Sampling for disease absence—deriving informed monitoring from epidemic traits
Monitoring for disease requires subsets of the host population to be sampled and tested for the pathogen. If all the samples return healthy, what are the chances the disease was present but missed? In this paper, we developed a statistical approach to solve this problem considering the fundamental p...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Published: |
Academic Press
2019
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| Online Access: | http://hdl.handle.net/20.500.11937/73209 |
| _version_ | 1848762954142449664 |
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| author | Bourhis, Y. Gottwald, T. Lopez-Ruiz, Fran Patarapuwadol, S. van den Bosch, F. |
| author_facet | Bourhis, Y. Gottwald, T. Lopez-Ruiz, Fran Patarapuwadol, S. van den Bosch, F. |
| author_sort | Bourhis, Y. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Monitoring for disease requires subsets of the host population to be sampled and tested for the pathogen. If all the samples return healthy, what are the chances the disease was present but missed? In this paper, we developed a statistical approach to solve this problem considering the fundamental property of infectious diseases: their growing incidence in the host population. The model gives an estimate of the incidence probability density as a function of the sampling effort, and can be reversed to derive adequate monitoring patterns ensuring a given maximum incidence in the population. We then present an approximation of this model, providing a simple rule of thumb for practitioners. The approximation is shown to be accurate for a sample size larger than 20, and we demonstrate its use by applying it to three plant pathogens: citrus canker, bacterial blight and grey mould. |
| first_indexed | 2025-11-14T10:55:46Z |
| format | Journal Article |
| id | curtin-20.500.11937-73209 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:55:46Z |
| publishDate | 2019 |
| publisher | Academic Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-732092019-02-14T05:13:27Z Sampling for disease absence—deriving informed monitoring from epidemic traits Bourhis, Y. Gottwald, T. Lopez-Ruiz, Fran Patarapuwadol, S. van den Bosch, F. Monitoring for disease requires subsets of the host population to be sampled and tested for the pathogen. If all the samples return healthy, what are the chances the disease was present but missed? In this paper, we developed a statistical approach to solve this problem considering the fundamental property of infectious diseases: their growing incidence in the host population. The model gives an estimate of the incidence probability density as a function of the sampling effort, and can be reversed to derive adequate monitoring patterns ensuring a given maximum incidence in the population. We then present an approximation of this model, providing a simple rule of thumb for practitioners. The approximation is shown to be accurate for a sample size larger than 20, and we demonstrate its use by applying it to three plant pathogens: citrus canker, bacterial blight and grey mould. 2019 Journal Article http://hdl.handle.net/20.500.11937/73209 10.1016/j.jtbi.2018.10.038 Academic Press restricted |
| spellingShingle | Bourhis, Y. Gottwald, T. Lopez-Ruiz, Fran Patarapuwadol, S. van den Bosch, F. Sampling for disease absence—deriving informed monitoring from epidemic traits |
| title | Sampling for disease absence—deriving informed monitoring from epidemic traits |
| title_full | Sampling for disease absence—deriving informed monitoring from epidemic traits |
| title_fullStr | Sampling for disease absence—deriving informed monitoring from epidemic traits |
| title_full_unstemmed | Sampling for disease absence—deriving informed monitoring from epidemic traits |
| title_short | Sampling for disease absence—deriving informed monitoring from epidemic traits |
| title_sort | sampling for disease absence—deriving informed monitoring from epidemic traits |
| url | http://hdl.handle.net/20.500.11937/73209 |