-
Integrating indicator-based and event-based surveillance data for risk mapping of West Nile virus, Europe, 2006 to 2021
- Kyla Serres1,2,* , Diana Erazo1,* , Garance Despréaux1,* , María F Vincenti-González1 , Wim Van Bortel3,4 , Elena Arsevska2,** , Simon Dellicour1,5,**
-
View Affiliations Hide AffiliationsAffiliations: 1 Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Brussels, Belgium 2 Joint Research Unit Animal, Health, Territories, Risks, Ecosystems (UMR ASTRE), French Agricultural Research Centre for International Development (CIRAD), National Research Institute for Agriculture, Food and Environment (INRAE), Montpellier, France 3 Unit of Entomology, Department of Biomedical Sciences, Institute of Tropical Medicine, Antwerp, Belgium 4 Outbreak Research team, Department of Biomedical Sciences, Institute of Tropical Medicine, Antwerp, Belgium 5 Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory for Clinical and Epidemiological Virology, KU Leuven, Leuven, Belgium * These authors contributed equally to the work and share first authorship. ** These authors contributed equally to the work and share last authorship.Kyla Serreskyla.serres ulb.be
-
View Citation Hide Citation
Citation style for this article: Serres Kyla, Erazo Diana, Despréaux Garance, Vincenti-González María F, Van Bortel Wim, Arsevska Elena, Dellicour Simon. Integrating indicator-based and event-based surveillance data for risk mapping of West Nile virus, Europe, 2006 to 2021. Euro Surveill. 2024;29(44):pii=2400084. https://doi.org/10.2807/1560-7917.ES.2024.29.44.2400084 Received: 06 Feb 2024; Accepted: 02 Jun 2024
Abstract
West Nile virus (WNV) has an enzootic cycle between birds and mosquitoes, humans being incidental dead-end hosts. Circulation of WNV is an increasing public health threat in Europe. While detection of WNV is notifiable in humans and animals in the European Union, surveillance based on human case numbers presents some limitations, including reporting delays.
We aimed to perform risk mapping of WNV circulation leading to human infections in Europe by integrating two types of surveillance systems: indicator-based and event-based surveillance.
For indicator-based surveillance, we used data on human case numbers reported to the European Centre for Disease Prevention and Control (ECDC), and for event-based data, we retrieved information from news articles collected through an automated biosurveillance platform. In addition to these data sources, we also used environmental data to train ecological niche models to map the risk of local WNV circulation leading to human infections.
The ecological niche models based on both types of surveillance data highlighted new areas potentially at risk of WNV infection in humans, particularly in Spain, Italy, France and Greece.
Although event-based surveillance data do not constitute confirmed occurrence records, integrating both indicator-based and event-based surveillance data proved useful. These results underscore the potential for a more proactive and comprehensive strategy in managing the threat of WNV in Europe by combining indicator- and event-based and environmental data for effective surveillance and public health response.
Article metrics loading...
Full text loading...
References
-
Semenza JC, Zeller H. Integrated surveillance for prevention and control of emerging vector-borne diseases in Europe. Euro Surveill. 2014;19(13):20757. https://doi.org/10.2807/1560-7917.ES2014.19.13.20757 PMID: 24721535
-
European Centre for Disease Prevention and Control (ECDC). West Nile virus infection - Annual Epidemiological Report for 2019. Stockholm: ECDC; 19 Mar 2021. Available from: https://www.ecdc.europa.eu/en/publications-data/west-nile-virus-infection-annual-epidemiological-report-2019
-
European Centre for Disease Prevention and Control (ECDC). Autochthonous transmission of chikungunya virus in mainland EU/EEA, 2007–present. Stockholm: ECDC; 13 Aug 2024. Available from: https://www.ecdc.europa.eu/en/infectious-disease-topics/z-disease-list/chikungunya-virus-disease/surveillance-threats-and
-
European Centre for Disease Prevention and Control (ECDC). Local transmission of dengue virus in mainland EU/EEA, 2010-present. Stockholm: ECDC; 18 Oct 2024. Available from: https://www.ecdc.europa.eu/en/all-topics-z/dengue/surveillance-and-disease-data/autochthonous-transmission-dengue-virus-eueea
-
Calistri P, Giovannini A, Hubalek Z, Ionescu A, Monaco F, Savini G, et al. Epidemiology of west nile in europe and in the mediterranean basin. Open Virol J. 2010;4(1):29-37. https://doi.org/10.2174/1874357901004010029 PMID: 20517490
-
Hayes EB, Komar N, Nasci RS, Montgomery SP, O’Leary DR, Campbell GL. Epidemiology and transmission dynamics of West Nile virus disease. Emerg Infect Dis. 2005;11(8):1167-73. https://doi.org/10.3201/eid1108.050289a PMID: 16102302
-
Castillo-Olivares J, Wood J. West Nile virus infection of horses. Vet Res. 2004;35(4):467-83. https://doi.org/10.1051/vetres:2004022 PMID: 15236677
-
Petersen LR, Brault AC, Nasci RS. West Nile virus: review of the literature. JAMA. 2013;310(3):308-15. https://doi.org/10.1001/jama.2013.8042 PMID: 23860989
-
Tsai TF, Popovici F, Cernescu C, Campbell GL, Nedelcu NI. West Nile encephalitis epidemic in southeastern Romania. Lancet. 1998;352(9130):767-71. https://doi.org/10.1016/S0140-6736(98)03538-7 PMID: 9737281
-
Bárdos V, Adamcová J, Dedei S, Gjini N, Rosický B, Simková A. Neutralizing antibodies against some neurotropic viruses determined in human sera in Albania. J Hyg Epidemiol Microbiol Immunol. 1959;3:277-82. PMID: 13796704
-
Bakonyi T, Haussig JM. West Nile virus keeps on moving up in Europe. Euro Surveill. 2020;25(46):2001938. https://doi.org/10.2807/1560-7917.ES.2020.25.46.2001938 PMID: 33213684
-
D’Amore C, Grimaldi P, Ascione T, Conti V, Sellitto C, Franci G, et al. West Nile virus diffusion in temperate regions and climate change. A systematic review. Infez Med. 2023;31(1):20-30. PMID: 36908379
-
Rocklöv J, Dubrow R. Climate change: an enduring challenge for vector-borne disease prevention and control. Nat Immunol. 2020;21(5):479-83. https://doi.org/10.1038/s41590-020-0648-y PMID: 32313242
-
Ostfeld RS. Biodiversity loss and the ecology of infectious disease. Lancet Planet Health. 2017;1(1):e2-3. https://doi.org/10.1016/S2542-5196(17)30010-4 PMID: 29851590
-
Paquet C, Coulombier D, Kaiser R, Ciotti M. Epidemic intelligence: a new framework for strengthening disease surveillance in Europe. Euro Surveill. 2006;11(12):212-4. https://doi.org/10.2807/esm.11.12.00665-en PMID: 17370970
-
Dub T, Mäkelä H, Van Kleef E, Leblond A, Mercier A, Hénaux V, et al. Epidemic intelligence activities among national public and animal health agencies: a European cross-sectional study. BMC Public Health. 2023;23(1):1488. https://doi.org/10.1186/s12889-023-16396-y PMID: 37542208
-
Jourdain F, Roiz D, de Valk H, Noël H, L’Ambert G, Franke F, et al. From importation to autochthonous transmission: Drivers of chikungunya and dengue emergence in a temperate area. PLoS Negl Trop Dis. 2020;14(5):e0008320. https://doi.org/10.1371/journal.pntd.0008320 PMID: 32392224
-
Arsevska E, Valentin S, Rabatel J, de Goër de Hervé J, Falala S, Lancelot R, et al. Web monitoring of emerging animal infectious diseases integrated in the French Animal Health Epidemic Intelligence System. PLoS One. 2018;13(8):e0199960. https://doi.org/10.1371/journal.pone.0199960 PMID: 30074992
-
Carrion M, Madoff LC. ProMED-mail: 22 years of digital surveillance of emerging infectious diseases. Int Health. 2017;9(3):177-83. https://doi.org/10.1093/inthealth/ihx014 PMID: 28582558
-
Freifeld CC, Mandl KD, Reis BY, Brownstein JS. HealthMap: global infectious disease monitoring through automated classification and visualization of Internet media reports. J Am Med Inform Assoc. 2008;15(2):150-7. https://doi.org/10.1197/jamia.M2544 PMID: 18096908
-
Valentin S, Arsevska E, Falala S, de Goër J, Lancelot R, Mercier A, et al. PADI-web: A multilingual event-based surveillance system for monitoring animal infectious diseases. Comput Electron Agric. 2020;169:105163. https://doi.org/10.1016/j.compag.2019.105163
-
Salami D, Sousa CA, Martins MDRO, Capinha C. Predicting dengue importation into Europe, using machine learning and model-agnostic methods. Sci Rep. 2020;10(1):9689. https://doi.org/10.1038/s41598-020-66650-1 PMID: 32546771
-
Watts MJ, Sarto I Monteys V, Mortyn PG, Kotsila P. The rise of West Nile Virus in Southern and Southeastern Europe: A spatial-temporal analysis investigating the combined effects of climate, land use and economic changes. One Health. 2021;13:100315. https://doi.org/10.1016/j.onehlt.2021.100315 PMID: 34485672
-
Farooq Z, Rocklöv J, Wallin J, Abiri N, Sewe MO, Sjödin H, et al. Artificial intelligence to predict West Nile virus outbreaks with eco-climatic drivers. Lancet Reg Health Eur. 2022;17:100370. https://doi.org/10.1016/j.lanepe.2022.100370 PMID: 35373173
-
European Centre for Disease Prevention and Control (ECDC). Long-term surveillance framework 2021–2027. Stockholm: ECDC; 4 Apr 2023. Available: https://www.ecdc.europa.eu/en/publications-data/long-term-surveillance-framework-2021-2027
-
Eurostat. NUTS- Nomenclature of Territorial Units for Statistics. Luxemburg: Eurostat; 2021. Available from: https://ec.europa.eu/eurostat
-
European Centre for Disease Prevention and Control (ECDC). West Nile virus - human cases compared to previous seasons, 23 August 2023. Stockholm: ECDC; 25 Aug 2023. Available from: https://www.ecdc.europa.eu/en/publications-data/west-nile-virus-human-cases-compared-previous-seasons-23-august-2023
-
Hernangómez D. giscoR: Download Map Data from GISCO API - Eurostat. Geneve: Zenodo; 28Aug2024. Available from: https://zenodo.org/doi/10.5281/zenodo.4317946
-
Giesen C, Herrador Z, Fernandez-Martinez B, Figuerola J, Gangoso L, Vazquez A, et al. A systematic review of environmental factors related to WNV circulation in European and Mediterranean countries. One Health. 2023;16:100478. https://doi.org/10.1016/j.onehlt.2022.100478 PMID: 37363246
-
Wint GRW, Balenghien T, Berriatua E, Braks M, Marsboom C, Medlock J, et al. VectorNet: collaborative mapping of arthropod disease vectors in Europe and surrounding areas since 2010. Euro Surveill. 2023;28(26):2200666. https://doi.org/10.2807/1560-7917.ES.2023.28.26.2200666 PMID: 37382886
-
Elith J, Leathwick JR, Hastie T. A working guide to boosted regression trees. J Anim Ecol. 2008;77(4):802-13. https://doi.org/10.1111/j.1365-2656.2008.01390.x PMID: 18397250
-
Colin B, Clifford S, Wu P, Rathmanner S, Mengersen K. Using boosted regression trees and remotely sensed data to drive decision-making. Open J Stat. 2017;7(5):859-75. https://doi.org/10.4236/ojs.2017.75061
-
Erazo D, Grant L, Ghisbain G, Marini G, Colón-González FJ, Wint W, et al. Contribution of climate change to the spatial expansion of West Nile virus in Europe. Nat Commun. 2024;15(1):1196. https://doi.org/10.1038/s41467-024-45290-3 PMID: 38331945
-
Randin CF, Dirnböck T, Dullinger S, Zimmermann NE, Zappa M, Guisan A. Are niche-based species distribution models transferable in space? J Biogeogr. 2006;33(10):1689-703. https://doi.org/10.1111/j.1365-2699.2006.01466.x
-
Valavi R, Elith J, Lahoz-Monfort JJ, Guillera-Arroita G. blockCV: An r package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models. Methods Ecol Evol. 2019;10(2):225-32. https://doi.org/10.1111/2041-210X.13107
-
Lobo JM, Jiménez-Valverde A, Real R. AUC: a misleading measure of the performance of predictive distribution models. Glob Ecol Biogeogr. 2008;17(2):145-51. https://doi.org/10.1111/j.1466-8238.2007.00358.x
-
Jiménez-Valverde A. Threshold-dependence as a desirable attribute for discrimination assessment: implications for the evaluation of species distribution models. Biodivers Conserv. 2014;23(2):369-85. https://doi.org/10.1007/s10531-013-0606-1
-
Jiménez-Valverde A. Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling. Glob Ecol Biogeogr. 2012;21(4):498-507. https://doi.org/10.1111/j.1466-8238.2011.00683.x
-
Ghisbain G, Thiery W, Massonnet F, Erazo D, Rasmont P, Michez D, et al. Projected decline in European bumblebee populations in the twenty-first century. Nature. 2024;628(8007):337-41. https://doi.org/10.1038/s41586-023-06471-0 PMID: 37704726
-
Leroy B, Delsol R, Hugueny B, Meynard CN, Barhoumi C, Barbet-Massin M, et al. Without quality presence–absence data, discrimination metrics such as TSS can be misleading measures of model performance. J Biogeogr. 2018;45(9):1994-2002. https://doi.org/10.1111/jbi.13402
-
Sørensen T. A Method of Establishing Groups of Equal Amplitude in Plant Sociology Based on Similarity of Species Content and Its Application to Analyses of the Vegetation on Danish Commons. Munksgaard in Komm.; 1948. Available from: https://books.google.be/books?id=rpS8GAAACAAJ
-
Li W, Guo Q. How to assess the prediction accuracy of species presence–absence models without absence data? Ecography. 2013;36(7):788-99. https://doi.org/10.1111/j.1600-0587.2013.07585.x
-
European Centre for Disease Prevention and Control (ECDC). Epidemiological update: West Nile virus transmission season in Europe, 2021. Stockholm: ECDC; 24 Mar 2022. Available from: https://www.ecdc.europa.eu/en/news-events/epidemiological-update-west-nile-virus-transmission-season-europe-2021
-
Farooq Z, Sjödin H, Semenza JC, Tozan Y, Sewe MO, Wallin J, et al. European projections of West Nile virus transmission under climate change scenarios. One Health. 2023;16:100509. https://doi.org/10.1016/j.onehlt.2023.100509 PMID: 37363233
-
García-Carrasco J-M, Muñoz A-R, Olivero J, Segura M, Real R. Predicting the spatio-temporal spread of West Nile virus in Europe. PLoS Negl Trop Dis. 2021;15(1):e0009022. https://doi.org/10.1371/journal.pntd.0009022 PMID: 33411739
-
García-Carrasco JM, Muñoz AR, Real R. Anticipating the locations in Europe of high-risk areas for West Nile virus outbreaks in 2021. Zoonoses Public Health. 2021;68(8):982-6. https://doi.org/10.1111/zph.12877 PMID: 34242480
-
Di Pol G, Crotta M, Taylor RA. Modelling the temperature suitability for the risk of West Nile Virus establishment in European Culex pipiens populations. Transbound Emerg Dis. 2022;69(5):e1787-99. https://doi.org/10.1111/tbed.14513 PMID: 35304820
-
Moser SK, Barnard M, Frantz RM, Spencer JA, Rodarte KA, Crooker IK, et al. Scoping review of Culex mosquito life history trait heterogeneity in response to temperature. Parasit Vectors. 2023;16(1):200. https://doi.org/10.1186/s13071-023-05792-3 PMID: 37316915
-
Drakou K, Nikolaou T, Vasquez M, Petric D, Michaelakis A, Kapranas A, et al. The effect of weather variables on mosquito activity: a snapshot of the main point of entry of Cyprus. Int J Environ Res Public Health. 2020;17(4):1403. https://doi.org/10.3390/ijerph17041403 PMID: 32098137
-
Roiz D, Ruiz S, Soriguer R, Figuerola J. Climatic effects on mosquito abundance in Mediterranean wetlands. Parasit Vectors. 2014;7(1):333. https://doi.org/10.1186/1756-3305-7-333 PMID: 25030527
-
Ruiz MO, Walker ED, Foster ES, Haramis LD, Kitron UD. Association of West Nile virus illness and urban landscapes in Chicago and Detroit. Int J Health Geogr. 2007;6(1):10. https://doi.org/10.1186/1476-072X-6-10 PMID: 17352825
-
Fanelli A, Schnitzler JC, De Nardi M, Donachie A, Capua I, Lanave G, et al. Epidemic intelligence data of Crimean-Congo haemorrhagic fever, European Region, 2012 to 2022: a new opportunity for risk mapping of neglected diseases. Euro Surveill. 2023;28(16):2200542. https://doi.org/10.2807/1560-7917.ES.2023.28.16.2200542 PMID: 37078883
-
Badker R, Miller K, Pardee C, Oppenheim B, Stephenson N, Ash B, et al. Challenges in reported COVID-19 data: best practices and recommendations for future epidemics. BMJ Glob Health. 2021;6(5):e005542. https://doi.org/10.1136/bmjgh-2021-005542 PMID: 33958393
Data & Media loading...