Global mapping of highly pathogenic avian influenza H5N1 and H5Nx clade 2.3.4.4 viruses with spatial cross-validation
- Université Libre de Bruxelles, Belgium
- International Livestock Research Institute, Kenya
- Swiss Institute of Bioinformatics, Switzerland
- University of Oklahoma, United States
- Food and Agriculture Organization of the United Nations, Italy
- Regional Office for Asia and the Pacific, Thailand
- 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.
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Author details
Timothy P Robinson
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.
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.
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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
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Methods: Untargeted metabolomics was performed on serum samples of 5,004 children aged 6-59 months, a subset of participants from the Brazilian National Survey on Child Nutrition (ENANI-2019). ECD was assessed using the Survey of Well-being of Young Children's milestones questionnaire. The graded response model was used to estimate developmental age. Developmental quotient (DQ) was calculated as the developmental age divided by chronological age. Partial least square regression selected metabolites with a variable importance projection ≥ 1. The interaction between significant metabolites and the child's age was tested.
Results: Twenty-eight top-ranked metabolites were included in linear regression models adjusted for the child's nutritional status, diet quality, and infant age. Cresol sulfate (β = -0.07; adjusted-p < 0.001), hippuric acid (β = -0.06; adjusted-p < 0.001), phenylacetylglutamine (β = -0.06; adjusted-p < 0.001), and trimethylamine-N-oxide (β = -0.05; adjusted-p = 0.002) showed inverse associations with DQ. We observed opposite directions in the association of DQ for creatinine (for children aged -1 SD: β = -0.05; p =0.01; +1 SD: β = 0.05; p =0.02) and methylhistidine (-1 SD: β = - 0.04; p =0.04; +1 SD: β = 0.04; p =0.03).
Conclusion: Serum biomarkers, including dietary and microbial-derived metabolites involved in the gut-brain axis, may potentially be used to track children at risk for developmental delays.
Funding: Supported by the Brazilian Ministry of Health and the Brazilian National Research Council.
