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|Title: ||Prediction of invasion from the early stage of an epidemic|
|Authors: ||Pérez-Reche, Francisco J.|
Neri, Franco M.
Taraskin, Sergei N.
Gilligan, Christopher A.
|Affiliation: ||University of Abertay Dundee. Scottish Informatics, Mathematics, Biology and Statistics Centre|
|Issue Date: ||Sep-2012|
|Publisher: ||The Royal Society|
|Type: ||Journal Article|
|Rights: ||This is the author final version of this article plus supplementary methods and results data supplement. Published versions (c)The Royal Society, available from http://dx.doi.org/10.1098/rsif.2012.0130|
|Citation: ||Pérez-Reche, F.J., et al. 2012. Prediction of invasion from the early stage of an epidemic. Journal of the Royal Society Interface. 9(74): pp.2085-2096. Available from http://dx.doi.org/10.1098/rsif.2012.0130|
|Abstract: ||Predictability of undesired events is a question of great interest in many scientific disciplines including seismology, economy and epidemiology. Here, we focus on the predictability of invasion of a broad class of epidemics caused by diseases that lead to permanent immunity of infected hosts after recovery or death. We approach the problem from the perspective of the science of complexity by proposing and testing several strategies for the estimation of important characteristics of epidemics, such as the probability of invasion. Our results suggest that parsimonious approximate methodologies may lead to the most reliable and robust predictions. The proposed methodologies are first applied to analysis of experimentally observed epidemics: invasion of the fungal plant pathogen Rhizoctonia solani in replicated host microcosms. We then consider numerical experiments of the susceptible–infected–removed model to investigate the performance of the proposed methods in further detail. The suggested framework can be used as a valuable tool for quick assessment of epidemic threat at the stage when epidemics only start developing. Moreover, our work amplifies the significance of the small-scale and finite-time microcosm realizations of epidemics revealing their predictive power.|
|Appears in Collections:||SIMBIOS Collection|
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