IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1007703.html
   My bibliography  Save this article

The spread of a wild plant pathogen is driven by the road network

Author

Listed:
  • Elina Numminen
  • Anna-Liisa Laine

Abstract

Spatial analyses of pathogen occurrence in their natural surroundings entail unique opportunities for assessing in vivo drivers of disease epidemiology. Such studies are however confronted by the complexity of the landscape driving epidemic spread and disease persistence. Since relevant information on how the landscape influences epidemiological dynamics is rarely available, simple spatial models of spread are often used. In the current study we demonstrate both how more complex transmission pathways could be incorpoted to epidemiological analyses and how this can offer novel insights into understanding disease spread across the landscape. Our study is focused on Podosphaera plantaginis, a powdery mildew pathogen that transmits from one host plant to another by wind-dispersed spores. Its host populations often reside next to roads and thus we hypothesize that the road network influences the epidemiology of P. plantaginis. To analyse the impact of roads on the transmission dynamics, we consider a spatial dataset on the presence-absence records on the pathogen collected from a fragmented landscape of host populations. Using both mechanistic transmission modeling and statistical modeling with road-network summary statistics as predictors, we conclude the evident role of the road network in the progression of the epidemics: a phenomena which is manifested both in the enhanced transmission along the roads and in infections typically occurring at the central hub locations of the road network. We also demonstrate how the road network affects the spread of the pathogen using simulations. Jointly our results highlight how human alteration of natural landscapes may increase disease spread.Author summary: Studying pathogen transmission dynamics within their natural environments can yield important new insights both on the known and unknown determinants of the real-world transmission process. In this study we analyse how a fungal plant pathogen occurs within a landscape, showing that the road network dictates where the pathogen occurs, not only by providing suitable habitat for the host plant, but also by enhancing transmissions along the roads. Mechanistic understanding of how and where the transmission is expected to occur can yield novel insights into the ecology of pathogens, and is essential for design of control strategies.

Suggested Citation

  • Elina Numminen & Anna-Liisa Laine, 2020. "The spread of a wild plant pathogen is driven by the road network," PLOS Computational Biology, Public Library of Science, vol. 16(3), pages 1-21, March.
    • Handle: RePEc:plo:pcbi00:1007703
    DOI: 10.1371/journal.pcbi.1007703
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007703
    Download Restriction: no
    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1007703&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pcbi.1007703?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. Ilkka Hanski & Otso Ovaskainen, 2000. "The metapopulation capacity of a fragmented landscape," Nature, Nature, vol. 404(6779), pages 755-758, April.
    3. Hanna Susi & Benoit Barrès & Pedro F. Vale & Anna-Liisa Laine, 2015. "Co-infection alters population dynamics of infectious disease," Nature Communications, Nature, vol. 6(1), pages 1-8, May.
    4. Kuparinen, Anna & Markkanen, Tiina & Riikonen, Hermanni & Vesala, Timo, 2007. "Modeling air-mediated dispersal of spores, pollen and seeds in forested areas," Ecological Modelling, Elsevier, vol. 208(2), pages 177-188.
    5. J. O. Lloyd-Smith & S. J. Schreiber & P. E. Kopp & W. M. Getz, 2005. "Superspreading and the effect of individual variation on disease emergence," Nature, Nature, vol. 438(7066), pages 355-359, November.
    6. Vittoria Colizza & Alain Barrat & Marc Barthelemy & Alain-Jacques Valleron & Alessandro Vespignani, 2007. "Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions," PLOS Medicine, Public Library of Science, vol. 4(1), pages 1-16, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Timothy C Matisziw & Ashkan Gholamialam & Kathleen M Trauth, 2020. "Modeling habitat connectivity in support of multiobjective species movement: An application to amphibian habitat systems," PLOS Computational Biology, Public Library of Science, vol. 16(12), pages 1-23, December.
    2. Fuqiang Dai & Hao Liu & Xia Zhang & Qing Li, 2021. "Exploring the Emerging Trends of Spatial Epidemiology: A Scientometric Analysis Based on CiteSpace," SAGE Open, , vol. 11(4), pages 21582440211, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Joseph B. Bak-Coleman & Ian Kennedy & Morgan Wack & Andrew Beers & Joseph S. Schafer & Emma S. Spiro & Kate Starbird & Jevin D. West, 2022. "Combining interventions to reduce the spread of viral misinformation," Nature Human Behaviour, Nature, vol. 6(10), pages 1372-1380, October.
    2. Joe Meagher & Nial Friel, 2022. "Assessing epidemic curves for evidence of superspreading," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2179-2202, October.
    3. Frédéric M Hamelin & Linda J S Allen & Vrushali A Bokil & Louis J Gross & Frank M Hilker & Michael J Jeger & Carrie A Manore & Alison G Power & Megan A Rúa & Nik J Cunniffe, 2019. "Coinfections by noninteracting pathogens are not independent and require new tests of interaction," PLOS Biology, Public Library of Science, vol. 17(12), pages 1-25, December.
    4. S. M. Mniszewski & S. Y. Del Valle & P. D. Stroud & J. M. Riese & S. J. Sydoriak, 2008. "Pandemic simulation of antivirals + school closures: buying time until strain-specific vaccine is available," Computational and Mathematical Organization Theory, Springer, vol. 14(3), pages 209-221, September.
    5. Tardy, Olivia & Lenglos, Christophe & Lai, Sandra & Berteaux, Dominique & Leighton, Patrick A., 2023. "Rabies transmission in the Arctic: An agent-based model reveals the effects of broad-scale movement strategies on contact risk between Arctic foxes," Ecological Modelling, Elsevier, vol. 476(C).
    6. Francis,David C. & Kubinec ,Robert, 2022. "Beyond Political Connections : A Measurement Model Approach to Estimating Firm-levelPolitical Influence in 41 Economies," Policy Research Working Paper Series 10119, The World Bank.
    7. Martinovici, A., 2019. "Revealing attention - how eye movements predict brand choice and moment of choice," Other publications TiSEM 7dca38a5-9f78-4aee-bd81-c, Tilburg University, School of Economics and Management.
    8. Wei Zhong, 2017. "Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 475-495, December.
    9. Yongping Bao & Ludwig Danwitz & Fabian Dvorak & Sebastian Fehrler & Lars Hornuf & Hsuan Yu Lin & Bettina von Helversen, 2022. "Similarity and Consistency in Algorithm-Guided Exploration," CESifo Working Paper Series 10188, CESifo.
    10. Floriana Gargiulo & Sônia Ternes & Sylvie Huet & Guillaume Deffuant, 2010. "An Iterative Approach for Generating Statistically Realistic Populations of Households," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-9, January.
    11. Heinrich, Torsten & Yang, Jangho & Dai, Shuanping, 2020. "Growth, development, and structural change at the firm-level: The example of the PR China," MPRA Paper 105011, University Library of Munich, Germany.
    12. van Kesteren Erik-Jan & Bergkamp Tom, 2023. "Bayesian analysis of Formula One race results: disentangling driver skill and constructor advantage," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 19(4), pages 273-293, December.
    13. Teruhiko Yoneyama & Sanmay Das & Mukkai Krishnamoorthy, 2012. "A Hybrid Model for Disease Spread and an Application to the SARS Pandemic," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(1), pages 1-5.
    14. Xin Xu & Yang Lu & Yupeng Zhou & Zhiguo Fu & Yanjie Fu & Minghao Yin, 2021. "An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks," Mathematics, MDPI, vol. 9(15), pages 1-14, July.
    15. Xiaoyue Xi & Simon E. F. Spencer & Matthew Hall & M. Kate Grabowski & Joseph Kagaayi & Oliver Ratmann & Rakai Health Sciences Program and PANGEA‐HIV, 2022. "Inferring the sources of HIV infection in Africa from deep‐sequence data with semi‐parametric Bayesian Poisson flow models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 517-540, June.
    16. Moshe B Hoshen & Anthony H Burton & Themis J V Bowcock, 2007. "Simulating disease transmission dynamics at a multi-scale level," International Journal of Microsimulation, International Microsimulation Association, vol. 1(1), pages 26-34.
    17. Kuschnig, Nikolas, 2021. "Bayesian Spatial Econometrics and the Need for Software," Department of Economics Working Paper Series 318, WU Vienna University of Economics and Business.
    18. Deniz Aksoy & David Carlson, 2022. "Electoral support and militants’ targeting strategies," Journal of Peace Research, Peace Research Institute Oslo, vol. 59(2), pages 229-241, March.
    19. Luc E. Coffeng & Sake J. de Vlas, 2022. "Predicting epidemics and the impact of interventions in heterogeneous settings: Standard SEIR models are too pessimistic," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 28-35, November.
    20. Richard Hunt & Shelton Peiris & Neville Weber, 2022. "Estimation methods for stationary Gegenbauer processes," Statistical Papers, Springer, vol. 63(6), pages 1707-1741, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1007703. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.