Department of Environmental Science, Policy & Management, University of California, Berkeley, California, United States of America.
PLoS Biol. 2012;10(4):e1001295. doi: 10.1371/journal.pbio.1001295. Epub 2012 Apr 3.
Modern infectious disease epidemiology builds on two independently developed fields: classical epidemiology and dynamical epidemiology. Over the past decade, integration of the two fields has increased in research practice, but training options within the fields remain distinct with few opportunities for integration in the classroom. The annual Clinic on the Meaningful Modeling of Epidemiological Data (MMED) at the African Institute for Mathematical Sciences has begun to address this gap. MMED offers participants exposure to a broad range of concepts and techniques from both epidemiological traditions. During MMED 2010 we developed a pedagogical approach that bridges the traditional distinction between classical and dynamical epidemiology and can be used at multiple educational levels, from high school to graduate level courses. The approach is hands-on, consisting of a real-time simulation of a stochastic outbreak in course participants, including realistic data reporting, followed by a variety of mathematical and statistical analyses, stemming from both epidemiological traditions. During the exercise, dynamical epidemiologists developed empirical skills such as study design and learned concepts of bias while classical epidemiologists were trained in systems thinking and began to understand epidemics as dynamic nonlinear processes. We believe this type of integrated educational tool will prove extremely valuable in the training of future infectious disease epidemiologists. We also believe that such interdisciplinary training will be critical for local capacity building in analytical epidemiology as Africa continues to produce new cohorts of well-trained mathematicians, statisticians, and scientists. And because the lessons draw on skills and concepts from many fields in biology--from pathogen biology, evolutionary dynamics of host--pathogen interactions, and the ecology of infectious disease to bioinformatics, computational biology, and statistics--this exercise can be incorporated into a broad array of life sciences courses.
经典流行病学和动力流行病学。在过去的十年中，这两个领域的融合在研究实践中有所增加，但这两个领域的培训选项仍然存在明显的差异，课堂上几乎没有整合的机会。非洲数学科学研究所的年度有意义的流行病学数据建模研讨会（MMED）开始解决这一差距。MMED 让参与者接触到来自两个流行病学传统的广泛概念和技术。在 MMED 2010 期间，我们开发了一种教学方法，弥合了经典流行病学和动力流行病学之间的传统区别，可以在从高中到研究生水平的多个教育层次上使用。该方法是实践导向的，包括对课程参与者中的随机疫情进行实时模拟，包括真实的数据报告，然后进行各种数学和统计分析，这些分析源自两个流行病学传统。在该练习中，动力流行病学家发展了实证技能，例如研究设计，并了解了来自经典流行病学传统的偏见概念；而经典流行病学家则接受了系统思维的培训，并开始将传染病视为动态非线性过程。我们相信这种类型的综合教育工具将在未来传染病流行病学家的培训中非常有价值。我们还认为，随着非洲不断培养出新一代训练有素的数学家、统计学家和科学家，这种跨学科培训对于分析流行病学的本地能力建设将至关重要。而且，由于这些课程汲取了生物学中许多领域的技能和概念——从病原体生物学、宿主-病原体相互作用的进化动态，到传染病生态学，再到生物信息学、计算生物学和统计学——因此这个练习可以纳入广泛的生命科学课程中。
