Data from: Using sensitivity analysis to identify key factors for the propagation of a plant epidemic
| Main Authors: | Rimbaud, Loup, Bruchou, Claude, Dallot, Sylvie, Pleydell, David R.J., Jacquot, Emmanuel, Soubeyrand, Samuel, Thébaud, Gaël, Pleydell, David R. J. |
|---|---|
| Format: | info dataset Journal |
| Terbitan: |
, 2017
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/4998178 |
| ctrlnum |
4998178 |
|---|---|
| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><creator>Rimbaud, Loup</creator><creator>Bruchou, Claude</creator><creator>Dallot, Sylvie</creator><creator>Pleydell, David R.J.</creator><creator>Jacquot, Emmanuel</creator><creator>Soubeyrand, Samuel</creator><creator>Thébaud, Gaël</creator><creator>Pleydell, David R. J.</creator><date>2017-12-04</date><description>Identifying the key factors underlying the spread of a disease is an essential but challenging prerequisite to design management strategies. To tackle this issue, we propose an approach based on sensitivity analyses of a spatiotemporal stochastic model simulating the spread of a plant epidemic. This work is motivated by the spread of sharka, caused by Plum pox virus, in a real landscape. We first carried out a broad-range sensitivity analysis, ignoring any prior information on six epidemiological parameters, to assess their intrinsic influence on model behaviour. A second analysis benefited from the available knowledge on sharka epidemiology and was thus restricted to more realistic values. The broad-range analysis revealed that the mean duration of the latent period is the most influential parameter of the model, whereas the sharka-specific analysis uncovered the strong impact of the connectivity of the first infected orchard. In addition to demonstrating the interest of sensitivity analyses for a stochastic model, this study highlights the impact of variation ranges of target parameters on the outcome of a sensitivity analysis. With regard to sharka management, our results suggest that sharka surveillance may benefit from paying closer attention to highly-connected patches whose infection could trigger serious epidemics.</description><description>Data & scripts_v2ZIP folder containing all scripts, data and simulation results used in this study.</description><identifier>https://zenodo.org/record/4998178</identifier><identifier>10.5061/dryad.b8kb0</identifier><identifier>oai:zenodo.org:4998178</identifier><relation>doi:10.1098/rsos.171435</relation><relation>url:https://zenodo.org/communities/dryad</relation><rights>info:eu-repo/semantics/openAccess</rights><rights>https://creativecommons.org/publicdomain/zero/1.0/legalcode</rights><subject>spatially-explicit model</subject><subject>Sobol's method</subject><subject>simulation model</subject><subject>heterogeneous landscape</subject><subject>sensitivity index</subject><subject>polynomial regression</subject><title>Data from: Using sensitivity analysis to identify key factors for the propagation of a plant epidemic</title><type>Other:info:eu-repo/semantics/other</type><type>Other:dataset</type><recordID>4998178</recordID></dc>
|
| format |
Other:info:eu-repo/semantics/other Other Other:dataset Journal:Journal Journal |
| author |
Rimbaud, Loup Bruchou, Claude Dallot, Sylvie Pleydell, David R.J. Jacquot, Emmanuel Soubeyrand, Samuel Thébaud, Gaël Pleydell, David R. J. |
| title |
Data from: Using sensitivity analysis to identify key factors for the propagation of a plant epidemic |
| publishDate |
2017 |
| topic |
spatially-explicit model Sobol's method simulation model heterogeneous landscape sensitivity index polynomial regression |
| url |
https://zenodo.org/record/4998178 |
| contents |
Identifying the key factors underlying the spread of a disease is an essential but challenging prerequisite to design management strategies. To tackle this issue, we propose an approach based on sensitivity analyses of a spatiotemporal stochastic model simulating the spread of a plant epidemic. This work is motivated by the spread of sharka, caused by Plum pox virus, in a real landscape. We first carried out a broad-range sensitivity analysis, ignoring any prior information on six epidemiological parameters, to assess their intrinsic influence on model behaviour. A second analysis benefited from the available knowledge on sharka epidemiology and was thus restricted to more realistic values. The broad-range analysis revealed that the mean duration of the latent period is the most influential parameter of the model, whereas the sharka-specific analysis uncovered the strong impact of the connectivity of the first infected orchard. In addition to demonstrating the interest of sensitivity analyses for a stochastic model, this study highlights the impact of variation ranges of target parameters on the outcome of a sensitivity analysis. With regard to sharka management, our results suggest that sharka surveillance may benefit from paying closer attention to highly-connected patches whose infection could trigger serious epidemics. Data & scripts_v2ZIP folder containing all scripts, data and simulation results used in this study. |
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IOS16997.4998178 |
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ZAIN Publications |
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7213 |
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library:special library |
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Cognizance Journal of Multidisciplinary Studies |
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5267 |
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Cognizance Journal of Multidisciplinary Studies |
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16997 |
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Multidisciplinary |
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Stockholm |
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INTERNASIONAL |
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1 |
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IOS16997 |
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2022-06-06T05:23:51Z |
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