.png){kind=icon}
.png){kind=icon}
Return
Developing Predictive Models and Early Warning Systems for Invading Pathogens: Wheat Rusts
.png){kind=icon}
PRE
AI
C
DOI：10.1146/annurev-phyto-121423-041956.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Innovations in aerobiological and epidemiological modeling are enablingthe development of powerful techniques to infer connectivity networks fortransboundary pathogens in ways that were not previously possible. The in-novations are supported by improved access to historical and near real-timehighly resolved weather data, multi-country disease surveillance data, andenhanced computing power. Using wheat rusts as an exemplar, we intro-duce a flexible modeling framework to identify characteristic pathways forlong-distance spore dispersal within countries and beyond national borders.We show how the models are used for near real-time early warning systemsto support smallholder farmers in East Africa and South Asia. Wheat rustpathogens are ideal exemplars because they continue to pose threats to foodsecurity, especially in regions of the world where resources for control arelimited. The risks are exacerbated by the rapid appearance and spread of newpathogenic strains, prodigious spore production, and long-distance dispersalfor transboundary and pandemic spread.
Keywords:
epidemiological models
meteorological models
Lagrangian particle dispersion models
mechanistic dispersal models
aerobiology
transboundary pathogens
Journal
IF:
11.9
Papers:
1.1K
Citations:
9.3K
Organization
Cited Papers