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- W1830755055 endingPage "955" @default.
- W1830755055 startingPage "947" @default.
- W1830755055 abstract "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." @default.
- W1830755055 created "2016-06-24" @default.
- W1830755055 creator A5003778401 @default.
- W1830755055 creator A5010943911 @default.
- W1830755055 creator A5012657096 @default.
- W1830755055 creator A5025831650 @default.
- W1830755055 creator A5031918844 @default.
- W1830755055 date "2015-07-01" @default.
- W1830755055 modified "2023-09-29" @default.
- W1830755055 title "History, Epidemic Evolution, and Model Burn-In for a Network of Annual Invasion: Soybean Rust" @default.
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