Four key challenges in infectious disease modelling using data from multiple sources.
Four key challenges in infectious disease modelling using data from multiple sources.
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DOI：
10.1016/j.epidem.2014.09.004
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发表时间：
2015-03
期刊：
影响因子：
3.8
通讯作者：
House, Thomas
中科院分区：
文献类型：
--
作者：
De Angelis, Daniela;Presanis, Anne M.;Birrell, Paul J.;Tomba, Gianpaolo Scalia;House, Thomas
健康决策越来越多地使用来自多个来源的模型和数据。使用多种数据源对模型参数进行推断具有挑战性。关键的挑战包括更周到的模型规范和批评。严格解决这些问题需要更好地利用现有工具。流行病模型的挑战可能会激发新的统计方法。旨在控制流行病的公共卫生相关政策决策越来越多地以证据为基础，利用多种数据来源。政策制定者依赖复杂的模型，这些模型必须是稳健的，现实地近似流行病，并与所有相关数据一致。以统计上严格和可验证的方式满足这些要求提出了一些具有挑战性的问题。如何对来自不同数据集的证据进行加权并处理它们之间的依赖关系，有效地估计和批判性地评估复杂模型是我们在本文中阐述的关键挑战，使用流感建模的例子。
Health decision making increasingly uses models and data from multiple sources. Inference on model parameters using a multiplicity of data sources is challenging. Key challenges include more thoughtful model specification and criticism. Addressing these problems rigorously will require better use of existing tools. Challenges in epidemic models may motivate new statistical methods. Public health-related decision-making on policies aimed at controlling epidemics is increasingly evidence-based, exploiting multiple sources of data. Policy makers rely on complex models that are required to be robust, realistically approximating epidemics and consistent with all relevant data. Meeting these requirements in a statistically rigorous and defendable manner poses a number of challenging problems. How to weight evidence from different datasets and handle dependence between them, efficiently estimate and critically assess complex models are key challenges that we expound in this paper, using examples from influenza modelling.
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影响因子：
3.7
作者：
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通讯作者：
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影响因子：
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作者：
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DOI：
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发表时间：
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期刊：
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