Global mapping of highly pathogenic avian influenza H5N1 and H5Nx clade 2.3.4.4 viruses with spatial cross-validation

  1. Madhur S Dhingra
  2. Jean Artois
  3. Timothy P Robinson
  4. Catherine Linard
  5. Celia Chaiban
  6. Ioannis Xenarios
  7. Robin Engler
  8. Robin Liechti
  9. Dimitry Kuznetsov
  10. Xiangming Xiao
  11. Sophie Von Dobschuetz
  12. Filip Claes
  13. Scott H Newman  Is a corresponding author
  14. Gwenaëlle Dauphin  Is a corresponding author
  15. Marius Gilbert  Is a corresponding author
  1. Université Libre de Bruxelles, Belgium
  2. International Livestock Research Institute, Kenya
  3. Swiss Institute of Bioinformatics, Switzerland
  4. University of Oklahoma, United States
  5. Food and Agriculture Organization of the United Nations, Italy
  6. Regional Office for Asia and the Pacific, Thailand
  7. Food and Agriculture Organization of the United Nations, Vietnam

Abstract

Global disease suitability models are essential tools to inform surveillance systems and enable early detection. We present the first global suitability model of highly pathogenic avian influenza (HPAI) H5N1 and demonstrate that reliable predictions can be obtained at global scale. Best predictions are obtained using spatial predictor variables describing host distributions, rather than land use or eco-climatic spatial predictor variables, with a strong association with domestic duck and extensively raised chicken densities. Our results also support a more systematic use of spatial cross-validation in large-scale disease suitability modelling compared to standard random cross-validation that can lead to unreliable measure of extrapolation accuracy. A global suitability model of the H5 clade 2.3.4.4 viruses, a group of viruses that recently spread extensively in Asia and the US, shows in comparison a lower spatial extrapolation capacity than the HPAI H5N1 models, with a stronger association with intensively raised chicken densities and anthropogenic factors.

Data availability

The following previously published data sets were used

Article and author information

Author details

  1. Madhur S Dhingra

    Spatial Epidemiology Lab, Université Libre de Bruxelles, Brussels, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  2. Jean Artois

    Spatial Epidemiology Lab, Université Libre de Bruxelles, Brussels, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  3. Timothy P Robinson

    Livestock Systems and Environment, International Livestock Research Institute, Nairobi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4266-963X
  4. Catherine Linard

    Spatial Epidemiology Lab, Université Libre de Bruxelles, Brussels, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  5. Celia Chaiban

    Spatial Epidemiology Lab, Université Libre de Bruxelles, Brussels, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  6. Ioannis Xenarios

    Swiss-Prot & Vital-IT group, Swiss Institute of Bioinformatics, Lausanne, Switzerland
    Competing interests
    The authors declare that no competing interests exist.
  7. Robin Engler

    Swiss-Prot & Vital-IT group, Swiss Institute of Bioinformatics, Lausanne, Switzerland
    Competing interests
    The authors declare that no competing interests exist.
  8. Robin Liechti

    Swiss-Prot & Vital-IT group, Swiss Institute of Bioinformatics, Lausanne, Switzerland
    Competing interests
    The authors declare that no competing interests exist.
  9. Dimitry Kuznetsov

    Swiss-Prot & Vital-IT group, Swiss Institute of Bioinformatics, Lausanne, Switzerland
    Competing interests
    The authors declare that no competing interests exist.
  10. Xiangming Xiao

    Department of Microbiology and Plant Biology, University of Oklahoma, Norman, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Sophie Von Dobschuetz

    Animal Production and Health Division, Food and Agriculture Organization of the United Nations, Rome, Italy
    Competing interests
    The authors declare that no competing interests exist.
  12. Filip Claes

    Emergency Center for Transboundary Animal Diseases, Regional Office for Asia and the Pacific, Bangkok, Thailand
    Competing interests
    The authors declare that no competing interests exist.
  13. Scott H Newman

    Emergency Center for Transboundary Animal Diseases, Food and Agriculture Organization of the United Nations, Hanoi, Vietnam
    For correspondence
    scott.newman@fao.org
    Competing interests
    The authors declare that no competing interests exist.
  14. Gwenaëlle Dauphin

    Animal Production and Health Division, Food and Agriculture Organization of the United Nations, Rome, Italy
    For correspondence
    Gwenaelle.Dauphin@fao.org
    Competing interests
    The authors declare that no competing interests exist.
  15. Marius Gilbert

    Spatial Epidemiology Lab, Université Libre de Bruxelles, Brussels, Belgium
    For correspondence
    marius.gilbert@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3708-3359

Funding

National Institutes of Health (1R01AI101028-02A1)

  • Madhur S Dhingra
  • Jean Artois
  • Xiangming Xiao
  • Marius Gilbert

United States Agency for International Development (Emerging Pandemic Threats program)

  • Scott H Newman

Biotechnology and Biological Sciences Research Council (BB/L019019/1)

  • Timothy P Robinson

Fonds De La Recherche Scientifique - FNRS (PDR T.0073.13)

  • Catherine Linard
  • Marius Gilbert

Medical Research Council (ESEI UrbanZoo (G1100783/1))

  • Timothy P Robinson

CGIAR (Research Programs on Agriculture for Nutrition and Health (A4NH) and Livestock)

  • Timothy P Robinson

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Colin A Russell, University of Cambridge, United Kingdom

Version history

  1. Received: July 12, 2016
  2. Accepted: November 14, 2016
  3. Accepted Manuscript published: November 25, 2016 (version 1)
  4. Version of Record published: December 16, 2016 (version 2)
  5. Version of Record updated: February 23, 2017 (version 3)
  6. Version of Record updated: March 1, 2017 (version 4)

Copyright

© 2016, Dhingra et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 4,136
    Page views
  • 747
    Downloads
  • 37
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, Scopus, PubMed Central.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Madhur S Dhingra
  2. Jean Artois
  3. Timothy P Robinson
  4. Catherine Linard
  5. Celia Chaiban
  6. Ioannis Xenarios
  7. Robin Engler
  8. Robin Liechti
  9. Dimitry Kuznetsov
  10. Xiangming Xiao
  11. Sophie Von Dobschuetz
  12. Filip Claes
  13. Scott H Newman
  14. Gwenaëlle Dauphin
  15. Marius Gilbert
(2016)
Global mapping of highly pathogenic avian influenza H5N1 and H5Nx clade 2.3.4.4 viruses with spatial cross-validation
eLife 5:e19571.
https://doi.org/10.7554/eLife.19571

Share this article

https://doi.org/10.7554/eLife.19571

Further reading

    1. Epidemiology and Global Health
    Olivera Djuric, Elisabetta Larosa ... The Reggio Emilia Covid-19 Working Group
    Research Article

    Background:

    The aim of our study was to test the hypothesis that the community contact tracing strategy of testing contacts in households immediately instead of at the end of quarantine had an impact on the transmission of SARS-CoV-2 in schools in Reggio Emilia Province.

    Methods:

    We analysed surveillance data on notification of COVID-19 cases in schools between 1 September 2020 and 4 April 2021. We have applied a mediation analysis that allows for interaction between the intervention (before/after period) and the mediator.

    Results:

    Median tracing delay decreased from 7 to 3.1 days and the percentage of the known infection source increased from 34–54.8% (incident rate ratio-IRR 1.61 1.40–1.86). Implementation of prompt contact tracing was associated with a 10% decrease in the number of secondary cases (excess relative risk –0.1 95% CI –0.35–0.15). Knowing the source of infection of the index case led to a decrease in secondary transmission (IRR 0.75 95% CI 0.63–0.91) while the decrease in tracing delay was associated with decreased risk of secondary cases (1/IRR 0.97 95% CI 0.94–1.01 per one day of delay). The direct effect of the intervention accounted for the 29% decrease in the number of secondary cases (excess relative risk –0.29 95%–0.61 to 0.03).

    Conclusions:

    Prompt contact testing in the community reduces the time of contact tracing and increases the ability to identify the source of infection in school outbreaks. Although there are strong reasons for thinking it is a causal link, observed differences can be also due to differences in the force of infection and to other control measures put in place.

    Funding:

    This project was carried out with the technical and financial support of the Italian Ministry of Health – CCM 2020 and Ricerca Corrente Annual Program 2023.

    1. Epidemiology and Global Health
    David Robert Grimes
    Research Advance Updated

    In biomedical science, it is a reality that many published results do not withstand deeper investigation, and there is growing concern over a replicability crisis in science. Recently, Ellipse of Insignificance (EOI) analysis was introduced as a tool to allow researchers to gauge the robustness of reported results in dichotomous outcome design trials, giving precise deterministic values for the degree of miscoding between events and non-events tolerable simultaneously in both control and experimental arms (Grimes, 2022). While this is useful for situations where potential miscoding might transpire, it does not account for situations where apparently significant findings might result from accidental or deliberate data redaction in either the control or experimental arms of an experiment, or from missing data or systematic redaction. To address these scenarios, we introduce Region of Attainable Redaction (ROAR), a tool that extends EOI analysis to account for situations of potential data redaction. This produces a bounded cubic curve rather than an ellipse, and we outline how this can be used to identify potential redaction through an approach analogous to EOI. Applications are illustrated, and source code, including a web-based implementation that performs EOI and ROAR analysis in tandem for dichotomous outcome trials is provided.