ArticleThe Lancet regional health. Europe2022
Artificial intelligence to predict West Nile virus outbreaks with eco-climatic drivers.
Article in The Lancet regional health. Europe, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 4 of them syntheses that pooled it.
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Who cites it
73 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Modelling the risk of West Nile virus infection in seven European countries from published serological and case notification data, 2008 to 2022.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2026Pooled it
- West Nile Virus Infection in Occupational Settings-A Systematic Review.Pathogens (Basel, Switzerland) · 2024Pooled it
- Rehabilitation approaches in West Nile Virus survivors: a systematic review.European journal of physical and rehabilitation medicine · 2024Pooled it
- Harnessing AI for advancing pathogenic microbiology: a bibliometric and topic modeling approach.Frontiers in microbiology · 2024Pooled it
- An Explainable Machine Learning Framework for Predicting West Nile Virus and Uncovering Key Transmission Drivers.GeoHealth · 2026Article
- Advances in mosquito-borne disease surveillance using machine learning.New microbes and new infections · 2026Article
- Evaluating the ability of a thermal biology-informed reproduction number to explain patterns of West Nile incidence in Europe.One health (Amsterdam, Netherlands) · 2026Article
- Predicting the spatio-temporal risk of human tick-borne encephalitis (TBE) in Europe by combining hazard and exposure drivers.One health (Amsterdam, Netherlands) · 2026Article
- Optimizing wild bird fecal surveillance sites for HPAI through risk estimation using machine learning algorithms.One health (Amsterdam, Netherlands) · 2026Article
- Risk assessment of West Nile fever in equines in Europe based on an ecological niche model.BMC veterinary research · 2026Article
- Developing Scenario-Based Strategies for Health, Climate, and Environmental Preparedness: The One Health, One Earth Approach.GeoHealth · 2026Article
- Predicting West Nile virus circulation: a 20-year spatiotemporal study in humans and animals in Spain, 2003 to 2022.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2026Article
- Integrating explainable AI and One Health: a new frontier in combating infectious diseases.EBioMedicine · 2026Review
- Diagnosis and Surveillance of West Nile Virus Infection in Horses: Current Methods, Challenges, and Future Directions.Veterinary sciences · 2026Review
- Review
- Accounting for spatial variation in weather factors predicts spatial variations in Culex quinquefasciatus abundance in the Desert Southwest.Parasites & vectors · 2026Article
- District-Level Dengue Early Warning Prediction System in Bangladesh Using Hybrid Explainable AI and Bayesian Deep Learning.Tropical medicine and infectious disease · 2026Article
- Association of avian biodiversity and West Nile Virus circulation in Culex mosquitoes in Emilia-Romagna, Italy.PLoS neglected tropical diseases · 2026Article
- Role of Artificial Intelligence in Infectious Diseases and Antimicrobial Resistance: A Comprehensive Review on Diagnostic, Treatment, and Prevention Aspects.Saudi medical journal · 2026Review
- The evolution of public health statistical modeling approaches and how to advance their incorporation into modern arboviral surveillance.Journal of medical entomology · 2026Article
13 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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 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).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.