Using sensitivity analysis to identify key factors for the propagation of a plant epidemic
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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The Royal Society
2018-01-01
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| Series: | Royal Society Open Science |
| Subjects: | |
| Online Access: | https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.171435 |
| _version_ | 1818400972871827456 |
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| author | Loup Rimbaud Claude Bruchou Sylvie Dallot David R. J. Pleydell Emmanuel Jacquot Samuel Soubeyrand Gaël Thébaud |
| author_facet | Loup Rimbaud Claude Bruchou Sylvie Dallot David R. J. Pleydell Emmanuel Jacquot Samuel Soubeyrand Gaël Thébaud |
| author_sort | Loup Rimbaud |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-14T07:45:04Z |
| format | Article |
| id | doaj.art-d9cb3107e3d64acba63a559638fb4a26 |
| institution | Directory Open Access Journal |
| issn | 2054-5703 |
| language | English |
| last_indexed | 2024-12-14T07:45:04Z |
| publishDate | 2018-01-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj.art-d9cb3107e3d64acba63a559638fb4a262022-12-21T23:10:54ZengThe Royal SocietyRoyal Society Open Science2054-57032018-01-015110.1098/rsos.171435171435Using sensitivity analysis to identify key factors for the propagation of a plant epidemicLoup RimbaudClaude BruchouSylvie DallotDavid R. J. PleydellEmmanuel JacquotSamuel SoubeyrandGaël ThébaudIdentifying 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.https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.171435heterogeneous landscapepolynomial regressionsensitivity indexsobol's methodsimulation modelspatially explicit model |
| spellingShingle | Loup Rimbaud Claude Bruchou Sylvie Dallot David R. J. Pleydell Emmanuel Jacquot Samuel Soubeyrand Gaël Thébaud Using sensitivity analysis to identify key factors for the propagation of a plant epidemic Royal Society Open Science heterogeneous landscape polynomial regression sensitivity index sobol's method simulation model spatially explicit model |
| title | Using sensitivity analysis to identify key factors for the propagation of a plant epidemic |
| title_full | Using sensitivity analysis to identify key factors for the propagation of a plant epidemic |
| title_fullStr | Using sensitivity analysis to identify key factors for the propagation of a plant epidemic |
| title_full_unstemmed | Using sensitivity analysis to identify key factors for the propagation of a plant epidemic |
| title_short | Using sensitivity analysis to identify key factors for the propagation of a plant epidemic |
| title_sort | using sensitivity analysis to identify key factors for the propagation of a plant epidemic |
| topic | heterogeneous landscape polynomial regression sensitivity index sobol's method simulation model spatially explicit model |
| url | https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.171435 |
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