & Philippe J. Guerin & Ali Djibo & Bryan T. Grenfell, 2008. "The dynamics of measles in sub-Saharan Africa," Nature, Nature, vol. 451(7179), pages 679-684, February.
    2. B. T. Grenfell & O. N. Bjørnstad & J. Kappey, 2001. "Travelling waves and spatial hierarchies in measles epidemics," Nature, Nature, vol. 414(6865), pages 716-723, December.
    3. P. Rohani & C. J. Green & N. B. Mantilla-Beniers & B. T. Grenfell, 2003. "Ecological interference between fatal diseases," Nature, Nature, vol. 422(6934), pages 885-888, April.
    4. B. F. Finkenstädt & B. T. Grenfell, 2000. "Time series modelling of childhood diseases: a dynamical systems approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(2), pages 187-205.
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