Prediction of invasion from the early stage of an epidemic
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...
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Published in | Journal of the Royal Society interface Vol. 9; no. 74; pp. 2085 - 2096 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
England
The Royal Society
07.09.2012
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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Bibliography: | istex:D730D5EA5D4421BA9F25A109518F4DF5B681BE8E ark:/67375/V84-19XKG85M-N ArticleID:rsif20120130 href:rsif20120130.pdf ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1742-5689 1742-5662 |
DOI: | 10.1098/rsif.2012.0130 |