Evidence map›Paper›PMID 22509129›Full record

ArticlePLoS biology2012

How to make epidemiological training infectious.

Steve E Bellan, Juliet R C Pulliam, James C Scott, Jonathan Dushoff, MMED Organizing Committee

Open access · goldAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.3field-weighted citation impact, top 21% of its field
1 · What the graph read from it

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 21 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 4 institutions in 2 countries.

Steve E BellanDepartment of Environmental Science, Policy & Management, University of California, Berkeley, California, United States of America. steve.bellan@gmail.com
Juliet R C Pulliam
James C Scott
Jonathan Dushoff
MMED Organizing Committee
Colby College · USMcMaster University · CAUniversity of California, Berkeley · USUniversity of Florida · US

Funding

The ecology of environmentally maintained episodic anthrax in Esotha, NamibiaR01GM083863 · NIGMS · UNIVERSITY OF CALIFORNIA BERKELEY · PI GETZ, WAYNE M · 2008 to 2011
$1.7M
Framework to Address Drug Resistant Infections and Global HealthR24TW008822 · FIC · UNIVERSITY OF CALIFORNIA BERKELEY · PI RILEY, LEE W · 2010 to 2010
$425k
FIC NIH HHS R24 TW008822FIC NIH HHS R24TW008822NIGMS NIH HHS GM83863NIGMS NIH HHS R01 GM083863
6 · The paper itself

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.

Indexed as

Problem-Based LearningData Interpretation, StatisticalEpidemicsEpidemiologic FactorsEpidemiologyHumansModels, BiologicalModels, Statistical

Identifiers

PMID22509129
PMCPMC3317897
OpenAlexW2070796600

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.