CoChat
Sign Up
Cited by 151Mosquito-borne diseases and controlClimate Change and Health ImpactsSpecies Distribution and Climate Change

The DOI matches its Crossref record; 73 of the 73 references it cites were found in a registry or an index.

Contribution of climate change to the spatial expansion of West Nile virus in Europe

Open access
Erazo, Diana, Grant, Luke, Ghisbain, Guillaume, Marini, Giovanni, Colón-González, Felipe J., Wint, William, Rizzoli, Annapaola, Van Bortel, Wim, Vogels, Chantal B. F., Grubaugh, Nathan D., Mengel, Matthias, Frieler, Katja, Thiery, Wim, Dellicour, Simon·2024·Nature Communications
DOI 10.1038/s41467-024-45290-3PMC PMC10853512
Open this paper in CoChat — free accountOpen PDF

Abstract

West Nile virus (WNV) is an emerging mosquito-borne pathogen in Europe where it represents a new public health threat. While climate change has been cited as a potential driver of its spatial expansion on the continent, a formal evaluation of this causal relationship is lacking. Here, we investigate the extent to which WNV spatial expansion in Europe can be attributed to climate change while accounting for other direct human influences such as land-use and human population changes. To this end, we trained ecological niche models to predict the risk of local WNV circulation leading to human cases to then unravel the isolated effect of climate change by comparing factual simulations to a counterfactual based on the same environmental changes but a counterfactual climate where long-term trends have been removed. Our findings demonstrate a notable increase in the area ecologically suitable for WNV circulation during the period 1901-2019, whereas this area remains largely unchanged in a no-climate-change counterfactual. We show that the drastic increase in the human population at risk of exposure is partly due to historical changes in population density, but that climate change has also been a critical driver behind the heightened risk of WNV circulation in Europe.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41467-024-45290-3

Cites

73 of 73 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Distribution of WNV cases reported in the ECDC database until 2019. We here report the density of both “probable” and “confirmed” cases in each administrative area for which there was at least one confirmed case before 2020. Specifically, we report the number of WNV cases by aggregating both “probable” and “confirmed” per km2 (a) or per 100,000 people (b), either considering the NUTS3 or optimised NUTS3 administrative polygons. Darker grey areas correspond to Switzerland and Bosnia & Herzegovina, two countries that are not included in the ECDC WNV surveillance. The administrative areas of those countries were not considered when training the ecological niche models. The United Kingdom is also not included in the ECDC WNV surveillance but was considered when training the models as the country has not reported any WNV case so far (www.nhs.uk).
Fig. 1 Distribution of WNV cases reported in the ECDC database until 2019. We here report the density of both “probable” and “confirmed” cases in each administrative area for which there was at least one confirmed case before 2020. Specifically, we report the number of WNV cases by aggregating both “probable” and “confirmed” per km2 (a) or per 100,000 people (b), either considering the NUTS3 or optimised NUTS3 administrative polygons. Darker grey areas correspond to Switzerland and Bosnia & Herzegovina, two countries that are not included in the ECDC WNV surveillance. The administrative areas of those countries were not considered when training the ecological niche models. The United Kingdom is also not included in the ECDC WNV surveillance but was considered when training the models as the country has not reported any WNV case so far (www.nhs.uk).CC BY · PubMed Central
Fig. 2 Changes in the ecological suitability of West Nile virus (WNV) in Europe. Past and present ecological suitability estimated for each administrative unit are based on both the reconstructions of the historical climate (a) and a counterfactual baseline (b). Ecological suitability values are averaged over the estimates of ten independent BRT models trained on present-day data retrieved from the ISIMIP3a reanalysis dataset GSWP3-W5E5. See Figs. S4–S6 for the estimates of three other ISIMIP3a reanalysis datasets (20CRv3, 20CRv3-ERA5, and 20CRv3-W5E5).
Fig. 2 Changes in the ecological suitability of West Nile virus (WNV) in Europe. Past and present ecological suitability estimated for each administrative unit are based on both the reconstructions of the historical climate (a) and a counterfactual baseline (b). Ecological suitability values are averaged over the estimates of ten independent BRT models trained on present-day data retrieved from the ISIMIP3a reanalysis dataset GSWP3-W5E5. See Figs. S4–S6 for the estimates of three other ISIMIP3a reanalysis datasets (20CRv3, 20CRv3-ERA5, and 20CRv3-W5E5).CC BY · PubMed Central
Fig. 3 Changes in the population at risk of exposure to West Nile virus (WNV) in Europe. Past changes in the population at risk of exposure to WNV have been estimated for both a counterfactual baseline and the observed historical climate data retrieved from four ISIMIP3a reanalysis datasets (GSWP3-W5E5, 20CRv3, 20CRv3-ERA5, and 20CRv3-W5E5) and while considering two different thresholds of ecological suitability (ES) above which an area was considered at risk (0.1 and 0.5).
Fig. 3 Changes in the population at risk of exposure to West Nile virus (WNV) in Europe. Past changes in the population at risk of exposure to WNV have been estimated for both a counterfactual baseline and the observed historical climate data retrieved from four ISIMIP3a reanalysis datasets (GSWP3-W5E5, 20CRv3, 20CRv3-ERA5, and 20CRv3-W5E5) and while considering two different thresholds of ecological suitability (ES) above which an area was considered at risk (0.1 and 0.5).CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 2

  1. IPCC. Annex II: Glossary. In Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2022).
  2. Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J Anim. Ecology 77, 802–813 (2008).

Cited by

Sorted by citations

See all 151

Related

See all

Metadata

Authors
Erazo, D., Grant, L., Ghisbain, G., Marini, G., Colón-González, F. J., Wint, W., Rizzoli, A., Van Bortel, W., Vogels, C. B. F., Grubaugh, N. D., Mengel, M., Frieler, K., Thiery, W., & Dellicour, S.
Affiliations
  • Université Libre de Bruxelles (BE)
  • Vrije Universiteit Brussel (BE)
  • University of Mons (BE)
  • Fondazione Edmund Mach (IT)
  • Yale University (US)
  • Potsdam Institute for Climate Impact Research (DE)
Corresponding author
  • Erazo, Diana
  • Dellicour, Simon
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2024-02-08
Volume
15
Issue
1
Pages
1196-1196
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-024-45290-3
PMC
PMC10853512
OpenAlex
W4391654441
Cited by
151
References
73
Citations by year
2026: 67 · 2025: 62 · 2024: 22
FWCI
62.55
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Lirias (submittedVersion, cc-by)
  • CINECA IRIS Institutional Research Information System (Fondazione Edmund Mach) (submittedVersion, cc-by)
  • ORBi UMONS (submittedVersion, other-oa)
  • Publication Database PIK (Potsdam Institute for Climate Impact Research (PIK)) (submittedVersion, cc-by)
  • VUBIR (Vrije Universiteit Brussel) (submittedVersion)
Funders
  • Georgia Clinical and Translational Science Alliance (UL1TR001863)
  • Vlaams Supercomputer Centrum
  • European Commission (801505, HORIZON2020, 874850)
  • European Centre for Disease Prevention and Control
  • Fonds De La Recherche Scientifique - FNRS (4515.22, G098321N, F.4515.22)
  • Fonds Wetenschappelijk Onderzoek (G098321N)
  • Vlaamse regering
  • National Institutes of Health (UL1TR001863)
  • Departement Economie, Wetenschap en Innovatie
  • National Center for Advancing Translational Sciences (TR001863)
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Mosquito-borne diseases and control
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Climate Change and Health Impacts
  • Physical Sciences › Environmental Science › Ecological Modeling › Species Distribution and Climate Change
Keywords
West Nile virus, climate change, spatial expansion, mosquito-borne diseases, ecological niche modeling, human population density, land use change, emerging infectious diseases, Europe
MeSH terms
Animals, Europe, Humans, Culicidae, West Nile Fever, West Nile virus, Climate Change

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review