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Forecasting Epidemiological and Evolutionary Dynamics of Infectious Diseases

机译:预测传染病流行病学和进化动态

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摘要

Mathematical models have been powerful tools in developing mechanistic understanding of infectious diseases. Furthermore, they have allowed detailed forecasting of epidemiological phenomena such as outbreak size, which is of considerable public-health relevance. The short generation time of pathogens and the strong selection they are subjected to (by host immunity, vaccines, chemotherapy, etc.) mean that evolution is also a key driver of infectious disease dynamics. Accurate forecasting of pathogen dynamics therefore calls for the integration of epidemiological and evolutionary processes, yet this integration remains relatively rare. We review previous attempts to model and predict infectious disease dynamics with or without evolution and discuss major chal- lenges facing the development of the emerging science of epidemic forecasting.
机译:数学模型是制定对传染病的机械理解的强大工具。 此外,他们允许详细预测流行病学现象,如爆发规模,这具有相当大的公共卫生的相关性。 病原体的短代时间和强烈选择它们受到它们的强烈选择(通过宿主免疫,疫苗,化学疗法等)意味着进化也是传染病动力学的关键驱动因素。 因此,准确的病原体动力学预测因此要求流行病学和进化过程的整合,但这种整合仍然相对较少。 我们审查以前的尝试模拟和预测传染病动力学,没有演变,并讨论了流行性预测的新兴科学发展的主要挑战。

著录项

  • 来源
    《Trends in Ecology & Evolution》 |2016年第10期|共13页
  • 作者单位

    CEFE UMR 5175 CNRS-Université de Montpellier-Université Paul-Valéry Montpellier-EPHE 1919 route de Mende 34293 Montpellier cedex 5 France;

    Department of Biology Queen's University Kingston Canada;

    Department of Ecology and Evolutionary Biology and Woodrow Wilson School of Public and International Affairs Princeton University Princeton NJ USA;

    Department of Ecology and Evolutionary Biology and Woodrow Wilson School of Public and International Affairs Princeton University Princeton NJ USA;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 数学生态学与生物模型;
  • 关键词

    powerful; generation; selection;

    机译:强大;一代;选择;

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