History, Epidemic Evolution, and Model Burn-In for a Network of Annual Invasion: Soybean Rust
Ecological history may be an important driver of epidemics and disease emergence. We evaluated the role of history and two related concepts, the evolution of epidemics and the burn-in period required for fitting a model to epidemic observations, for the U.S. soybean rust epidemic (caused by Phakopso...
| Autores principales: | , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Scientific Societies
2015
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/69031 |
| _version_ | 1855515584565870592 |
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| author | Sanatkar M Scoglio, Caterina Natarajan S Isard, S. Garrett, K.A. |
| author_browse | Garrett, K.A. Isard, S. Natarajan S Sanatkar M Scoglio, Caterina |
| author_facet | Sanatkar M Scoglio, Caterina Natarajan S Isard, S. Garrett, K.A. |
| author_sort | Sanatkar M |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Ecological history may be an important driver of epidemics and disease emergence. We evaluated the role of history and two related concepts, the evolution of epidemics and the burn-in period required for fitting a model to epidemic observations, for the U.S. soybean rust epidemic (caused by Phakopsora pachyrhizi). This disease allows evaluation of replicate epidemics because the pathogen reinvades the United States each year. We used a new maximum likelihood estimation approach for fitting the network model based on observed U.S. epidemics. We evaluated the model burn-in period by comparing model fit based on each combination of other years of observation. When the miss error rates were weighted by 0.9 and false alarm error rates by 0.1, the mean error rate did decline, for most years, as more years were used to construct models. Models based on observations in years closer in time to the season being estimated gave lower miss error rates for later epidemic years. The weighted mean error rate was lower in backcasting than in forecasting, reflecting how the epidemic had evolved. Ongoing epidemic evolution, and potential model failure, can occur because of changes in climate, host resistance and spatial patterns, or pathogen evolution. |
| format | Journal Article |
| id | CGSpace69031 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2015 |
| publishDateRange | 2015 |
| publishDateSort | 2015 |
| publisher | Scientific Societies |
| publisherStr | Scientific Societies |
| record_format | dspace |
| spelling | CGSpace690312024-04-25T06:00:21Z History, Epidemic Evolution, and Model Burn-In for a Network of Annual Invasion: Soybean Rust Sanatkar M Scoglio, Caterina Natarajan S Isard, S. Garrett, K.A. climate change agriculture Ecological history may be an important driver of epidemics and disease emergence. We evaluated the role of history and two related concepts, the evolution of epidemics and the burn-in period required for fitting a model to epidemic observations, for the U.S. soybean rust epidemic (caused by Phakopsora pachyrhizi). This disease allows evaluation of replicate epidemics because the pathogen reinvades the United States each year. We used a new maximum likelihood estimation approach for fitting the network model based on observed U.S. epidemics. We evaluated the model burn-in period by comparing model fit based on each combination of other years of observation. When the miss error rates were weighted by 0.9 and false alarm error rates by 0.1, the mean error rate did decline, for most years, as more years were used to construct models. Models based on observations in years closer in time to the season being estimated gave lower miss error rates for later epidemic years. The weighted mean error rate was lower in backcasting than in forecasting, reflecting how the epidemic had evolved. Ongoing epidemic evolution, and potential model failure, can occur because of changes in climate, host resistance and spatial patterns, or pathogen evolution. 2015-07 2015-11-27T09:15:40Z 2015-11-27T09:15:40Z Journal Article https://hdl.handle.net/10568/69031 en Open Access Scientific Societies Sanatkar M, Scoglio C, Natarajan B, Isard S, Garrett K. 2015. History, Epidemic Evolution, and Model Burn-In for a Network of Annual Invasion: Soybean Rust. Phytopathology 105(7): 947-955. |
| spellingShingle | climate change agriculture Sanatkar M Scoglio, Caterina Natarajan S Isard, S. Garrett, K.A. History, Epidemic Evolution, and Model Burn-In for a Network of Annual Invasion: Soybean Rust |
| title | History, Epidemic Evolution, and Model Burn-In for a Network of Annual Invasion: Soybean Rust |
| title_full | History, Epidemic Evolution, and Model Burn-In for a Network of Annual Invasion: Soybean Rust |
| title_fullStr | History, Epidemic Evolution, and Model Burn-In for a Network of Annual Invasion: Soybean Rust |
| title_full_unstemmed | History, Epidemic Evolution, and Model Burn-In for a Network of Annual Invasion: Soybean Rust |
| title_short | History, Epidemic Evolution, and Model Burn-In for a Network of Annual Invasion: Soybean Rust |
| title_sort | history epidemic evolution and model burn in for a network of annual invasion soybean rust |
| topic | climate change agriculture |
| url | https://hdl.handle.net/10568/69031 |
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