9
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Related collections

          Most cited references85

          • Record: found
          • Abstract: found
          • Article: not found

          Epidemic Spreading in Scale-Free Networks

          The Internet has a very complex connectivity recently modeled by the class of scale-free networks. This feature, which appears to be very efficient for a communications network, favors at the same time the spreading of computer viruses. We analyze real data from computer virus infections and find the average lifetime and persistence of viral strains on the Internet. We define a dynamical model for the spreading of infections on scale-free networks, finding the absence of an epidemic threshold and its associated critical behavior. This new epidemiological framework rationalizes data of computer viruses and could help in the understanding of other spreading phenomena on communication and social networks.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Using Deep Learning for Image-Based Plant Disease Detection

            Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Superspreading and the effect of individual variation on disease emergence

              Coughs and sneezes... From Typhoid Mary to SARS, it has long been known that some people spread disease more than others. But for diseases transmitted via casual contact, contagiousness arises from a plethora of social and physiological factors, so epidemiologists have tended to rely on population averages to assess a disease's potential to spread. A new analysis of outbreak data shows that individual differences in infectiousness exert powerful influences on the epidemiology of ten deadly diseases. SARS and measles (and perhaps avian influenza) show strong tendencies towards ‘superspreading events’ that can ignite explosive epidemics — but this same volatility makes outbreaks more likely to fizzle out. Smallpox and pneumonic plague, two potential bioterrorism agents, show steadier growth but still differ markedly from the traditional average-based view. These findings are relevant to how emerging diseases are detected and controlled. Supplementary information The online version of this article (doi:10.1038/nature04153) contains supplementary material, which is available to authorized users.
                Bookmark

                Author and article information

                Journal
                Phytopathology
                Phytopathology
                PHYTO
                Phytopathology
                The American Phytopathological Society (APS)
                0031-949X
                1943-7684
                13 August 2019
                2019
                : 109
                : 9
                : 1519-1532
                Affiliations
                [1 ]Plant Pathology Department, University of Florida, Gainesville, FL 32611-0680, U.S.A
                [2 ]Institute for Sustainable Food Systems, University of Florida, Gainesville, FL 32611-0680, U.S.A
                [3 ]Emerging Pathogens Institute, University of Florida, Gainesville, FL 32611-0680, U.S.A
                [4 ]Department of Rural Development and Agribusiness, Gulu University, Gulu, Uganda
                [5 ]Natural Resource Institute, University of Greenwich, Greenwich, United
                Author notes
                []Corresponding authors: K. F. Andersen; andersenk@ 123456ufl.edu , and K. A. Garrett; karengarrett@ 123456ufl.edu
                Article
                PHYTO-109-09-1519
                10.1094/PHYTO-03-18-0072-R
                7779973
                30785374
                54f7c08e-08cb-43c8-9816-5643e4df705b
                © 2019 The Author(s)

                This is an open access article distributed under the CC BY 4.0 International license.

                History
                : 14 February 2019
                Categories
                Analytical and Theoretical Plant Pathology

                disease control and pest management,ecology and epidemiology,postharvest pathology and mycotoxins,techniques,virology

                Comments

                Comment on this article