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

In 1996, highly pathogenic avian influenza of subtype H5N1 gave rise to the progenitor of the present H5N1 HPAI subtype in Guangdong, China (A/Goose/Guangdong/1/96[H5N1]) (Duan et al., 2007). Initially, restricted to Southern China, the virus started spreading in 2003 and by 2008, it had spread to more than 60 countries (Food and Agriculture Organization of the United Nations, 2016), persisting now in only a few. Transmission of infection from birds to humans was also reported, causing disease in 850 confirmed human cases including 449 deaths, as of May 2016, making this virus a continuing source of human health concerns (WHO/GIP, 2016; Lai et al., 2016). Re-assortment with other influenza viruses led to the replacement of most internal viral genes of the original H5N1 virus. However, the haemagglutinin (HA) gene H5 has remained present in all isolates and was therefore used to develop a standardised ‘clade’ nomenclature, first adopted in 2008, based on the evolution and divergence of H5N1 viruses that evolved from the original HA gene of the 1996 H5N1 virus (WHO/OIE/FAO H5N1 Evolution Working Group, 2008). In the initial years from 1996 to 2008, 10 distinct clades (0–9) had been generated and by 2012, 11 distinct actively circulating clades had been identified (World Health Organization/World Organisation for Animal Health/Food and Agriculture Organization (WHO/OIE/FAO) H5N1 Evolution Working Group, 2014).

Between 2009 and 2013, H5Nx HPAI viruses from the clade 2.3.4 showed an apparent geographical range expansion and were not only of the H5N1 subtype. Continuous live-poultry market surveillance in China identified novel clade 2.3.4 reassortant viruses of different H5N2, H5N5 and H5N8 subtypes, alongside H5N1 (Gu et al., 2011; Zhao et al., 2012, 2013). All these viruses were part of an H5 monophyletic group of viruses that shared the H5 gene of an H5N1 clade 2.3.4 variant with neuraminidase (NA) genes from different viruses (Gu et al., 2013). Consequently, the nomenclature of H5Nx viruses that clustered in this divergent HA group was updated as a new clade 2.3.4.4, in addition to two other new clades (Smith and Donis, 2015). From January 2014 onward, viruses of clade 2.3.4.4 started spreading internationally. The first H5N8 HPAI virus outbreaks outside of China were reported in South Korea and Japan in spring 2014 (Hill et al., 2015). In May 2014, a novel H5N6 clade 2.3.4.4 reassortant caused outbreaks in China and Lao PDR (Wong et al., 2015) and thereafter from Viet Nam and Myanmar. In November 2014, H5N8 HPAI viruses were reported from Germany, Netherlands, UK, Italy and the Russian Federation in rapid succession. In the same autumn and winter 2014/2015, H5N2 HPAI were reported from outbreaks in British Colombia, Canada. This virus contained genes similar to those of the Eurasian clade 2.3.4.4 alongside genes from North American wild bird lineages (World Organisation for Animal Health, 2016 ). In Taiwan, novel (H5N2, H5N3) reassortants also caused several outbreaks in 2014 (Lee et al., 2016). In December 2014, the new H5N8 and H5N2 HPAI viruses were detected in wild birds in Washington USA, before being found in poultry. By February 2015, the H5N2 HPAI virus had triggered a true epidemic in commercial poultry in the US, with nearly 43 million chickens and 7 million turkeys killed or culled across more than 20 different states (Poultry Science Association, 2016). All these HPAI H5N1, H5N2, H5N6 and H5N8 viruses found in Eurasia and North America shared an H5 gene segment belonging to clade 2.3.4.4 (Claes et al., 2014; Food and Agriculture Organization of the United Nations, 2016).

In summary, we can describe two periods and groups of viruses. From 2003 to 2010, the H5 HPAI viruses responsible for international spread and most outbreaks in poultry were of the N1 type, with continuous evolution of the H genes into different sub-lineages and gradual changes in its internal genes yielding clades and sub clades. From 2010 onward, H5Nx clade 2.3.4 viruses reassorted with several other avian influenza viruses leading to generation of a diversity of H5 clade 2.3.4.4 viruses bearing NA other than N1 in Asia. These novel reassortant viruses then began spreading internationally in 2013, in some cases further reassorting with viruses from other geographic lineages to yield new viruses, all bearing an H5 clade 2.3.4.4 haemagglutinin.

Following the spatio-temporal pattern of H5N1 HPAI spread, several spatial analytical studies were conducted to identify risk factors associated with H5N1 HPAI presence. The majority of these have been country-level studies in Thailand (Gilbert et al., 2006), Viet Nam (Minh et al., 2009), China (Martin et al., 2011), Bangladesh (Ahmed et al., 2012), Indonesia (Yupiana et al., 2010) and India (Dhingra et al., 2014). Several studies have also been conducted at regional (Adhikari D, 2009; Gilbert et al., 2008; Williams and Peterson, 2009) and continental levels (Hogerwerf et al., 2010; Peterson and Williams, 2008). Spatial risk factors associated with H5N1 HPAI presence through different studies were reviewed in 2012 (Gilbert and Pfeiffer, 2012) and the study highlighted domestic duck density, indices of water presence (distance to rivers and proportion of land occupied by water) and anthropogenic variables (human population density and distance to roads) to be the most consistent risk factors across studies, countries and scales. However, studies comparing different sets of factors were never carried out at a global scale, and none made a distinction between clades and sub-lineages.

In this analysis, we aimed to produce a first global suitability map for H5N1 HPAI virus sustained transmission, to establish its capacity to provide reliable spatial extrapolations at large spatial scales and to compare different sets of spatial predictor variables in their predictive capacity. Machine learning techniques have become very powerful in reproducing observed distribution patterns with sets of predictor variables, but their skill in spatial extrapolation is rarely quantified and could help better discriminate among sets of important predictor variables. In addition, the very fast recent spread of clade 2.3.4.4 H5Nx viruses (H5N1, H5N2, H5N6 and H5N8), associated with multiple reassortments was unprecedented (De Vries et al., 2015) and warranted further examination. A separate analysis of how 2.3.4.4 H5Nx viruses had spread in the geographical and environmental space was hence carried out in comparison to the HPAI H5N1 viruses.

Boosted Regression Trees (BRT) models were developed to predict the global suitability of H5N1 HPAI and H5Nx clade 2.3.4.4 presence. The predictor variables were categorised into four sets (Table 1) of variables. The Set 1 variables included the host variables of extensive and intensive chicken densities, human population density, and a variable to account for the effect of mass vaccination of poultry in China (IsChina). Set 2 included land cover variables with IsChina. Set 3 included Fourier-transformed climatic variables of land-surface temperature (LST) and Normalised Difference Vegetation Index (NDVI) with IsChina. Finally, Set 4 variables included Set 1 variables in addition to selected variables from the earlier sets that were selected on the basis of prior epidemiological knowledge. The models were subjected to three different types of cross validations to measure their goodness-of-fit (GOF) and transferability: (i) standard cross-validation (CV) with a random and stratified divide between training and validation sets, (ii) a calibrated cross-validation to account for the spatial sorting bias (SSB) sensu Hijmans (2012) i.e. the tendency to have distance between training-presence and testing-presence sites to be smaller than the distance between training-presence and testing-absence sites, and (iii) a spatial cross-validation (Spatial CV) to spatially separate the training and validation sets by large distances and measure the spatial extrapolation capacity of the models.

The bootstrapped goodness of fit values for the H5N1 HPAI and H5Nx HPAI clade 2.3.4.4 models for the different sets of covariates and cross validation methods are shown in Figure 1. For the H5N1 HPAI global model, all overall GOF metrics were good with predictive accuracy Area Under the Curve (AUC) values higher than 0.9 when evaluated through standard CV (Figure 1). The reduction in GOF taking into account the SSB was minor and followed the same pattern. However, when evaluated through spatial CV, the different sets of covariates showed contrasting AUC values. The land-use (Set 2) and eco-climatic (Set 3) based models extrapolated poorly, and the Set 1 and Set 4 performed best. It is also noteworthy, that the combination of Sets 1 and 2 (Set 2.1), or Sets 1 and 3 (Set 3.1) did not result in significantly better models than Set 1 alone (Figure 1—figure supplement 1), and even tended to reduce the average AUC of spatial CV.

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The models for the H5Nx clade 2.3.4.4 virus also had high GOF metrics estimated by standard CV (Figure 1). Here too, a significant amount of predictive power was already obtained with the models containing only Set 1 variables, with AUC values close to 0.9. There was a strong impact of spatial CV on the GOF metrics, with a drastic reduction in predictive power when extrapolating over large distances (Figure 1). Throughout the different spatial CV metrics, Set 2, and 4 showed better AUC values than Set 1, and given that Set 4 was more parsimonious, with fewer predictor variables, it was kept as the final model for H5Nx clade 2.3.4.4 suitability. Similar conclusions could be drawn from models using combinations of Set 1 and Set 2 (Set 2.1), or Set 1 and Set 3 (Set 3.1) (Figure 1—figure supplement 1).

The relative contribution (RC) of the predictor variables of Set 1 and Set 4 for H5N1 HPAI and the H5 HPAI clade 2.3.4.4 models are presented in Figure 2. The most noticeable difference concerned the role of domestic duck density, human population density and chicken density. The H5Nx HPAI clade 2.3.4.4 showed much higher RC for human population density and intensively raised chickens than the H5N1 HPAI one. Conversely, a comparatively much higher RC of domestic duck density and extensively raised chicken was observed for the H5N1 HPAI model than for the H5Nx HPAI clade 2.3.4.4 models. Upon the inclusion of additional predictors in Set 4 (Figure 2), the influence of these host-based predictor variables followed a similar pattern. In addition, annual mean temperature made a relatively high contribution in both models, and cultivated vegetation showed a much higher RC in the H5Nx HPAI clade 2.3.4.4 model than in the H5N1 HPAI one.

Figure 2

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Partial dependence plots of the BRT models allow the contribution of a particular variable to be depicted on the fitted response after taking into account the effect of all the other predictors in the model (Figure 3, and Figure 3—figure supplement 1). The main difference between the partial dependence plots of the different variables was for the density of extensively raised chickens, which showed a positive association with H5N1 HPAI presence contrasting with an absence of association with the H5Nx HPAI clade 2.3.4.4 presence (Figure 3). Other profiles were somewhat comparable for the two groups of viruses and showed a positive association between virus presence and duck density, intensively raised chicken density, human population density, a negative association with the IsChina variable (Figure 3) and an optimum for percentage of cropland and temperature (Figure 3—figure supplement 1). It should be kept in mind that their relative contributions, i.e. their weight in the final prediction strongly differed between the two groups of viruses. It is noteworthy that the models outlined above were built using optimal number of trees estimated through spatial CV instead of standard CV, and this resulted in much lower optimal number of trees compared to standard CV models (Figure 3—figure supplement 2), suggesting that standard CV may be over fitting local clusters of presence points rather than making reliable large-distance predictions. The suitability maps of the models are presented in Figure 4. To interpret the extrapolation capacity of these suitability maps, multivariate environmental similarity surfaces (MESS) (Elith et al., 2010) were computed (Figure 4—figure supplement 3) giving information on where the models extrapolate within the range of predictor variables in the occurrence points. As observed, both models extrapolate prediction in areas with similar environmental conditions, as depicted by positive MESS values. However, the geographical space with high similarity to the occurrence point is comparatively wider for the HPAI H5N1 model, than for the H5Nx clade 2.3.4.4 models.

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Figure 4—source data 1

Suitability predictions for the HPAI H5N1 best model (GeoTiff format).

https://doi.org/10.7554/eLife.19571.010

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Figure 4—source data 2

Suitability predictions for the H5Nx clade 2.3.4.4 best model (GeoTiff format).

https://doi.org/10.7554/eLife.19571.011

Download elife-19571-fig4-data2-v4.zip

As expected, high suitability values for the H5N1 HPAI model (Figure 4) are found in several parts of Asia, including China (when the effect of the IsChina variable is removed). Other areas where H5N1 HPAI has spread extensively are highlighted, such as eastern Indo-Gangetic plain, Thailand central plain, south Myanmar and the Red river and Mekong deltas of Vietnam, the island of Java in Indonesia and the Nile Delta in Egypt. The model also highlights areas where H5N1 HPAI was introduced but did not persist over long periods of time, such as in South Korea, Japan, Ukraine and Romania. Areas of western Africa, such as Nigeria, where the H5N1 HPAI outbreaks have been unfolding since late 2014 have been predicted as suitable by the model. Isolated parts in Eastern Europe, North America, Mexico, Dominican Republic and South America, are also deemed suitable for H5N1 establishment.

The suitability map for the H5Nx HPAI clade 2.3.4.4 virus is somewhat different, highlighting more isolated areas (Figure 4). The spatial extrapolation capacity of this model was low, and predictions made at large distances from known points of presence should be interpreted with caution. As this clade is still spreading, there may still be large areas of the landscape where it could potentially become established and where the model predictions may be inaccurate. In areas close to presence points, the predictions are believed to be robust, with several areas within Asia, such as China, South Korea, Japan and Taiwan depicted as suitable. The well-known virus ‘reassortment-sink’ areas of the Indo-Gangetic plains, the river deltas of Vietnam, southern Myanmar and Java, Indonesia, are also highlighted as areas of suitability. In Africa, the Nile delta is depicted as suitable for establishment. In North America, the high suitability areas match the intensive poultry areas of the Midwestern and southern states of USA. The Netherlands, Belgium and northwest France are highlighted with high suitability in Europe. In Australia, the commercial poultry rearing areas of Victoria and New South Wales are predicted as suitable; even though HPAI subtype H5 has never been reported in Australia.

The suitability predictions for the HPAI H5N1 and H5Nx clade 2.3.4.4 best models using Set 1 and Set 4 predictor variables are in Figure 4—source data 1 and 2, respectively. The suitability maps for H5Nx HPAI and H5 clade 2.3.4.4 for Sets 2 and 3 are presented in Figure 4—figure supplement 1 and Figure 4—figure supplement 2, respectively.

A first important result of this study is that it was possible to build a global suitability model for HPAI H5N1 virus with a high extrapolation capacity robustly established through spatial cross-validation. Interestingly, H5N1 HPAI outbreaks appeared to be best modelled by predictor variables relating to host distribution. Alternative models based on land use or eco-climatic variables showed marginally better accuracy metrics when evaluated with standard CV, but significantly lower extrapolation capacity than the host-only variable. Even the models combining host variables with other environmental predictors did not produce significantly better results when evaluated through spatial CV. This observation matches earlier observations that association with eco-climatic variables were not consistently reproducible across countries and studies (Gilbert and Pfeiffer, 2012) and that H5N1 HPAI is probably not as strongly environmentally constrained as other authors have suggested (Williams and Peterson, 2009; Zhang et al., 2014). This strongly contrasts with vector-borne diseases, where clear eco-climatic boundaries of vectors can be mapped, and where climate has a strong influence on vector seasonality and population dynamics (McMichael and Lindgren, 2011; Morin and Comrie, 2013). In the case of a directly contagious disease such as avian influenza, successful transmission and clinical outbreaks have been observed over a wide range of temperature and humidity conditions (e.g. Russia, Nigeria, Egypt, Northern China, Indonesia). Our results suggest that the main large-scale constrains to suitability for H5N1 HPAI occurrence are related to the distribution of hosts; densities of chickens and ducks raised in different systems, and to the density of the human population, probably as a surrogate measure for various anthropogenic transmission mechanisms. For the new H5Nx clade 2.3.4.4 viruses, we found a somewhat different result, with a clear improvement of the extrapolation capacity of models using a set of variables combining host distribution and environmental variables. However, these models were of relatively low overall predictive power, most likely because the virus has not yet had a chance to extend fully to its potential range of occurrence as compared to H5N1 HPAI, and false pseudo-absences may have had a strong impact on the construction of models and, therefore, on the accuracy of predictions. For this model, and given its low extrapolation capacity, we emphasise that predictions made at long distances from points of presence should be interpreted with caution, as there may still be large areas where it could potentially become established and where the our model predictions may be inaccurate.

A second important result of this study is to demonstrate the importance of spatial CV in building and validating avian influenza suitability models over large geographical extents. The difference between standard and spatial CV evaluation of GOF was already quite significant for the HPAI H5N1 models. The difference was even more striking for the H5Nx clade 2.3.4.4 models, which all appeared very good when evaluated through standard CV. However, they had poor extrapolation accuracy, sometimes even not much better than a null model when evaluated through spatial CV. Machine-learning techniques used in species distribution modelling have become incredibly powerful at reproducing a pattern from a given set of occurrence data and this is essentially what standard CV measures. However, as our results demonstrate, default cross-validation technique is a very misleading measure of their geographical extrapolation capacity. Spatial CV was found not only to be important for evaluating the extrapolation capacity of a given model, but also to be the only way to truly discriminate our model outputs based on different sets of predictor variables. The focus on extrapolation capacity for selecting predictor variables is driven by the assumption that a model that includes statistical relationships linked to causal mechanisms should spatially extrapolate well, as the cause-consequence statistical associations have a greater chance to apply well in different places than those that are coincidental. Of course, cause-consequence relationships may vary in space too, but the underlying assumption of suitability modelling extrapolation is that these remain constant over the spatial domain within which the model is applied. So, models based on coincidental statistical associations are expected to extrapolate poorly in the geographical domain, and these losses of predictability can hardly be quantified through standard CV because of spatial correlations between training and validation sets.

A third set of important results consisted in the comparison of the H5N1 HPAI and H5Nx clade 2.3.4.4 models, which showed areas of convergences and differences in the geographic and predictor variables spaces. Domestic duck density was the most important variable for both models, though with a lower RC for the H5Nx clade 2.3.4.4 model in Set 4. Ducks have always been strongly associated with areas of persistence and evolution of H5N1 HPAI (Gilbert and Pfeiffer, 2012), which relates to their capacity to act as an intermediate, domestic reservoir between wild Anatidae, the main wild reservoir of avian influenza viruses, and domesticated poultry. Ducks have been referred to as the ‘Trojan horses’ for H5N1 HPAI H5N1 presence (Kim et al., 2014) on account of their role in virus introduction, evolution, transmission and persistence (Hulse-Post et al., 2005), which has been demonstrated in both host pathogenicity (Cornelissen et al., 2013; Smith and Donis, 2015) and geospatial studies (Gilbert and Pfeiffer, 2012). The absence of duck density may in fact explain a lot of the difference in extrapolating capacity found between the host-model (Set 1) and the land-use and eco-climatic models (Set 2 and 3) that cannot discriminate areas with similar land-use and eco-climatic conditions, but that have very different duck densities. For example, India is predicted at relatively high suitability by the land-use model (Set 2, Figure 4) at a very low suitability by the host-based (except around Bangladesh) reflecting the near-absence of significant domestic duck densities in much of the country, in accordance with previous results (Gilbert et al., 2010).

The finding of a strong association between H5Nx clade 2.3.4.4 and ducks was somewhat less expected as the disease was found mostly in chicken farms in more intensive poultry production areas, but results are, however, in line with those of Hill et al. (2015) who found through phylogeographic analysis that the introduction of H5Nx clade 2.3.4.4 to South Korea was associated with areas where domestic ducks and wild waterfowl intermingled. Complex reassortment of multiple subtypes may also occur in areas where domestic ducks and migratory birds have an opportunity to share food, water and habitat, creating opportunities for virus transmission between different species, co-infection of individual animals with different influenza viruses and subsequent gene reassortment (Deng et al., 2013). It would be prudent for countries to put such areas under active surveillance for early detection of HPAI introductions and for monitoring of virus evolution. This would include the countries of the Americas and African continent where duck rearing is not as common as in South East Asia. It is noteworthy that one of the most severe recent H5 HPAI epidemics that started in 2015 in Dordogne region of France, a traditionally important duck rearing area with some of the highest duck densities in the country, even if the outbreaks were apparently caused by distinct H5 viruses from those circulating in Asia. So, the association found with domestic duck densities fits with existing knowledge of H5N1 spatial epidemiology and was a major predictor in both the H5N1 HPAI and H5Nx clade 2.3.4.4 models.

In contrast, the association with extensively and intensively raised chickens provided different results for the H5N1 HPAI and H5Nx clade 2.3.4.4 models, with the latter being more strongly associated with intensified chicken production systems, found in intensive crop production areas with high human population densities. An interesting hypothesis to explain this pattern would be a greater fitness of H5Nx clade 2.3.4.4 viruses to spread through intensive chicken production and poultry trade systems (Claes et al., 2016). We still lack extensive published experimental infection results of the new clade in poultry, but preliminary results are indicative of a lower pathogenicity of the H5Nx clade 2.3.4.4 virus in chickens compared to H5N1 HPAI, with longer survival and shedding period (Kim et al., 2014; Swayne et al., 2015). A lower virulence in chicken was also found for the reassortant H5N2, H5N6 and H5N8 clade 2.3.4.4 viruses compared to previous 2.3.4 HPAI H5N1 viruses (Sun et al., 2016), although they remained highly pathogenic. A lower mortality and longer period of infectivity may assist the virus in circulating longer and within intensified poultry production and trading systems, leading to increased opportunities for onward transmission. Evolution towards reduced pathogenicity would appear an asset in improving farm-to-farm transmission and long-term persistence even in the absence of domestic ducks. This could partly explain the stronger association of H5Nx clade 2.3.4.4 viruses with intensive chicken production areas in eastern Asia and in the US.

Our analyses have focussed on poultry outbreak locations and are therefore of more limited use in identifying the locations of initial introduction of avian influenza viruses, or places where viruses may undergo more frequent reassortment events leading to the local emergence of new viruses. Future work may look more explicitly into those aspects and could lead to better prevention at the sources of virus introduction and emergence.

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Author details

1. Madhur S Dhingra

   1. Spatial Epidemiology Lab, Université Libre de Bruxelles, Brussels, Belgium
   2. Department of Animal Husbandry and Dairying, Government of Haryana, Panchkula, India

   Contribution

   MSD, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Jean Artois

   Competing interests

   The authors declare that no competing interests exist.

2. Jean Artois

   Spatial Epidemiology Lab, Université Libre de Bruxelles, Brussels, Belgium

   Contribution

   JA, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Madhur S Dhingra

   Competing interests

   The authors declare that no competing interests exist.

3. Timothy P Robinson

   Livestock Systems and Environment, International Livestock Research Institute, Nairobi, Kenya

   Contribution

   TPR, Conception and design, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-4266-963X

4. Catherine Linard

   1. Spatial Epidemiology Lab, Université Libre de Bruxelles, Brussels, Belgium
   2. Department of Geography, Université de Namur, Namur, Belgium

   Contribution

   CL, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

5. Celia Chaiban

   Spatial Epidemiology Lab, Université Libre de Bruxelles, Brussels, Belgium

   Contribution

   CC, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

6. Ioannis Xenarios

   1. Swiss-Prot and Vital-IT group, Swiss Institute of Bioinformatics, Lausanne, Switzerland
   2. Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland

   Contribution

   IX, Conception and design, Drafting or revising the article, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

7. Robin Engler

   Swiss-Prot and Vital-IT group, Swiss Institute of Bioinformatics, Lausanne, Switzerland

   Contribution

   RE, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

8. Robin Liechti

   Swiss-Prot and Vital-IT group, Swiss Institute of Bioinformatics, Lausanne, Switzerland

   Contribution

   RL, Drafting or revising the article, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

9. Dmitri Kuznetsov

   Swiss-Prot and Vital-IT group, Swiss Institute of Bioinformatics, Lausanne, Switzerland

   Contribution

   DK, Drafting or revising the article, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

10. Xiangming Xiao

    1. Department of Microbiology and Plant Biology, University of Oklahoma, Norman, United States
    2. Center for Spatial Analysis, University of Oklahoma, Norman, United States
    3. Institute of Biodiversity Science, Fudan University, Shanghai, China

    Contribution

    XX, Acquisition of data, Drafting or revising the article

    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

    Contribution

    SVD, Conception and design, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

12. Filip Claes

    Emergency Center for Transboundary Animal Diseases, FAO Regional Office for Asia and the Pacific, Bangkok, Thailand

    Contribution

    FC, Conception and design, Drafting or revising the article

    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

    Contribution

    SHN, Conception and design, Drafting or revising the article

    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

    Contribution

    GD, Conception and design, Acquisition of data, Drafting or revising the article

    For correspondence

    Gwenaelle.Dauphin@fao.org

    Competing interests

    The authors declare that no competing interests exist.

15. Marius Gilbert

    1. Spatial Epidemiology Lab, Université Libre de Bruxelles, Brussels, Belgium
    2. Fonds National de la Recherche Scientifique, Brussels, Belgium

    Contribution

    MG, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

    For correspondence

    marius.gilbert@gmail.com

    Competing interests

    The authors declare that no competing interests exist.

    "This ORCID iD identifies the author of this article:" 0000-0003-3708-3359

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