Artificial intelligence to predict West Nile virus outbreaks with eco-climatic drivers.

ABSTRACT:

Background

In Europe, the frequency, intensity, and geographic range of West Nile virus (WNV)-outbreaks have increased over the past decade, with a 7.2-fold increase in 2018 compared to 2017, and a markedly expanded geographic area compared to 2010. The reasons for this increase and range expansion remain largely unknown due to the complexity of the transmission pathways and underlying disease drivers. In a first, we use advanced artificial intelligence to disentangle the contribution of eco-climatic drivers to WNV-outbreaks across Europe using decade-long (2010-2019) data at high spatial resolution.

Methods

We use a high-performance machine learning classifier, XGBoost (eXtreme gradient boosting) combined with state-of-the-art XAI (eXplainable artificial intelligence) metho

SUBMITTER: Farooq Z 

PROVIDER: S-EPMC8971633 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature