Modeling Epidemics in Seed Systems and Landscapes To Guide Management Strategies: The Case of Sweet Potato in Northern Uganda

Abstract

Seed systems are critical for deployment of improved varieties but also can serve as major conduits for the spread of seedborne 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 sweet potato 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 “supranetwork.” 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 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, whereas 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. [Formula: see text] Copyright © 2019 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

User guide to the toolbox for working with root, tuber and banana seed systems. RTB User guide

“…Results from this intervention include the adoption by hundreds of farmers of positive selection, a simple technique for selecting the best seed tubers; the identification of three potato clones with resistance to viruses to be released as varieties; and the adaptation of international standards for seed certification to Georgian conditions. Similarly, using impact network analysis (tool 2, Table 1) the study team modelled scenarios for spread for S. endobioticum, as part of a risk assessment analysis, and identified areas where the pathogen needs to be monitored carefully to prevent major losses (Andersen et al 2020), building on concepts developed in a study of sweetpotato seed systems in Uganda (Andersen et al 2019).…”

User guide to impact network analysis (INA). RTB User Guide

“…How well do different methods for selecting quarantine locations influence system outcomes? (Andersen et al 2019) How do different patterns of cropland connectivity (patterns of cropland proximity that make spread of pathogens more or less likely) influence requirements for regional management? (Margosian et al 2009;Xing et al 2020) How do choices among management types, with adoption influenced by observations of one's own and others' success rates, influence system outcomes?…”

“…More examples of output from INAscene are available in vignettes in Garrett 2020a and 2020b, and in Garrett 2020e.SMARTSURVThe output from smartsurv can be used to plot the relative importance of nodes for surveillance. Examples of the application of this analysis for seed systems are available inBuddenhagen et al (2017; Figure 3)andAndersen et al (2019). More examples of output from smartsurv are available in a vignette in Garrett 2020c and in Garrett 2020e.…”

Detection, forecasting and control of infectious disease epidemics: modelling outbreaks in humans, animals and plants

“…Despite the overall focus on public health and modelling pathogens of humans (and animals, to a lesser extent), there are a number of research groups working in plant disease epidemic modelling. As described below, these groups have tended to publish their research in journals specifically focused on plant systems such as Phytopathology [37][38][39], Plant Pathology [40 -42] and New Phytologist [43,44], rather than more general (and typically higher-impact) journals, with a few high profile exceptions (e.g. [45,46]).…”