How to Make Epidemiological Training Infectious
How to Make Epidemiological Training Infectious
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DOI：
10.1371/journal.pbio.1001295
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发表时间：
2012-04-01
期刊：
影响因子：
9.8
通讯作者：
Dushoff, Jonathan
中科院分区：
文献类型：
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作者：
Bellan, Steve E.;Pulliam, Juliet R. C.;Dushoff, Jonathan
现代传染病流行病学建立在两个独立发展的领域之上：经典流行病学和动态流行病学。在过去十年中，这两个领域在研究实践中的整合有所增加，但领域内的培训选择仍然不同，在课堂上整合的机会很少。非洲数学科学研究所的年度流行病学数据有意义建模诊所(MMEd)已经开始解决这一差距。MMED让参与者接触到来自流行病学传统的广泛概念和技术。在2010年MMED期间，我们开发了一种教学方法，在经典流行病学和动态流行病学之间的传统区别之间架起了桥梁，并可用于从高中到研究生水平的多个教育级别。这种方法是亲力亲为的，包括对课程参与者的随机疫情进行实时模拟，包括现实的数据报告，然后进行各种数学和统计分析，这两种传统都源于流行病学。在演习中，动态流行病学家发展了研究设计等经验技能，并学习了偏差的概念，而经典流行病学家则接受了系统思维的培训，开始将流行病理解为动态的非线性过程。我们相信，这种类型的综合教育工具将被证明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，随着非洲继续培养新的训练有素的数学家、统计学家和科学家，这种跨学科培训对当地分析流行病学的能力建设至关重要。由于这些课程借鉴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力学、传染病的生态学到生物信息学、计算生物学和统计学-这一练习可以纳入广泛的生命科学课程。
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.