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

Zia Farooq; Joacim Rocklöv; Jonas Wallin; Najmeh Abiri; Maquines Odhiambo Sewe; Henrik Sjödin; Jan C Semenza

doi:10.1016/j.lanepe.2022.100370

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

Lancet Reg Health Eur. 2022 Mar 30:17:100370. doi: 10.1016/j.lanepe.2022.100370. eCollection 2022 Jun.

Authors

Zia Farooq¹, Joacim Rocklöv², Jonas Wallin³, Najmeh Abiri³, Maquines Odhiambo Sewe¹, Henrik Sjödin¹, Jan C Semenza²

Affiliations

¹ Department of public health and clinical medicine, Section of sustainable health, Umeå university, Sweden.
² Heidelberg institute of global health and Interdisciplinary center for scientific computing, University of Heidelberg, Im Neuenheimer Feld 205, Heidelberg 69120, Germany.
³ Department of statistics, Lund university, Sweden.

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) methodology to describe the predictive ability and contribution of different drivers of the emergence and transmission of WNV-outbreaks in Europe, respectively.

Findings: Our model, trained on 2010-2017 data achieved an AUC (area under the receiver operating characteristic curve) score of 0.97 and 0.93 when tested with 2018 and 2019 data, respectively, showing a high discriminatory power to classify a WNV-endemic area. Overall, positive summer/spring temperatures anomalies, lower water availability index (NDWI), and drier winter conditions were found to be the main determinants of WNV-outbreaks across Europe. The climate trends of the preceding year in combination with eco-climatic predictors of the first half of the year provided a robust predictive ability of the entire transmission season ahead of time. For the extraordinary 2018 outbreak year, relatively higher spring temperatures and the abundance of Culex mosquitoes were the strongest predictors, in addition to past climatic trends.

Interpretation: Our AI-based framework can be deployed to trigger rapid and timely alerts for active surveillance and vector control measures in order to intercept an imminent WNV-outbreak in Europe.

Funding: The work was partially funded by the Swedish Research Council FORMAS for the project ARBOPREVENT (grant agreement 2018-05973).

Keywords: Climate adaptation; Culex vectors; Early warning systems; Emerging infectious disease; Europe; Outbreaks management; Preparedness; SHAP; West Nile virus; XGBoost; forecasting.