Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

780
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
780
Microbe-Plant Interactions01:09

Microbe-Plant Interactions

155
Microbe-plant interactions represent a dynamic spectrum of associations shaped by intricate chemical signaling. These interactions can be neutral, beneficial, or detrimental, and profoundly influence plant physiology, growth, and ecosystem function. The plant microbiome, comprising bacteria, fungi, archaea, protists, and viruses, plays a pivotal role in mediating these effects through surface colonization, internal colonization, or systemic symbiosis.Mutualistic associations, particularly with...
155

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Predicting the cross-continental spread of the cassava brown streak disease epidemic in sub-Saharan Africa.

Scientific reports·2026
Same author

Parameterisation of epidemiological models from small field experiments: A case study of banana bunchy top virus transmission.

Mathematical biosciences·2026
Same author

Optimizing crop clustering to minimize pathogen invasion in agriculture.

Scientific reports·2025
Same author

Optimizing crop varietal mixtures for viral disease management: A case study on cassava virus epidemics.

PLoS computational biology·2025
Same author

Developing a spatio-temporal model for banana bunchy top disease: leveraging remote sensing and survey data.

Frontiers in plant science·2025
Same author

Where to refine spatial data to improve accuracy in crop disease modelling: an analytical approach with examples for cassava.

Royal Society open science·2025
Same journal

Emerging Tree Diseases Driven by Climate Change: A Critical Perspective on Current Challenges and Future Directions.

Annual review of phytopathology·2026
Same journal

Biological Control Microorganisms that Induce Plant Defense Responses.

Annual review of phytopathology·2026
Same journal

Unveiling a Hidden Menace: Invasive Tree Pathogens, Less Known but Increasingly Threatening Southern Hemisphere Forests.

Annual review of phytopathology·2026
Same journal

New Insights into Genomic Variations and Mutational Events Associated with Plant-Pathogen Interactions.

Annual review of phytopathology·2026
Same journal

Tree Killer, Qu'est-ce Que C'est? Insights From Forest Pathogen Genomes.

Annual review of phytopathology·2026
Same journal

From Trucks to Trays: Progress and Challenges in Phytosanitation of Inert Surfaces to Mitigate Plant Pathogen Spread.

Annual review of phytopathology·2026
See all related articles

Related Experiment Video

Updated: May 6, 2026

A Rapid and Efficient Method for Assessing Pathogenicity of Ustilago maydis on Maize and Teosinte Lines
07:09

A Rapid and Efficient Method for Assessing Pathogenicity of Ustilago maydis on Maize and Teosinte Lines

Published on: January 3, 2014

8.5K

Developing Predictive Models and Early Warning Systems for Invading Pathogens: Wheat Rusts.

Christopher A Gilligan1

  • 1Epidemiology and Modelling Group, Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom;

Annual Review of Phytopathology
|June 10, 2024
PubMed
Summary
This summary is machine-generated.

New modeling approaches, using weather and disease data, can track transboundary pathogen spread. This helps create early warning systems for farmers, like those protecting wheat crops from rusts.

Keywords:
Lagrangian particle dispersion modelsaerobiologyepidemiological modelsmechanistic dispersal modelsmeteorological modelstransboundary pathogens

More Related Videos

Visualizing Early Infection Sites of Rice Blast Disease Magnaporthe oryzae on Barley Hordeum vulgare Using a Basic Microscope and a Smartphone
07:36

Visualizing Early Infection Sites of Rice Blast Disease Magnaporthe oryzae on Barley Hordeum vulgare Using a Basic Microscope and a Smartphone

Published on: March 17, 2023

1.6K
Author Spotlight: Integrating Biochemical Functions of β-Glucanases and Peroxidase Enzymes in Wheat-RWA Interaction
10:26

Author Spotlight: Integrating Biochemical Functions of β-Glucanases and Peroxidase Enzymes in Wheat-RWA Interaction

Published on: July 26, 2024

659

Related Experiment Videos

Last Updated: May 6, 2026

A Rapid and Efficient Method for Assessing Pathogenicity of Ustilago maydis on Maize and Teosinte Lines
07:09

A Rapid and Efficient Method for Assessing Pathogenicity of Ustilago maydis on Maize and Teosinte Lines

Published on: January 3, 2014

8.5K
Visualizing Early Infection Sites of Rice Blast Disease Magnaporthe oryzae on Barley Hordeum vulgare Using a Basic Microscope and a Smartphone
07:36

Visualizing Early Infection Sites of Rice Blast Disease Magnaporthe oryzae on Barley Hordeum vulgare Using a Basic Microscope and a Smartphone

Published on: March 17, 2023

1.6K
Author Spotlight: Integrating Biochemical Functions of β-Glucanases and Peroxidase Enzymes in Wheat-RWA Interaction
10:26

Author Spotlight: Integrating Biochemical Functions of β-Glucanases and Peroxidase Enzymes in Wheat-RWA Interaction

Published on: July 26, 2024

659

Area of Science:

  • Aerobiology
  • Epidemiological modeling
  • Computational modeling

Background:

  • Transboundary pathogens pose significant threats to food security, particularly in resource-limited regions.
  • Emergence of new strains, high spore production, and long-distance dispersal exacerbate risks.
  • Limited resources for disease control in affected areas heighten vulnerability.

Purpose of the Study:

  • To introduce a flexible modeling framework for inferring pathogen connectivity networks.
  • To identify characteristic pathways for long-distance spore dispersal, both domestically and internationally.
  • To demonstrate the application of these models in near real-time early warning systems.

Main Methods:

  • Integration of aerobiological and epidemiological modeling techniques.
  • Utilization of historical and near real-time high-resolution weather data.
  • Incorporation of multi-country disease surveillance data and enhanced computing power.

Main Results:

  • Development of powerful techniques to infer connectivity networks for transboundary pathogens.
  • Identification of characteristic pathways for long-distance spore dispersal using wheat rusts as an exemplar.
  • Demonstration of near real-time early warning systems for smallholder farmers in East Africa and South Asia.

Conclusions:

  • Innovations in modeling enable unprecedented inference of pathogen spread.
  • The developed framework effectively identifies dispersal pathways and supports early warning systems.
  • This approach enhances food security by mitigating the impact of transboundary plant diseases like wheat rusts.