Impact of scale on the effectiveness of disease control strategies for epidemics with cryptic infection in a dynamical landscape: an example for a crop disease

Abstract

We use a spatially explicit, stochastic model to analyse the effectiveness of different scales of local control strategies in containing the long-term, multi-seasonal spread of a crop disease through a dynamically changing population of susceptible crops in which there is cryptic infection. The model distinguishes between susceptible, infested and symptomatic fields. It is motivated by rhizomania disease on sugar beet in the UK as an exemplar of a spatially structured and partially asymptomatic epidemic. Our results show the importance of matching the scales of local control strategies to prevent intensification and regional spread of disease with the inherent temporal and spatial scales of an epidemic. A simple field-scale containment strategy, whereby the susceptible crop is no longer grown on fields showing symptoms, fails for this system with cryptic infection because the locally applied control lags behind the epidemic. A farm-scale strategy, whereby growers respond to the disease status of neighbouring farms by transferring their quota for sugar beet to farmers in regions of reduced risk, succeeds. We conclude that a soil-borne pathogen such as rhizomania could be managed by movement of susceptible crops in the landscape using a strategy that matches the temporal and spatial scales of the epidemic and which take account of risk aversion among growers. We show some parallels and differences in effectiveness between a 'culling' strategy involving crop removal around emerging foci and the local deployment of partially resistant varieties that reduce amplification and transmission of inoculum. Some relationships between the control of plant and livestock diseases are briefly discussed.

Trade-off between disease resistance and crop yield: a landscape-scale mathematical modelling perspective

“…Combining these two effects could produce complex dynamics [19,24]. Finally, it would be interesting to include the effects of heterogeneity in growing behaviour or a more formal treatment of risk-aversion [34,35]. In this paper, we model the yield in the presence of uncertainty as the average of the yield when the outbreak epidemic does or does not occur.…”

Modeling when, where, and how to manage a forest epidemic, motivated by sudden oak death in California

“…One important principle is matching the scale of treatment with the inherent spatial and temporal scales of pathogen spread to achieve effective management (6,15,(25)(26)(27)32). Characterization of epidemic scales is complicated, as the epidemic advances through heterogeneous host populations subject to variability in environmental drivers.…”

“…Successful control of epidemics involves matching the scale of management with the inherent scale of spread (6). Early detection and timely removal of affected trees from a small number of newly infected sites soon after initial introduction(s) can prevent epidemics (7).…”

Optimal and robust control of invasive alien species spreading in homogeneous landscapes

“…This could be achieved by adopting a spatially explicit simulation approach (Gilligan et al 2007). In this case, more flexible spread models such as metapopulation models (Rowthorn et al in press), cellular automata or individual-based models could be considered.…”