Modeling epidemics in seed systems and landscapes to guide management strategies: The case of sweetpotato in Northern Uganda - Andersen, K.F.; Buddenhagen, C.; Rachkara, P.; Gibson, R.; Kalule, S.; Phillips, D.; Garrett, K.A. (Scientific Societies, 2019)
Seed systems are critical for deployment of improved varieties, but also can serve as major conduits for the spread of seed-borne pathogens. As in many other epidemic systems, epidemic risk in seed systems often depends on the structure of networks of trade, social interactions, and landscape connectivity. In a case study, we evaluated the structure of an informal sweetpotato seed system in the Gulu Region of Northern Uganda, for its vulnerability to the spread of emerging epidemics, and its utility for disseminating improved varieties. Seed transaction data were collected by surveying vine sellers weekly during the 2014 growing season. We combined data from these observed seed transactions with estimated dispersal risk based on village-to-village proximity to create a multilayer network, or ‘supra-network’. Both the inverse power law function and negative exponential function, common models for dispersal kernels, were evaluated in a sensitivity analysis/uncertainty quantification across a range of parameters chosen to represent spread based on proximity in the landscape. In a set of simulation experiments, we modeled the introduction of a novel pathogen, and evaluated the influence of spread parameters on the selection of villages for surveillance and for management. We found that the starting position in the network was critical for epidemic progress and final epidemic outcomes, largely driven by node out-degree. The efficacy of node centrality measures was evaluated for utility in identifying villages in the network to manage and limit disease spread. Node degree often performed as well as other more complicated centrality measures for the networks where village-to-village spread was modeled by the inverse power law, while betweenness centrality was often more effective for negative exponential dispersal. This analysis framework can be applied to provide recommendations for a wide variety of seed systems.