Global mapping of highly pathogenic avian influenza H5N1 and H5Nx clade 2.3.4.4 viruses with spatial cross-validation
Madhur S Dhingra,
Jean Artois,
Timothy P Robinson,
Catherine Linard,
Celia Chaiban,
Ioannis Xenarios,
Robin Engler,
Robin Liechti,
Dmitri Kuznetsov,
Xiangming Xiao,
Sophie Von Dobschuetz,
Filip Claes,
Scott H Newman,
Gwenaëlle Dauphin,
Marius Gilbert
Affiliations
Madhur S Dhingra
Spatial Epidemiology Lab, Université Libre de Bruxelles, Brussels, Belgium; Department of Animal Husbandry and Dairying, Government of Haryana, Panchkula, India
Jean Artois
Spatial Epidemiology Lab, Université Libre de Bruxelles, Brussels, Belgium
Livestock Systems and Environment, International Livestock Research Institute, Nairobi, Kenya
Catherine Linard
Spatial Epidemiology Lab, Université Libre de Bruxelles, Brussels, Belgium; Department of Geography, Université de Namur, Namur, Belgium
Celia Chaiban
Spatial Epidemiology Lab, Université Libre de Bruxelles, Brussels, Belgium
Ioannis Xenarios
Swiss-Prot and Vital-IT group, Swiss Institute of Bioinformatics, Lausanne, Switzerland; Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
Robin Engler
Swiss-Prot and Vital-IT group, Swiss Institute of Bioinformatics, Lausanne, Switzerland
Robin Liechti
Swiss-Prot and Vital-IT group, Swiss Institute of Bioinformatics, Lausanne, Switzerland
Dmitri Kuznetsov
Swiss-Prot and Vital-IT group, Swiss Institute of Bioinformatics, Lausanne, Switzerland
Xiangming Xiao
Department of Microbiology and Plant Biology, University of Oklahoma, Norman, United States; Center for Spatial Analysis, University of Oklahoma, Norman, United States; Institute of Biodiversity Science, Fudan University, Shanghai, China
Sophie Von Dobschuetz
Animal Production and Health Division, Food and Agriculture Organization of the United Nations, Rome, Italy
Filip Claes
Emergency Center for Transboundary Animal Diseases, FAO Regional Office for Asia and the Pacific, Bangkok, Thailand
Scott H Newman
Emergency Center for Transboundary Animal Diseases, Food and Agriculture Organization of the United Nations, Hanoi, Vietnam
Gwenaëlle Dauphin
Animal Production and Health Division, Food and Agriculture Organization of the United Nations, Rome, Italy
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.
