Nik J. Cunniffe

TL;DR: S spatio-temporal, stochastic epidemiological modeling in combination with realistic geographical modeling is used to predict the spread of the sudden oak death pathogen through heterogeneous host populations in wildland forests, subject to fluctuating weather conditions.

TL;DR: In this article, the authors describe an integrated research agenda that could help mitigate future plant disease pandemics, including disease surveillance and improved detection technologies including pathogen sensors and predictive modeling and data analytics.

TL;DR: A number of aspects of plant epidemiology are very distinctive, and this leads to specific challenges in modelling plant diseases, which in turn sets a certain agenda for modellers.

TL;DR: In this paper, a multilevel modeling framework for simulating the emergence of landscape spatial structure in urbanizing regions using a combination of field-based and object-based representations of land change is presented.

TL;DR: A roadmap to facilitate the development of reliable models to guide exit strategies is proposed, and has three parts: improve estimation of key epidemiological parameters; understand sources of heterogeneity in populations; and focus on requirements for data collection, particularly in low-to-middle-income countries.