Evidence map›Paper›PMID 41042792›Full record

ArticlePLoS computational biology2025

Modelling plant disease spread and containment: Simulation and approximate Bayesian Computation for Xylella fastidiosa in Puglia, Italy.

Daniel Chapman, Flavia Occhibove, James M Bullock, Pieter S A Beck, Juan A Navas-Cortes, Steven M White

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Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

6 authors.

Daniel ChapmanBiological and Environmental Sciences, University of Stirling, Stirling, United Kingdom.ORCID 0000-0003-1836-4112
Flavia OcchiboveUK Centre for Ecology & Hydrology, Wallingford, United Kingdom.ORCID 0000-0001-7134-8416
James M BullockUK Centre for Ecology & Hydrology, Wallingford, United Kingdom.
Pieter S A BeckEuropean Commission, Joint Research Centre (JRC), Ispra, Italy.
Juan A Navas-CortesDepartment of Crop Protection, Institute for Sustainable Agriculture, Spanish National Research Council (CSIC), Córdoba, Spain.
Steven M WhiteUK Centre for Ecology & Hydrology, Wallingford, United Kingdom.

Funding

European Union’s Horizon 2020 Research and Innovation ProgrammeUK Research and Innovation Strategic Priorities
6 · The paper itself

Abstract

Mathematical and computational models play a crucial role in understanding the epidemiology of economically important plant disease outbreaks, and in evaluating the effectiveness of surveillance and disease management measures. A case in point is Xylella fastidiosa, one of the world's most deadly plant pathogens. Since its European discovery in olives in Puglia, Italy in 2013, there remain key knowledge gaps that undermine landscape-scale containment efforts of the outbreak, most notably concerning the year of introduction, the rate of spread, dispersal mechanisms and control efficacy. To address this, we developed a spatially explicit simulation model for the outbreak spreading among olive groves coupled to a simulation of the real surveillance and containment measures. We used Approximate Bayesian Computation to fit the model to surveillance and remote-sensing infection data, comparing the fits for three alternative dispersal mechanisms (isotropic, wind and road). The model accurately explained the rate and spatiotemporal pattern of the outbreak and found weak support for the wind dispersal model over the isotropic model. It suggests that the bacterium may have been introduced as early as 2003 (95% CI [2000, 2009]), earlier than previous estimates and congruent with anecdotal evidence. The isotropic model estimates the pathogen is spreading at 5.7 km y-1 (95% CI [5.4-5.9]) under containment measures, down from 7.2 km y-1 (95% CI [6.9-7.5]) without containment measures. Our estimate of an approximately 10-year lag between introduction and detection highlights the need for stronger biosecurity and surveillance for earlier detection of emerging plant pathogens. The outputs from simulations without any disease management also suggest that while containment measures have caused some slowing of X. fastidiosa spread, stronger measures will be required to contain the outbreak fully.

Indexed as

Models, BiologicalPlant DiseasesXylellaBayes TheoremComputational BiologyComputer SimulationDisease OutbreaksItalyOlea

Identifiers

PMID41042792
PMCPMC12510647

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.