Database of present and future situation of West Nile virus in the Afro-Palaearctic Pathogeographic System
Data files
Mar 01, 2024 version files 22.70 MB
-
Database.xlsx
22.70 MB
-
README.md
2.64 KB
Abstract
West Nile virus (WNV) is a globally widespread arthropod-borne virus that poses a significant public health concern. Mosquitoes transmit the virus in an enzootic cycle among birds, which act as reservoirs. Climate plays a crucial role in these outbreaks as mosquitoes are highly influenced by climatic conditions, and bird migrations are also affected by weather patterns. Consequently, changes in climate can potentially impact the occurrence of WNV outbreaks. We used biogeographic modelling based on machine learning algorithms and fuzzy logic to analyse and evaluate separately the risk of WNV outbreaks in two different biogeographic regions, the Afrotropical and the Western Palaearctic region. By employing fuzzy logic tools, we constructed a comprehensive risk model that integrates the Afro-Palaearctic system as a unified operational unit for WNV spread. This innovative approach recognizes the Afro-Palaearctic region as a pathogeographic system, characterized by biannual connections facilitated by billions of migratory bird reservoirs carrying the disease. Subsequently, we forecasted the effects of different climate change scenarios on the spread of WNV in the Afro-Palaearctic system during the periods 2011-2040 and 2041-2070. Our findings revealed an increasing epidemic and epizootic risk south of the Sahara. However, the area where an upsurge in risk was forecasted the most lies within Europe, with the anticipation of risk expansion into regions presently situated beyond the virus' distribution range, including Central and Northern Europe. Gaining insight into the risk within the Afro-Palaearctic system is crucial for establishing coordinated and international One Health surveillance efforts. This becomes particularly relevant in the face of ongoing climate change, which disrupts the ecological equilibrium among vectors, reservoirs, and human populations. We show that the application of biogeographical tools to assess risk of infectious disease, i.e., pathogeography, is a promising approach for understanding the distribution patterns of zoonotic diseases and for anticipating their future spread.
README: Database of Present and future situation of West Nile virus in the Afro-Palaearctic Pathogeographic System
https://doi.org/10.5061/dryad.4xgxd25hs
Dataset containing all the records and variables to elaborate the risk models of West Nile virus infection in the Afro-Palaearctic system. The dataset also contains the the result of the models in favourability values.
Description of the data and file structure
The database is an Excel file where all the information regarding the distribution records of West Nile virus infection cases in Africa and Europe has been compiled, encompassing the Afro-palearctic system.
The field identifier for each Operational Geographic Unit (OGU) is the column HEX10_ID. The "Region" column displays each biogeographic region: "ETIO" for the Afrotropical region and "PAL" for the Palearctic region. "Continent" refers to the African ("AF") and European ("EU") continents. In the database can be seen the mean values for each OGU of different variables. The bioclimatic variables from CHELSA (https://chelsa-climate.org/ include: BIO1, BIO5, BIO6, BIO7, and BIO12. In the database there are also topographic variables such as altitude ("ALT") and slope ("SLOPE"), anthropogenic variables such as population density ("DENS_POB") or distance to railway lines ("DIST_RAIL"), and livestock variables such as ("CHICKEN", "GOAT", or "CATTLE"), and ecosystem variables such as forest loss ("FOREST_LOSS") and different variables from GlobCover (Land Cover version 2.1) indicated as "CLASS". All variables, as well as their sources from which they were extracted, are included in the sheet "Variables Explanation". Additionally, transformations of these variables have also been made to incorporate the unimodal effect of the variables on the distribution effect. These transformations are as follows:
- Squared variables. For example, the variable "CLASS30" squared would be "CLASS30_2".
- Unimodal response. The y or logit of the unimodal response of the variable, such as "YCLASS30".
Furthermore, the values of the variables in the different future projections are found in the various combinations of Shared Socioeconomic Pathways (abbreviated as 126, 370 and 585 ) and Global Circulation Models (abbreviated as MPI and GFDL) for the years 2040 (abbreviated as 40) and 2070 (abbreviated as 70).
Sharing/Access information
The sources of data can be seen in the sheet named "Variables Explanation" inside the Dataset.
Code/Software
Scripts for elaboration of the models can be seen in Supplementary File 1.