  West Nile virus (WNV) is a vector-borne pathogen of global relevance and is
currently the most widely distributed flavivirus of encephalitis worldwide.
This virus infects birds, humans, horses, and other mammals, and its
transmission cycle occurs in urban and rural areas. Climate conditions have
direct and indirect impacts on vector abundance and virus dynamics within the
mosquito. The significance of environmental variables as drivers in WNV
epidemiology is increasing under the current climate change scenario. In this
study, we used a machine learning algorithm to model WNV distributions in South
America. Our model evaluated eight environmental variables (type of biome,
annual temperature, seasonality of temperature, daytime temperature variation,
thermal amplitude, seasonality of precipitation, annual rainfall, and
elevation) for their contribution to the occurrence of WNV since its
introduction in South America (2004). Our results showed that environmental
variables can directly alter the occurrence of WNV, with lower precipitation
and higher temperatures associated with increased virus incidence. High-risk
areas may be modified in the coming years, becoming more evident with high
greenhouse gas emission levels. Countries such as Bolivia and Paraguay will be
greatly affected, drastically changing their current WNV distribution. Several
Brazilian areas will also increase the likelihood of presenting WNV, mainly in
the Northeast and Midwest regions and the Pantanal biome. The Galapagos Islands
will also probably increase their geographic range suitable for WNV occurrence.
It is necessary to develop preventive policies to minimize potential WNV
infection in humans and enhance active epidemiological surveillance in birds,
humans, and other mammals before it becomes a more significant public health
problem in South America.
