ArticleScientific reports2025
Early warning system of the seasonal west nile virus infection risk in humans in northern greece, 2020-2024.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Article
- Human antibodies against West Nile and related orthoflaviviruses.bioRxiv : the preprint server for biology · 2026Article
- Retrospective Analysis and Cross-Validated Forecasting of West Nile Virus Transmission in Italy: Insights from Climate and Surveillance Data.Tropical medicine and infectious disease · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study introduces a novel forecasting tool for West Nile Virus (WNV) risk at the municipal level, based on a previously developed climate-dependent epidemiological model coupled with a data-driven model and field data in an ensemble framework. The modelling system was evaluated at the municipal level in the regions of Central Macedonia (RCM) and Thessaly (RTH) in Greece for the period 2020-2024 and classifies the municipalities according to the risk level of occurrence of a WNV human case (WNVhc) within each forecast season. The modelling system produces seasonal forecasts updated monthly throughout the entire mosquito breeding season (April-September). From the first forecast of the year, WNVhc were correctly predicted at 71% of the infected municipalities in RCM and 60% of the infected municipalities in RTH, percentages that increased by at least 20% after July when surveillance data were incorporated. The observed number of infected humans was within the predicted range, highlighting the part of the region with an expected outbreak. The approach shows that the coupling of a climate-dependent epidemiological model at local level with a data-driven model, incorporating entomological data to predict the risk of WNVhc, can be a useful tool in planning control strategies.
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