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An infectious way to teach students about outbreaks
Author(s) -
Íde Cremin,
Oliver J. Watson,
Alastair Heffernan,
Natsuko Imai,
Norin Ahmed,
Sandra Bivegete,
Teresia Njoki Kimani,
Demetris Kyriacou,
Preveina Mahadevan,
Rima Mustafa,
Panagiota Pagoni,
Marisa K. Sophiea,
Charles Whittaker,
Leo Beacroft,
Steven Riley,
Matthew C. Fisher
Publication year - 2017
Publication title -
epidemics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.023
H-Index - 41
eISSN - 1755-4365
pISSN - 1878-0067
DOI - 10.1016/j.epidem.2017.12.002
Subject(s) - outbreak , infectious disease (medical specialty) , computer science , visualization , data science , disease , operations research , medicine , data mining , virology , mathematics , pathology
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.

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