Evidence map›Paper›PMID 29289499›Full record

ArticleEpidemics2018

An infectious way to teach students about outbreaks.

Íde Cremin, Oliver Watson, Alastair Heffernan, Natsuko Imai, Norin Ahmed, Sandra Bivegete, Teresia Kimani, Demetris Kyriacou, Preveina Mahadevan, Rima Mustafa and 6 more

Abstract read
In one paragraph

Article in Epidemics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Review
  2. Article
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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

16 authors.

Íde CreminDepartment of Infectious Disease Epidemiology, Imperial College London, UK.
Oliver WatsonDepartment of Infectious Disease Epidemiology, Imperial College London, UK. Electronic address: o.watson15@imperial.ac.uk.
Alastair HeffernanDepartment of Infectious Disease Epidemiology, Imperial College London, UK.
Natsuko ImaiDepartment of Infectious Disease Epidemiology, Imperial College London, UK.
Norin AhmedDepartment of Infectious Disease Epidemiology, Imperial College London, UK.
Sandra BivegeteDepartment of Infectious Disease Epidemiology, Imperial College London, UK.
Teresia KimaniDepartment of Infectious Disease Epidemiology, Imperial College London, UK.
Demetris KyriacouDepartment of Infectious Disease Epidemiology, Imperial College London, UK.
Preveina MahadevanDepartment of Infectious Disease Epidemiology, Imperial College London, UK.
Rima MustafaDepartment of Infectious Disease Epidemiology, Imperial College London, UK.
Panagiota PagoniDepartment of Infectious Disease Epidemiology, Imperial College London, UK.
Marisa SophieaDepartment of Infectious Disease Epidemiology, Imperial College London, UK.
Charlie WhittakerDepartment of Infectious Disease Epidemiology, Imperial College London, UK.
Leo BeacroftDepartment of Infectious Disease Epidemiology, Imperial College London, UK.
Steven RileyDepartment of Infectious Disease Epidemiology, Imperial College London, UK.
Matthew C FisherDepartment of Infectious Disease Epidemiology, Imperial College London, UK.

Funding

Models for synthesising molecular, clinical and epidemiological data, and translaU01GM110721 · NIGMS · IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE · PI CAUCHEMEZ, SIMON, FERGUSON, NEIL MORRIS · 2014 to 2018
$1.9M
Medical Research Council MR/J008761/1Medical Research Council MR/K000373/1Medical Research Council MR/K010174/1BMedical Research Council MR/R015600/1NIGMS NIH HHS U01 GM110721Wellcome TrustWellcome Trust 105268/Z/14/ZWellcome Trust 109312/Z/15/Z
6 · The paper itself

Abstract

The study of infectious disease outbreaks is required to train today's epidemiologists. A typical way to introduce and explain key epidemiological concepts is through the analysis of a historical outbreak. There are, however, few training options that explicitly utilise real-time simulated stochastic outbreaks where the participants themselves comprise the dataset they subsequently analyse. In this paper, we present a teaching exercise in which an infectious disease outbreak is simulated over a five-day period and subsequently analysed. We iteratively developed the teaching exercise to offer additional insight into analysing an outbreak. An R package for visualisation, analysis and simulation of the outbreak data was developed to accompany the practical to reinforce learning outcomes. Computer simulations of the outbreak revealed deviations from observed dynamics, highlighting how simplifying assumptions conventionally made in mathematical models often differ from reality. Here we provide a pedagogical tool for others to use and adapt in their own settings.

Indexed as

Computer SimulationModels, TheoreticalDisease OutbreaksEpidemiologyHumansStudentsNetwork reconstructionOutbreak analysisPedagogical toolSimulation analysisTeaching

Identifiers

PMID29289499
PMCPMC5971212

What OpenQuestion holds

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Registered trials

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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.