DOI: 10.5061/dryad.4xgxd25hs
title: Database of present and future situation of West Nile virus in the Afro-Palaearctic Pathogeographic System

description: 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.
description: # Database of Present and future situation of West Nile virus in the
 Afro-Palaearctic Pathogeographic System
 [https://doi.org/10.5061/dryad.4xgxd25hs](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/](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.