4.6 Article

How to Make Epidemiological Training Infectious

期刊

PLOS BIOLOGY
卷 10, 期 4, 页码 -

出版社

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pbio.1001295

关键词

-

资金

  1. National Institute of Health [R24TW008822, GM83863]
  2. Henry Wheeler Center for Emerging and Neglected Diseases
  3. African Biomathematics Initiative (National Science Foundation) [DMS-829652]
  4. NSF-NIH Ecology of Infectious Diseases [1134964]
  5. Center for Discrete Mathematics and Theoretical Computer Science
  6. African Institute for Mathematical Sciences
  7. South African Centre for Excellence in Epidemiological Modelling and Analysis
  8. Chang-Lin Tien Environmental Fellowship
  9. Andrew Fellowship
  10. University of California
  11. Mary Thompson Rocca Fellowship
  12. Berkeley Environmental Science, Policy & Management and Entomology Society
  13. National Institute of Health Ecology of Infectious Disease [GM83863]
  14. Research and Policy in Infectious Disease Dynamics (RAPIDD) of the Science and Technology Directorate
  15. Department of Homeland Security
  16. Fogarty International Center, National Institutes of Health
  17. Direct For Biological Sciences
  18. Division Of Environmental Biology [1134964] Funding Source: National Science Foundation
  19. Direct For Mathematical & Physical Scien
  20. Division Of Mathematical Sciences [0829652] Funding Source: National Science Foundation

向作者/读者索取更多资源

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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据