ArticlePLoS biology2012
How to make epidemiological training infectious.
Article in PLoS biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 21 citations in OpenAlex.
- Trends in acute respiratory infection and associated factors among under-5 children in Ethiopia.BMC pediatrics · 2026Article
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- Facilitating Understanding, Modeling and Simulation of Infectious Disease Epidemics in the Age of COVID-19.Frontiers in public health · 2021Article
- A multilevel analysis of the determinants of handwashing behavior among households in Eswatini: a secondary analysis of the 2014 multiple indicator cluster survey.African health sciences · 2020Article
- The Zombie Virus Pandemic: An Innovative Simulation Integrating Virology, Population Health, and Bioethics for Preclinical Medical Students.MedEdPORTAL : the journal of teaching and learning resources · 2020Article
- How to choose the best control strategy? Mathematical models as a tool for pre-intervention evaluation on a macroparasitic disease.PLoS neglected tropical diseases · 2020Article
- How Modelling Can Enhance the Analysis of Imperfect Epidemic Data.Trends in parasitology · 2019Review
- An infectious way to teach students about outbreaks.Epidemics · 2018Article
- Learning infectious disease epidemiology in a modern framework.PLoS computational biology · 2017Article
- Temporally Varying Relative Risks for Infectious Diseases: Implications for Infectious Disease Control.Epidemiology (Cambridge, Mass.) · 2017Review
- Reassessment of HIV-1 acute phase infectivity: accounting for heterogeneity and study design with simulated cohorts.PLoS medicine · 2015Article
- Estimating the per-exposure effect of infectious disease interventions.Epidemiology (Cambridge, Mass.) · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
Abstract
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.