Centre for Infectious Diseases, University of Edinburgh, Ashworth Laboratories, Kings Buildings, West Mains Road, Edinburgh EH9 3JT, UK.
Philos Trans R Soc Lond B Biol Sci. 2011 Jul 12;366(1573):2045-54. doi: 10.1098/rstb.2010.0387.
Formal, quantitative approaches are now widely used to make predictions about the likelihood of an infectious disease outbreak, how the disease will spread, and how to control it. Several well-established methodologies are available, including risk factor analysis, risk modelling and dynamic modelling. Even so, predictive modelling is very much the 'art of the possible', which tends to drive research effort towards some areas and away from others which may be at least as important. Building on the undoubted success of quantitative modelling of the epidemiology and control of human and animal diseases such as AIDS, influenza, foot-and-mouth disease and BSE, attention needs to be paid to developing a more holistic framework that captures the role of the underlying drivers of disease risks, from demography and behaviour to land use and climate change. At the same time, there is still considerable room for improvement in how quantitative analyses and their outputs are communicated to policy makers and other stakeholders. A starting point would be generally accepted guidelines for 'good practice' for the development and the use of predictive models.
现在，人们广泛采用正式的定量方法来预测传染病爆发的可能性、疾病的传播方式以及如何控制疾病。目前有几种成熟的方法，包括风险因素分析、风险建模和动态建模。即便如此，预测建模仍然非常“受限于可能的情况”，这往往会促使研究工作集中在某些方面，而忽略了其他可能同样重要的方面。在艾滋病、流感、口蹄疫和疯牛病等人类和动物疾病的流行病学和控制的定量建模取得巨大成功的基础上，需要关注建立一个更全面的框架，以捕捉疾病风险的潜在驱动因素的作用，这些驱动因素包括人口统计学和行为、土地利用和气候变化等。同时，如何向政策制定者和其他利益相关者传达定量分析及其结果，仍然有很大的改进空间。一个起点是为预测模型的开发和使用制定普遍接受的“良好实践”指南。
