Global risk mapping of highly pathogenic avian influenza H5N1 and H5Nx in the light of epidemic episodes occurring from 2020 onwards
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles (ULB), Belgium
- Data Science Institute, University of Hasselt, Belgium
- Food and Agriculture Organization of the United Nations, Italy
- Interactions Hôtes-Agents Pathogènes (IHAP), Université de Toulouse, France
- Environmental Research Group Oxford Ltd, c/o Department of Biology, United Kingdom
- Avia-GIS research department, Avia-GIS, Belgium
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles, Vrije Universiteit Brussel, Belgium
Figures
Figure 1
Epidemic curves for both wild and domestic bird cases.
Orange and blue histograms report the weekly number of H5N1 cases and the weekly number of H5Nx cases, respectively.
Figure 2 with 4 supplements
Response curves associated with the environmental variables included in the ecological niche models.
For the ecological niche models trained on wild bird infection records, we here only display the response curves estimated for the environmental variables associated with an averaged relative influence (RI) >4% for at least one of the considered occurrence datasets (thus not reporting the response curves obtained for the following variables: evergreen deciduous needleleaf trees, evergreen broadleaf trees, shrublands, and regularly flooded vegetation). Each curve was retrieved from a distinct boosted regression tree (BRT) model trained for a specific dataset of occurrence data. We also report the averaged RI (in %) of each environmental variable in the respective ecological models trained on a specific dataset of occurrence data (see Supplementary file 1 for the complete list of RI estimates along with their first and third quartiles). Due to a lack of data, the model was not trained for H5N1 in wild birds before 2020.
Figure 2—figure supplement 1
Environmental factors included in the ecological niche modelling.
‘LST’ refers to ‘land surface temperature’, ‘NDVI’ to the ‘normalised difference vegetation index’, and ‘EVI’ to the ‘enhanced vegetation index’.
Figure 2—figure supplement 2
Comparison between the presence/pseudo-absence data used to train the ecological niche models (1° column) and the resulting ecological niche suitability maps (2° column).
In the first column, presence and sampled pseudo-absence points are displayed as red and dark points, respectively. As detailed in the Materials and methods section, pseudo-absence points were only sampled in countries in which there was at least one presence point (for either H5N1 or H5Nx, the considering time period, and bird populations). For Russia and China, we considered the admin-1 administrative areas instead of the country to define if pseudo-absence points should be sampled in the corresponding area. Specifically, for a given administrative region (i.e. a given country or admin-1 region in case of Russia or China) gathering at least one presence point, we sampled a number of pseudo-absence points equal to three times the number of presence points in that region. Furthermore, pseudo-absence points were sampled according to the log-transformed raster of human population density, which prevented the sampling of pseudo-absence in unpopulated areas non-associated with any surveillance activity.
Figure 2—figure supplement 3
Comparison between the presence/pseudo-absence data used to train the ecological niche models (1° column) and the resulting ecological niche suitability maps (2° column).
In the first column, presence and sampled pseudo-absence points are displayed as red and dark points, respectively. As detailed in the Materials and methods section, pseudo-absence points were only sampled in countries in which there was at least one presence point (for either H5N1 or H5Nx, the considering time period, and bird populations). For Russia and China, we considered the admin-1 administrative areas instead of the country to define if pseudo-absence points should be sampled in the corresponding area. Specifically, for a given administrative region (i.e. a given country or admin-1 region in case of Russia or China) gathering at least one presence point, we sampled a number of pseudo-absence points equal to three times the number of presence points in that region. Furthermore, pseudo-absence points were sampled according to the log-transformed raster of human population density, which prevented the sampling of pseudo-absence in unpopulated areas non-associated with any surveillance activity.
Figure 2—figure supplement 4
Boxplots reporting the spatial sorting bias (SSB) and area under the curve (AUC) of the receiving operator curve metrics associated with various ecological niche models trained for H5N1 and H5Nx.
For the SSB estimates, we report values obtained for the standard cross-validation (A), as well as spatial cross-validation procedures based on the reference points (B) and the blocks generation (C) approaches (see the Materials and methods section for further detail). SSB estimates range from 0 to 1; values close to 0 indicate a strong impact of spatial autocorrelation on model training, while values close to 1 suggest little to no impact. The AUC metrics computed for the ecological niche models trained with the spatial cross-validation procedures based on the ‘epicentres’, and we here report the values obtained for the models trained on each of the four distinct sets of environmental factors.
Figure 3 with 1 supplement
Areas ecologically suitable for local H5N1 or H5Nx circulation leading to infection cases in domestic bird populations.
We estimated the ecological suitability for two different time periods (2015–2020 and 2020–2022) and for both wild and domestic bird populations. Dynamic visualisations of the results are available here: https://mood-platform.avia-gis.com/core.
Figure 3—figure supplement 1
Comparison between the ecological niche suitability estimated for H5N1 and H5Nx before 2016 and after 2020 for domestic bird populations.
Specifically, we here compare the ecological niche models trained by Dhingra et al., 2016, on occurrence records collected between January 2004 and March 2015 (<2016) and the ecological niche models trained in the present study on occurrence records collected between January 2020 and March 2023 (>2020).
Author response image 1
Comparison of variable relative influence (%) between models trained with pseudo-absences weighted by poultry density (red) and human population density (blue) for domestic bird outbreaks.
Results are shown for four datasets: H5N1 (<2020), H5N1 (>2020), H5Nx (<2020), and H5Nx (>2020).
Author response image 2
Comparison of variable relative influence (%) between models trained with pseudo-absences weighted by poultry density (red) and human population density (blue) for wild bird outbreaks.
Results are shown for three datasets: H5N1 (>2020), H5Nx (<2020), and H5Nx (>2020).
Tables
Table 1
Species diversity indices estimated from infected bird cases before and after 2020.
This table presents the Shannon and Simpson species diversity indices for various bird groups, comparing values before and after the year 2020. The indices are provided for all birds, sea birds, wild birds, domestic birds, as well as all birds affected by H5N1 and non-H5N1 strains.
| All birds | Sea birds | Wild birds | Domestic birds | All birds H5N1 | All birds non-H5N1 | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Shannon | Simpson | Shannon | Simpson | Shannon | Simpson | Shannon | Simpson | Shannon | Simpson | Shannon | Simpson | |
| <2020 | 4.227 | 0.971 | 2.187 | 0.843 | 3.072 | 0.832 | 3.919 | 0.966 | 2.003 | 0.833 | 3.415 | 0.945 |
| >2020 | 5.033 | 0.982 | 2.255 | 0.831 | 5.066 | 0.983 | 2.967 | 0.897 | 2.952 | 0.907 | 3.254 | 0.914 |
Author response table 1
Comparison of model predictive performances (AUC) between pseudo-absence sampling were weighted by poultry density and by human population density across host groups, virus types, and time periods.
Differences in AUC values are shown as the value for poultry-weighted minus human-weighted pseudo-absences.
| Dataset | AUC (poultry-weighted pseudo-absences) | AUC (human-weighted pseudo-absences) | AUC differences between the two approaches |
|---|---|---|---|
| H5N1, domestic birds, <2020 | 0.877 | 0.864 | -0.013 |
| H5N1, wild birds, >2020 | 0.837 | 0.829 | -0.008 |
| H5N1, domestic birds, >2020 | 0.831 | 0.828 | -0.003 |
| H5Nx, wild birds, <2020 | 0.856 | 0.858 | 0.002 |
| H5Nx, domestic birds, <2020 | 0.853 | 0.855 | 0.002 |
| H5Nx, wild birds, >2020 | 0.847 | 0.842 | -0.005 |
| H5Nx, domestic birds, >2020 | 0.835 | 0.833 | -0.002 |
Additional files
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Supplementary file 1
Supplementary tables reporting model performance metrics (area under the curve [AUC]) and relative influence estimates of environmental variables used in the ecological niche models.
- https://cdn.elifesciences.org/articles/104748/elife-104748-supp1-v1.docx
-
Supplementary file 2
Distribution of H5Nx and H5N1 occurrence records in sea birds from 2015 to 2023, categorised by bird family.
The three panels successively show the total occurrence records for all H5Nx subtypes, occurrence records for non-H5N1 H5Nx subtypes, and all H5N1 occurrence records.
- https://cdn.elifesciences.org/articles/104748/elife-104748-supp2-v1.docx
-
MDAR checklist
- https://cdn.elifesciences.org/articles/104748/elife-104748-mdarchecklist1-v1.docx
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Global risk mapping of highly pathogenic avian influenza H5N1 and H5Nx in the light of epidemic episodes occurring from 2020 onwards
eLife 14:RP104748.
https://doi.org/10.7554/eLife.104748.4
