Department of Infectious Disease Epidemiology, Imperial College London, UK.
Department of Infectious Disease Epidemiology, Imperial College London, UK.
Epidemics. 2018 Jun;23:42-48. doi: 10.1016/j.epidem.2017.12.002. Epub 2017 Dec 9.
The study of infectious disease outbreaks is required to train today's epidemiologists. A typical way to introduce and explain key epidemiological concepts is through the analysis of a historical outbreak. There are, however, few training options that explicitly utilise real-time simulated stochastic outbreaks where the participants themselves comprise the dataset they subsequently analyse. In this paper, we present a teaching exercise in which an infectious disease outbreak is simulated over a five-day period and subsequently analysed. We iteratively developed the teaching exercise to offer additional insight into analysing an outbreak. An R package for visualisation, analysis and simulation of the outbreak data was developed to accompany the practical to reinforce learning outcomes. Computer simulations of the outbreak revealed deviations from observed dynamics, highlighting how simplifying assumptions conventionally made in mathematical models often differ from reality. Here we provide a pedagogical tool for others to use and adapt in their own settings.
传染病爆发研究是培训今天的流行病学家所必需的。介绍和解释关键流行病学概念的一种典型方法是通过分析历史爆发。然而，很少有培训选项明确利用实时模拟随机爆发，其中参与者自己构成他们随后分析的数据集。在本文中，我们提出了一个教学练习，其中在五天的时间内模拟传染病爆发，然后对其进行分析。我们迭代地开发了教学练习，以提供对分析爆发的额外见解。为了配合实践，开发了一个用于可视化、分析和模拟爆发数据的 R 包，以加强学习成果。对爆发的计算机模拟显示出与观察到的动态的偏差，突出了在数学模型中通常做出的简化假设如何常常与现实不同。在这里，我们提供了一个教学工具，供其他人在自己的环境中使用和改编。
