Early detection of West Nile virus in France: quantitative assessment of syndromic surveillance system using nervous signs in horses.
Abstract: West Nile virus (WNV) is a growing public health concern in Europe and there is a need to develop more efficient early detection systems. Nervous signs in horses are considered to be an early indicator of WNV and, using them in a syndromic surveillance system, might be relevant. In our study, we assessed whether or not data collected by the passive French surveillance system for the surveillance of equine diseases can be used routinely for the detection of WNV. We tested several pre-processing methods and detection algorithms based on regression. We evaluated system performances using simulated and authentic data and compared them to those of the surveillance system currently in place. Our results show that the current detection algorithm provided similar performances to those tested using simulated and real data. However, regression models can be easily and better adapted to surveillance objectives. The detection performances obtained were compatible with the early detection of WNV outbreaks in France (i.e. sensitivity 98%, specificity >94%, timeliness 2·5 weeks and around four false alarms per year) but further work is needed to determine the most suitable alarm threshold for WNV surveillance in France using cost-efficiency analysis.
Publication Date: 2016-12-12 PubMed ID: 27938434PubMed Central: PMC9507807DOI: 10.1017/S0950268816002946Google Scholar: Lookup
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- Evaluation Study
- Journal Article
- Comparative Study
- Diagnosis
- Disease control
- Disease Diagnosis
- Disease Management
- Disease Outbreaks
- Disease Prevalence
- Disease Surveillance
- Disease Treatment
- Epidemiology
- Equine Diseases
- Equine Health
- Horses
- Infectious Disease
- Nervous System
- Public Health
- Regression Analysis
- Veterinary Medicine
- Veterinary Research
- Veterinary Science
- West Nile Virus
Summary
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The research evaluates the French surveillance system for equine diseases in early detection of West Nile virus (WNV), using horses’ nervous signs, and suggests that regression models yield better surveillance results.
Objectives of the Research
- To evaluate the French surveillance system for detecting West Nile virus (WNV).
- The focus is on nervous symptoms in horses, as these are considered early indicators of WNV.
- To compare the effectiveness of various pre-processing methods and detection algorithms, particularly those based on regression.
- To assess if the data gathered by the French equine diseases surveillance system can be used routinely for WNV detection.
Methods Employed in the Research
- For the assessment, both simulated data and real-world data were used.
- Different pre-processing methods were tested along with detection algorithms based on regression.
- The results were compared with those of the currently existing surveillance system.
Findings of the Study
- The study found that the algorithm currently in use for detection provided similar performances to those tested using simulated and real data.
- However, it was seen that regression models can be adapted more easily and better to meet surveillance requirements.
- The research revealed that the sensitivity was at 98% and specificity was over 94% with timeliness around 2.5 weeks and about four false alarms per year, which signifies that the detection performances can support early detection of WNV outbreaks in France.
Implications and Suggestions for Future Research
- Although the initial results were promising, the study indicates the need for additional research to determine the most suitable alarm threshold for WNV surveillance in France.
- This could be achieved through a cost-efficiency analysis which would help in enhancing the effectiveness of the surveillance system and reduce the number of false alarms.
Cite This Article
APA
Faverjon C, Vial F, Andersson MG, Lecollinet S, Leblond A.
(2016).
Early detection of West Nile virus in France: quantitative assessment of syndromic surveillance system using nervous signs in horses.
Epidemiol Infect, 145(5), 1044-1057.
https://doi.org/10.1017/S0950268816002946 Publication
Researcher Affiliations
- INRA UR0346 Animal Epidemiology,VetagroSup, Marcy l'Etoile,France.
- Epi-Connect, Djupdalsvägen Skogås,Sweden.
- Department of Chemistry,Environment and Feed Hygiene. The National Veterinary Institute,Uppsala,Sweden.
- UPE, ANSES,Animal Health Laboratory,UMR1161 Virologie, INRA, ANSES, ENVA,Maisons-Alfort,France.
- Réseau d'EpidémioSurveillance en Pathologie Equine (RESPE),Caen,France.
MeSH Terms
- Animals
- France / epidemiology
- Horse Diseases / etiology
- Horse Diseases / pathology
- Horses
- Nervous System Diseases / epidemiology
- Nervous System Diseases / pathology
- Nervous System Diseases / veterinary
- Sensitivity and Specificity
- Sentinel Surveillance
- West Nile Fever / epidemiology
- West Nile Fever / pathology
- West Nile Fever / veterinary
- West Nile virus / isolation & purification
Conflict of Interest Statement
None
References
This article includes 45 references
- Campbell GL, Marfin AA, Lanciotti RS, Gubler DJ. West Nile virus.. Lancet Infect Dis 2002 Sep;2(9):519-29.
- Castillo-Olivares J, Wood J. West Nile virus infection of horses.. Vet Res 2004 Jul-Aug;35(4):467-83.
- Smithburn KC. A neurotropic virus isolated from the blood of a native of Uganda. American Journal of Tropical Medicine and Hygiene 1940; s1.–20: 471–492.
- Ozdenerol E, Taff GN, Akkus C. Exploring the spatio-temporal dynamics of reservoir hosts, vectors, and human hosts of West Nile virus: a review of the recent literature.. Int J Environ Res Public Health 2013 Oct 25;10(11):5399-432.
- Calistri P, Giovannini A, Hubalek Z, Ionescu A, Monaco F, Savini G, Lelli R. Epidemiology of west nile in europe and in the mediterranean basin.. Open Virol J 2010 Apr 22;4:29-37.
- Di Sabatino D, Bruno R, Sauro F, Danzetta ML, Cito F, Iannetti S, Narcisi V, De Massis F, Calistri P. Epidemiology of West Nile disease in Europe and in the Mediterranean Basin from 2009 to 2013.. Biomed Res Int 2014;2014:907852.
- Bakonyi T, Ivanics E, Erdélyi K, Ursu K, Ferenczi E, Weissenböck H, Nowotny N. Lineage 1 and 2 strains of encephalitic West Nile virus, central Europe.. Emerg Infect Dis 2006 Apr;12(4):618-23.
- Calzolari M, Monaco F, Montarsi F, Bonilauri P, Ravagnan S, Bellini R, Cattoli G, Cordioli P, Cazzin S, Pinoni C, Marini V, Natalini S, Goffredo M, Angelini P, Russo F, Dottori M, Capell G, Savini G. New incursions of West Nile virus lineage 2 in Italy in 2013: the value of the entomological surveillance as early warning system.. Vet Ital 2013 Jul-Sep;49(3):315-9.
- Hernández-Triana LM, Jeffries CL, Mansfield KL, Carnell G, Fooks AR, Johnson N. Emergence of west nile virus lineage 2 in europe: a review on the introduction and spread of a mosquito-borne disease.. Front Public Health 2014;2:271.
- Danis K, Papa A, Theocharopoulos G, Dougas G, Athanasiou M, Detsis M, Baka A, Lytras T, Mellou K, Bonovas S, Panagiotopoulos T. Outbreak of West Nile virus infection in Greece, 2010.. Emerg Infect Dis 2011 Oct;17(10):1868-72.
- Del Giudice P, Schuffenecker I, Vandenbos F, Counillon E, Zellet H. Human West Nile virus, France.. Emerg Infect Dis 2004 Oct;10(10):1885-6.
- ECDC. Human and equine West Nile virus infections in France, August-September 2003 (http://www.eurosurveillance.org/ViewArticle.aspx?ArticleId=2312). Accessed 9 September 2015.
- Murgue B, Murri S, Zientara S, Durand B, Durand JP, Zeller H. West Nile outbreak in horses in southern France, 2000: the return after 35 years.. Emerg Infect Dis 2001 Jul-Aug;7(4):692-6.
- Anon. Programme de surveillance vétérinaire de la fièvre West-Nile. Direction générale de l'alimentation 2007; Note de service DGAL/SDSPA/N2007-–136 [in French] (http://agriculture.gouv.fr/sites/minagri/files/documents//dgaln20078136z.pdf) Accessed 7 july 2016.
- Bahuon C, Marcillaud-Pitel C, Bournez L, Leblond A, Beck C, Hars J, Leparc-Goffart I, L'Ambert G, Paty MC, Cavalerie L, Daix C, Tritz P, Durand B, Zientara S, Lecollinet S. West Nile virus epizootics in the Camargue (France) in 2015 and reinforcement of surveillance and control networks.. Rev Sci Tech 2016 Dec;35(3):811-824.
- Doherr MG, Audigé L. Monitoring and surveillance for rare health-related events: a review from the veterinary perspective.. Philos Trans R Soc Lond B Biol Sci 2001 Jul 29;356(1411):1097-106.
- Triple S Project. Guideline for designing and implementing a syndromic surveillance system. 2011 (http://www.syndromicsurveillance.eu/Triple-S_guidelines.pdf). Accessed 7 July 2016.
- Leblond A, Hendrikx P, Sabatier P. West Nile virus outbreak detection using syndromic monitoring in horses.. Vector Borne Zoonotic Dis 2007 Fall;7(3):403-10.
- Saegerman C, Alba-Casals A, García-Bocanegra I, Dal Pozzo F, van Galen G. Clinical Sentinel Surveillance of Equine West Nile Fever, Spain.. Transbound Emerg Dis 2016 Apr;63(2):184-93.
- Chevalier V, Lecollinet S, Durand B. West Nile virus in Europe: a comparison of surveillance system designs in a changing epidemiological context.. Vector Borne Zoonotic Dis 2011 Aug;11(8):1085-91.
- Léon A, Fortier G, Fortier C, Freymuth F, Tapprest J, Leclercq R, Pronost S. Detection of equine herpesviruses in aborted foetuses by consensus PCR.. Vet Microbiol 2008 Jan 1;126(1-3):20-9.
- Tsui FC, Wagner MM, Dato V, Chang CC. Value of ICD-9 coded chief complaints for detection of epidemics.. Proc AMIA Symp 2001;:711-5.
- Serfling RE. Methods for current statistical analysis of excess pneumonia-influenza deaths.. Public Health Rep (1896) 1963 Jun;78(6):494-506.
- Dórea FC, Revie CW, McEwen BJ, McNab WB, Kelton D, Sanchez J. Retrospective time series analysis of veterinary laboratory data: preparing a historical baseline for cluster detection in syndromic surveillance.. Prev Vet Med 2013 May 1;109(3-4):219-27.
- Lotze T, Murphy S, Shmueli G. Implementation and comparison of preprocessing methods for biosurveillance data. Advances in Diseases Surveillance 2008; 6: 1–20.
- Bozdogan H. Model selection and Akaike's information criterion (AIC): the general theory and its analytical extensions. Psychometrika 1987; 52: 345–370.
- Kalekar PS. Time series forecasting using Holt-Winters exponential smoothing. Kanwal Rekhi School of Information Technology 2004; 4329008: 1–13.
- Box GEP, Jenkins GM, Reinsel GC. Time Series Analysis: Forecasting and Control, 4th edn. Hoboken, NJ: Wiley, 2008.
- Chai T, Draxler RR. Root mean square error (RMSE) or mean absolute error (MAE)? – arguments against avoiding RMSE in the literature. Geoscience Model Development 2014; 7: 1247–1250.
- Autorino GL, Battisti A, Deubel V, Ferrari G, Forletta R, Giovannini A, Lelli R, Murri S, Scicluna MT. West Nile virus epidemic in horses, Tuscany region, Italy.. Emerg Infect Dis 2002 Dec;8(12):1372-8.
- Kutasi O, Bakonyi T, Lecollinet S, Biksi I, Ferenczi E, Bahuon C, Sardi S, Zientara S, Szenci O. Equine encephalomyelitis outbreak caused by a genetic lineage 2 West Nile virus in Hungary.. J Vet Intern Med 2011 May-Jun;25(3):586-91.
- Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve.. Radiology 1982 Apr;143(1):29-36.
- R Development Core Team. R: A language and environment for statistical computing. version 3.0.2. Vienna, Austria: R Foundation for Statistical Computing, 2008.
- Ripley B. MASS: Support functions and datasets for Venables and Ripley's MASS, 2015. version R package version 7.3-–5.
- Hyndman R. forecast: forecasting functions for time series and linear models, 2016. version R package version 7.1.
- Delignette-Muller M-L. fitdistrplus: help to fit of a parametric distribution to non-censored or censored data, 2015. version R package version 1.0-6.
- Jurasinski G. flux: flux rate calculation from dynamic closed chamber measurements, 2014. version R package version 0.3-0.
- Lunn DP, Davis-Poynter N, Flaminio MJ, Horohov DW, Osterrieder K, Pusterla N, Townsend HG. Equine herpesvirus-1 consensus statement.. J Vet Intern Med 2009 May-Jun;23(3):450-61.
- Chaintoutis SC, Dovas CI, Papanastassopoulou M, Gewehr S, Danis K, Beck C, Lecollinet S, Antalis V, Kalaitzopoulou S, Panagiotopoulos T, Mourelatos S, Zientara S, Papadopoulos O. Evaluation of a West Nile virus surveillance and early warning system in Greece, based on domestic pigeons.. Comp Immunol Microbiol Infect Dis 2014 Mar;37(2):131-41.
- Eidson M, Kramer L, Stone W, Hagiwara Y, Schmit K. Dead bird surveillance as an early warning system for West Nile virus.. Emerg Infect Dis 2001 Jul-Aug;7(4):631-5.
- Johnson GD, Eidson M, Schmit K, Ellis A, Kulldorff M. Geographic prediction of human onset of West Nile virus using dead crow clusters: an evaluation of year 2002 data in New York State.. Am J Epidemiol 2006 Jan 15;163(2):171-80.
- Mostashari F, Kulldorff M, Hartman JJ, Miller JR, Kulasekera V. Dead bird clusters as an early warning system for West Nile virus activity.. Emerg Infect Dis 2003 Jun;9(6):641-6.
- Veksler A, Eidson M, Zurbenko I. Assessment of methods for prediction of human West Nile virus (WNV) disease from WNV-infected dead birds.. Emerg Themes Epidemiol 2009 Jun 5;6:4.
- Faverjon C, Andersson MG, Decors A, Tapprest J, Tritz P, Sandoz A, Kutasi O, Sala C, Leblond A. Evaluation of a Multivariate Syndromic Surveillance System for West Nile Virus.. Vector Borne Zoonotic Dis 2016 Jun;16(6):382-90.
- Leblond A, Lecollinet S. Clinical screening of horses and early warning for West Nile virus. Equine Veterinary Education Published online: 28 February 2016.
Citations
This article has been cited 10 times.- Cavalleri JV, Korbacska-Kutasi O, Leblond A, Paillot R, Pusterla N, Steinmann E, Tomlinson J. European College of Equine Internal Medicine consensus statement on equine flaviviridae infections in Europe. J Vet Intern Med 2022 Nov;36(6):1858-1871.
- Özçelik R, Graubner C, Remy-Wohlfender F, Dürr S, Faverjon C. Evaluating 5.5 Years of Equinella: A Veterinary-Based Voluntary Infectious Disease Surveillance System of Equines in Switzerland. Front Vet Sci 2020;7:327.
- Cameron AR, Meyer A, Faverjon C, Mackenzie C. Quantification of the sensitivity of early detection surveillance. Transbound Emerg Dis 2020 Nov;67(6):2532-2543.
- Faverjon C, Schärrer S, Hadorn DC, Berezowski J. Simulation Based Evaluation of Time Series for Syndromic Surveillance of Cattle in Switzerland. Front Vet Sci 2019;6:389.
- Otten ND, Toft N, Thomsen PT, Houe H. Evaluation of the performance of register data as indicators for dairy herds with high lameness prevalence. Acta Vet Scand 2019 Oct 21;61(1):49.
- Nistor P, Stanga L, Chirila A, Iorgoni V, Gligor A, Ciresan A, Popa I, Florea B, Imre M, Cocioba V, Iancu I, Degi J, Herman V. Seroprevalence and Passive Clinical Surveillance of West Nile Virus in Horses from Ecological High-Risk Areas in Western Romania: Exploratory Findings from a Cross-Sectional Study. Microorganisms 2025 Aug 16;13(8).
- Laidoudi Y, Durand G, Watier-Grillot S, Dessimoulie AS, Labarde C, Normand T, Andréo V, Guérin P, Grard G, Davoust B. Evidence of Antibodies against the West Nile Virus and the Usutu Virus in Dogs and Horses from the Southeast of France. Transbound Emerg Dis 2023;2023:8779723.
- Oliveira VHS, Dórea FC, Dean KR, Bang Jensen B. Exploring Options for Syndromic Surveillance in Aquaculture: Outbreak Detection of Salmon Pancreas Disease Using Production Data from Norwegian Farms. Transbound Emerg Dis 2024;2024:9861677.
- Carrasco L, Utrilla MJ, Fuentes-Romero B, Fernandez-Novo A, Martin-Maldonado B. West Nile Virus: An Update Focusing on Southern Europe. Microorganisms 2024 Dec 18;12(12).
- Schwarz ER, Long MT. Comparison of West Nile Virus Disease in Humans and Horses: Exploiting Similarities for Enhancing Syndromic Surveillance. Viruses 2023 May 24;15(6).
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