# Integrating indicator-based and event-based surveillance data for risk mapping of West Nile virus, Europe, 2006 to 2021

Kyla Serres, Diana Erazo, Garance Despréaux, María F. Vincenti-González, Wim van Bortel, Elena Arsevska, Simon Dellicour

► To cite this version:

Kyla Serres, Diana Erazo, Garance Despréaux, María F. Vincenti-González, Wim van Bortel, et al. Integrating indicator-based and event-based surveillance data for risk mapping of West Nile virus, Europe, 2006 to 2021. Eurosurveillance, 2024, 29 (44), 10 p. 10.2807/1560-7917.ES.2024.29.44.2400084. hal-05182596

HAL Id: hal-05182596

https://hal.science/hal-05182596v1

Submitted on 23 Jul 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# 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,**

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. Entomology Unit, 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.

Correspondence: Kyla Serres (kyla.serres@ulb.be)

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

Article received on 06 Feb 2024 / Accepted on 02 Jun 2024 / Published on 31 Oct 2024

**Background:** 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. **Aim:** 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. **Methods:** 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. **Results:** 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. **Conclusion:** 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.

## Introduction

Emerging and re-emerging vector-borne diseases (VBD) are currently among prime public health concerns in Europe [1]. Over the past years, Europe has seen an increase in the frequency and geographic expansion of VBD, including infections with West Nile virus (WNV), a virus transmitted by mosquitoes of the *Culex* species complex, as well as dengue virus (DENV) and chikungunya virus (CHIKV), transmitted by *Aedes albopictus* and *A. aegypti* mosquitoes [2-4]. The Flavivirus causing WNV infection is maintained in an enzootic bird-mosquito transmission cycle [5,6]. Mosquitoes acquire the infection by feeding on an infected bird and subsequently transmit the virus to mammals, such as humans and horses that only act as dead-end hosts [7,8]. Since the first findings of WNV in Europe in 1959 [9,10], WNV infection has been notified in humans in several European countries, particularly in central and Mediterranean Europe [11].

Climate and land-use changes, increased mobility of people and animals, biodiversity loss and the introduction of non-endemic pathogens and their vectors into new environments have been identified as drivers of spread of vector-borne pathogens [12-14]. Assessing the risk of local circulation of vector-borne pathogens is a critical public health priority. Identifying areas at risk can help health authorities include appropriate epidemiological and entomological investigations, active case finding and vector control activities in their national surveillance plans.

<figure><img src="image_2.png" /></figure>
## KEY PUBLIC HEALTH MESSAGE

**What did you want to address in this study and why?**

West Nile virus is transmitted via mosquitoes to humans and other animals. Infections of West Nile virus are increasing in Europe, and the disease has a considerable impact on human and animal health. We aimed at mapping areas at risk of local circulation of West Nile virus by combining environmental data with notified human cases of West Nile virus infection and data retrieved from news articles.

**What have we learnt from this study?**

By using environmental data and two types of surveillance data, we identified new areas in southern Europe potentially suitable for local circulation of West Nile virus leading to human cases.

**What are the implications of your findings for public health?**

The new areas identified at risk could be used to identify regions requiring targeted enhanced surveillance and preventive measures by public health authorities. Overall, our findings underscore the usefulness of a multifaceted surveillance approach in disease risk mapping.

Epidemic intelligence is a framework that encompasses activities related to the early identification, verification, analysis and assessment of potential health threats and investigation to recommend control measures. Epidemic intelligence integrates both indicator-based surveillance (IBS) and event-based surveillance (EBS) components [15]. Indicator-based surveillance (IBS) relies on structured, verified indicators such as reported case numbers collected from routine surveillance systems [16]. In event-based surveillance (EBS), on the other hand, information is collected from various data sources which have not been officially validated by health institutions. Epidemic intelligence was introduced in 2006 as a paramount to more effective disease surveillance, risk assessment and collaboration among European public health institutes [15], and today, a combination of IBS and EBS data are the most common data sources in Europe [16]. Although IBS is the epidemic intelligence keystone, it has limitations, such as potential delays between symptom observation, laboratory confirmation and reporting to the competent authorities, as seen with some European VBD outbreaks [17]. Event-based surveillance has been developed to reinforce traditional surveillance and contribute to early detection of outbreaks and risk assessment of potential health threats, notably by broadening the range of data sources, including informal sources such as news articles and social media reports or information from expert networks such as HealthMap (https://www.healthmap.org/en/), ProMED-mail (https://promedmail.org/) and Platform for Automated extraction of animal Disease Information from the web (PADI-web; https://padi-web.cirad.fr/en/) [18-20].

The French Epidemic Intelligence System (FEIS, also known in French as Veille Sanitaire Internationale) created PADI-web [18], an automated biosurveillance system, designed to gather, process and extract epidemiological information from online news sources [21]. This platform collects news articles using custom Really Simple Syndication (RSS) feeds. These feeds search for articles by linking terms related to the disease or syndrome of interest, filtering out extraneous elements like ads, images and comments [18,21]. Furthermore, machine-learning techniques are implemented in PADI-web to identify the pertinent epidemiological features in the text (e.g. disease, affected host, clinical signs, outbreak location coordinates, publication date). Currently, PADI-web collects disease information from Google News due to its global coverage and support for multiple languages.

Many studies on risk mapping of VBD in Europe rely on data collected through an IBS approach. For instance, Salami and colleagues used machine learning and model-agnostic methods to predict the importation risk of DENV into Europe using monthly data on imported cases of dengue from the European Centre for Disease Prevention and Control (ECDC) [22]. Watts and colleagues used data on human cases of WNV infection reported to ECDC and climate, land-use and economic-data to identify risk factors associated with WNV infection in southern and south-eastern Europe [23]. Farooq and colleagues applied a supervised machine learning approach to map the WNV outbreak risk in Europe using an ensemble climate model with ECDC human case data [24].

In this study, we assessed how EBS data could complement traditional IBS disease data to enhance risk

<figure><img src="image_3.png" /></figure>
<figure><img src="image_4.png" /><figcaption>Confirmed autochthonous human cases of West Nile virus infection, Europe, 2006–2021</figcaption></figure>

NUTS3: nomenclature of territorial units for statistics, level 3; WNV: West Nile virus.

Panel A. Original NUTS3 areas are displayed.

Panel B. Optimised NUTS3 used in the ecological niche modelling analyses are displayed.

Areas with at least one confirmed autochthonous case of WNV infection in humans in the indicator-based surveillance are shown in light blue; areas with at least one autochthonous case of WNV infection in humans detected in the event-based surveillance are shown in light green; areas with autochthonous cases of WNV infection in humans detected in both indicator and event-based surveillance are shown in dark blue. Darker grey areas correspond to Switzerland and Bosnia and Herzegovina, countries that are not included in the European Centre for Disease Prevention and Control surveillance of West Nile virus infection.

mapping of VBDs in Europe, using WNV as a case study. While ECDC also uses EBS to monitor global public health threats in Europe [25], its use of EBS differs from our approach, as we focus on applying EBS specifically for risk mapping. In our study, we used both EBS and IBS data to train ecological niche models using a boosted regression trees (BRT) approach to estimate the ecological suitability of WNV given local environmental conditions. These analyses allowed us to investigate the added value of using the integrated IBS and EBS datasets for risk mapping of local WNV circulation leading to human infection.

## Methods

### Data on West Nile virus infection in humans

The IBS data used in our study were retrieved from the European Surveillance System (TESSy) of the ECDC which provided us aggregated data on confirmed human cases of WNV infection from August 2006 to November 2021 at the NUTS (nomenclature of territorial units for statistics) level 3 (NUTS3) [26]. We curated

the dataset to include only autochthonous cases, defined as infections of local origin, i.e. not associated with a recent travel-history. We retrieved the EBS data on WNV infections in humans from January 2006 to December 2021 from the PADI-web platform. In April 2022, we collected news reports identifying outbreak signals of WNV infections up to December 2021.

We defined an outbreak as a location associated with at least one autochthonous case. We set feeds using the keyword 'West Nile virus' and translated the keyword by AGROVOC Multilingual Thesaurus (https://www.fao.org/agrovoc/) to 16 languages spoken in European countries (Austria, Bulgaria, Croatia, Czechia, France, Germany, Greece, Hungary, Italy, the Netherlands, Portugal, Romania, Serbia, Slovakia, Slovenia and Spain) where WNV cases had been previously documented in the WNV epidemiological update of ECDC [27]. We then systematically verified all the news articles identified by PADI-web and only retained those reporting at least one autochthonous human case of WNV infection in Europe. We manually verified that

<figure><img src="image_5.png" /></figure>
the outbreak event coordinates identified by PADI-web corresponded to outbreak sites mentioned in the news article. We excluded the following entries: duplicated locations (i.e. previously identified outbreak location), country-level locations, locations not associated with an outbreak, locations associated with other disease cases and words mistakenly identified as outbreak locations by the system pipeline. Furthermore, any missing outbreak locations not automatically identified by the system pipeline were added by finding the coordinates through Google Earth. The resulting dataset was considered a moderator-verified dataset of WNV occurrence records. These locations were not cross-checked with public health authorities or scientific literature to confirm whether they corresponded to confirmed cases. To ensure consistency in the geographical granularity of the IBS records, we used the R package (R Foundation, Vienna, Austria) 'gisco' version 0.3.5 (https://zenodo.org/doi/10.5281/zenodo.4317946) [28] to match each EBS case or outbreak coordinates to the associated NUTS₃ level (Figure 1).

At the end, we compiled two datasets: one with IBS data and another with IBS and EBS data, EBS data verified by moderators. Based on these datasets, we subsequently categorised NUTS₃ areas as presence or absence sites for WNV infections in humans. Presence was categorised as areas with at least one confirmed or verified autochthonous human case of WNV infection and absences as areas where no autochthonous human cases of WNV infection were observed (Figure 1).

## Environmental data

We used 14 environmental factors documented to influence the risk of circulation of WNV in Europe. These environmental factors were selected from a literature review by Giesen and colleagues [29]: human population density, air temperature (by season), precipitation (by season), relative humidity (by season), soil moisture, leaf index (plant canopies), urban areas, wet crops, pastures, agroforestry areas, forest areas, arable lands, arid shrublands, wetlands and open water areas. Summer covered June–August, autumn September–November, winter December–February and spring March–May. These environmental variables were retrieved from three databases, listed in Supplementary Table S1: (i) the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), which focuses on quantifying climate-related risks under various climate change and socioeconomic conditions, (ii) ERA5, the fifth generation of the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis, offering comprehensive global climate and weather information and (iii) Corine Land Cover, a programme led by the European Commission that provides pan-European land cover data classified into 44 categories.

## Optimisation of the map of administrative areas

The European Union (EU) has developed NUTS, the regional classification which mirrors the territorial administrative divisions of European countries [26]. The NUTS divides each EU country into three levels, based on population size in the region. At the third administrative level, the sizes of the NUTS (NUTS₃) areas can considerably vary across European countries. For instance, NUTS₃ areas in Germany are relatively small and comparable in size to NUTS level 4 areas in some other countries. In our ecological modelling analyses, we created an optimised NUTS map, i.e. a map where the NUTS areas were as uniform in polygon size as possible. This optimised GIS shapefile was specifically crafted for the European network for medical and veterinary entomology (VectorNet) [30], a project co-led by the ECDC and the European Food Safety Authority (EFSA).

## Ecological niche modelling

To map the ecological suitability of Europe for WNV circulation leading to human cases, we implemented a BRT approach [31]. This machine learning algorithm generates a collection of sequentially fitted regression trees that optimise the predictive probability of occurrence given local environmental conditions. In this study, such a predictive probability can be seen as an indication of how suitable an environment is, ranging from 0 to 1, with higher values indicating a greater degree of ecological suitability for local WNV circulation. The BRT approach is able to estimate nonlinear relationships between the response and predictive variables, even in the presence of multicollinearity [31]. However, the algorithm can be computationally expensive and prone to overfitting if not properly regularised [32]. We used the BRT algorithm implemented in the R package 'dismo' version 1.3.9 (https://cran.r-project.org/web/packages/dismo/index.html).

To investigate the usefulness in integrating EBS data for modelling the risk of local WNV circulation, we trained two ecological niche models: one based on IBS data and the other on IBS and EBS data. Specifically, we implemented a Bernoulli BRT approach on the presence and absence data, running 100 independent replicate BRT analyses of both datasets to ensure robustness and accuracy. The 100 resulting maps of ecological suitability were eventually averaged for visualisation purposes. The optimised NUTS₃ areas with at least one confirmed autochthonous case were considered presence locations, the others as potential pseudo-absence locations. We chose to analyse presence and pseudo-absence data rather than incidence data to avoid treating the absolute number of cases as a reliable proxy for prevalence of WNV infection [33].

To investigate the robustness and sensitivity of our results to the sampling intensity of pseudo-absences across the study area, we further trained ecological niche models based on alternative subsets of potential

<figure><img src="image_6.png" /></figure>
FIGURE 2

Comparison of West Nile virus risk maps obtained when analysing indicator-based surveillance data and indicator-based
and event-based surveillance data, Europe, 2006–2021

<figure><img src="image_7.png" /></figure>

EBS: event-based surveillance; ENM: ecological niche modelling; IBS: indicator-based surveillance; NUTS: nomenclature of territorial units for statistics.

Panel A. The map shows the ecological suitability of optimised NUTS level 3 areas for local circulation of West Nile virus, as estimated by ENM analyses based on IBS data.

Panel B. The map shows the ecological suitability of optimised NUTS level 3 areas for local circulation of West Nile virus, as estimated by ENM analyses based on a combination of IBS and EBS data. These maps display the averaged ecological suitability values estimated by 100 replicates of the boosted regression tree analysis.

Panel C. The map highlights the difference in ecological suitability values. Areas coloured in red indicate areas with higher ecological suitability estimates when the model is trained on the combination of IBS and EBS data, while light blue areas represent areas with higher ecological suitability estimates when the models are only trained on IBS data.

pseudo-absences. While the main analyses were
based on pseudo-absences sampled across 50% of the
administrative areas where no WNV presence had been
confirmed, we also considered alternative datasets of
pseudo-absences sampled from 25%, 75% and 100%
of the administrative areas not associated with a pres-
ence record. In each case, we re-trained 100 ecological
niche models. Based on these newly generated mod-
els, we then re-evaluated the difference in ecological
suitability between models incorporating both IBS and
EBS data and those considering only IBS data.

To address spatial autocorrelation and prevent model overfitting, we opted for a spatial instead of the standard cross-validation procedure because the standard one often overestimates the predictive ability of the model when occurrence data exhibit spatial autocorrelation [34]. Specifically, we applied the spatial cross-validation procedure based on block generation introduced by Valavi and colleagues, implemented in the R package 'blockCV' version 3.0.0 [35]: our presences/pseudo-absences dataset was split into five spatial folds determined by this block generation method. We trained the BRT models using the following parameter values: a tree complexity of 5, a learning rate of 0.005 and a step size of 10.

The predictive performance of each BRT model was cal-
culated by computing the area under the receiver oper-
ating characteristic curve (AUC). Values of AUC close
to 1 indicate high model predictive performance, while
AUC values close to or below 0.5 indicate poor predic-
tive performance, presented in Supplementary Table
S2. Because the AUC metric has been criticised in
previous studies due to its dependence on prevalence
[36-39], we also estimated the predictive performance
of the model by calculating the prevalence-pseudoab-
sence-calibrated Sørensen's index (SI<sub>ppc</sub>) [40,41], which
has a minimum value of 0 and a maximum value of 1
representing the highest predictive performance, data
presented in Supplementary Table S2. While our eco-
logical suitability estimates ranged between 0 and 1,
the computation of this index requires binary pres-
ence-absence estimates. For the SI<sub>ppc</sub> computations, we
thus conducted an optimisation process that involved
varying threshold values from 0 to 1 in increments
of 0.01 to select the threshold value maximising the
SI<sub>ppc</sub> [39,42].

To determine the contribution of each environmental
variable, we calculated their relative influence (RI) in
the BRT models, as presented in Supplementary Table
S3. For a specific environmental factor, the RI value
is computed as the number of times this factor is
selected for splitting a tree, weighted by the squared

<figure><img src="image_8.png" /></figure>
improvement to the model resulting from each split and averaged over all trees [31]. Additionally, partial dependence plots or response curves were plotted to show how ecological suitability varies with one specific predictor variable while all others are kept at a constant mean, presented in Supplementary Figure S1. This visualisation technique enhances our understanding of the model behaviour and aids in interpreting the relationships between the different predictors and the response variable [31].

Finally, we evaluated the changes in ecological suit-
ability between the two averaged ecological suitability
maps obtained when solely analysing the IBS data and
when analysing both IBS and EBS data. This involved
subtracting the estimated ecological suitability values
of each optimised NUTS₃ area with the model trained
on both IBS and EBS data from those estimated for
the same area with the model only trained on IBS data
(Figure 2C). Additionally, we calculated the correspond-
ing area at risk by determining the area of each NUTS₃
area associated with an ecological suitability value
that increased by 5%, 10% and 20%, respectively.

Results

From 2006 to 2021, autochthonous human cases of
WNV infection with known places of infection were
reported to ECDC in 182 NUTS3 administrative areas
from 16 countries [43]. In addition, PADI-web detected
706 articles, of which 250 met our inclusion criteria.
From these, the algorithm automatically identified
2,356 locations associated with outbreaks of WNV
infections in humans. After manual verification, 766
locations were confirmed as unique outbreak sites. We
excluded 600 country-level locations, 650 duplicates,
270 unrelated to outbreaks of WNV infection and 70
associated with other VBD. Additionally, we manu-
ally added 192 locations missed by the algorithm. In
total, 958 locations were included, corresponding to 92
NUTS3 areas identified by PADI-web, of which 22 were
not associated with any IBS record (Figure 1).

All our trained ecological niche models showed
good predictive performance as measured by the
AUC and SI<sub>ppc</sub> metrics, displaying values > 0.8, as
presented in Supplementary Table S2. In our study,
climatic factors, namely seasonal temperature, rela-
tive humidity and precipitation, exhibited the high-
est RI (>5%) in both kinds of models, i.e. either
trained on IBS or IBS and EBS data, as presented
in Supplementary Table S3 and Supplementary
Figure S1. Specifically, summer air temperature (RI
IBS=14.99%; RI IBS + EBS=17.93%), summer precipita-
tion (RI IBS = 8.73%; RI IBS+EBS = 8.64%), spring rela-
tive humidity (RI IBS = 13.87%; RI IBS+EBS = 5.42%)
and autumn relative humidity (RI IBS = 6.64%; RI
IBS+EBS = 6.55%) contributed significantly to both
kinds of models. Winter precipitation (RI IBS = 6.02%;
RI IBS+EBS = 7.59%) and winter relative humidity (RI
IBS = 15.34%; RI IBS+EBS = 20.25%) also displayed
notable RI values. Furthermore, our response curves

show that temperatures ranging from 15 to 28°C,
precipitation from 0.2 to 0.5 cm and relative humid-
ity levels from 70 to 85% had a positive influence
on the probability of the occurrence of human cases
of WNV infection, as presented in Supplementary
Figure S1. As demonstrated in the response curves
in Supplementary Figure S1, both kinds of models also
showed clear positive associations between ecological
suitability for local WNV circulation and human popula-
tion density, urban areas, open water areas, wet crops
and wetlands, as well as the level of soil moisture.
The relative influence of these variables was gener-
ally similar between both models, with a difference
of < 1%, except for open water areas (1.4% difference).
Specifically, the BRT models trained on integrated IBS
and EBS data assigned a higher RI to open water, mak-
ing it the top variable among the land-use variables, as
shown in Supplementary Table S3. Regarding the other
land-use variables, arable land, leaf index and forest
areas were negatively associated with the ecologi-
cal suitability for local WNV circulation, as presented
in Supplementary Figure S1.

As shown in Figure 2, we compared the ecological suit-
ability estimated for each NUTS₃ area when using IBS
data (Figure 2A) or a combination of IBS and EBS data
(Figure 2B). Both kinds of models identified highly
suitable regions for local WNV circulation leading to
human cases throughout southern Europe, particularly
in southern Iberian Peninsula, southern France, Italy,
the Balkan Peninsula and countries in the Danube
River Basin. The models trained on both IBS and EBS
data identified additional areas potentially at risk of
local WNV circulation, specifically in central and south-
ern Italy, southern Spain, southern France and Greece
(Figures 2B,C). As detailed in Supplementary Figure
S2, these inferred trends remained consistent with the
results obtained when training the ecological niche
models considering all or alternative subsets of poten-
tial pseudo-absences.

To further quantify these predictive differences
between the models trained on IBS or IBS and EBS
data, we computed the total area of the NUTS3 asso-
ciated with an ecological suitability value that had
increased by 5%, 10% and 20% when considering
both IBS and EBS data. Among the NUTS3 areas with
greater ecological suitability (red areas in Figure 2C),
151 demonstrated an increase of ≥ 5% (corresponding
to a total area of 587,766 km²), 78 an increase of ≥ 10%
(corresponding to a total area of 307,200 km²) and 31
an increase of ≥ 20% (corresponding to a total area
of 114,010 km²). Additionally, we identified 47 NUTS3
areas exhibiting higher ecological suitability when
solely training the models on IBS data (light blue areas
in Figure 2C). Among the 151 NUTS3 areas with a differ-
ence of ≥ 5% or more in ecological suitability, 41 corre-
sponded to WNV presences identified by both IBS and
EBS data, while 21 solely corresponded to EBS data,
totalling 62 NUTS3 areas with WNV occurrence records.

<figure><img src="image_9.png" /></figure>
detected by the official surveillance systems. The EBS data could enrich IBS datasets by revealing additional locations with cases, enhancing geographic granularity and potentially bridging potential surveillance gaps [53]. Areas associated with an increased estimated risk when both EBS and IBS data were included to train ecological niche models could constitute new areas for closer monitoring of local virus circulation and where preventive measures could be implemented by public health authorities. Overall, incorporating EBS data in disease risk mapping assessment has the potential of improving surveillance and subsequent design of control measures.

## Conclusion

By highlighting new areas potentially at risk of local circulation of WNV, our study illustrated the benefits of integrating EBS data along with IBS data. Event-based data could be an important tool for automatic and tailored health data collection, offering a dynamic and complementary approach for monitoring health threats. Finally, EBS could prove particularly advantageous in regions where the target pathogen is not yet known to circulate. In these areas, existing surveillance systems may be less established, and integrating EBS data can help address surveillance bias issues until adequate surveillance systems are implemented.

## Disclaimer

The views and opinions of the authors expressed herein do not necessarily state or reflect those of ECDC. The accuracy of the authors' statistical analysis and the findings they report are not the responsibility of ECDC. ECDC is not responsible for conclusions or opinions drawn from the data provided. ECDC is not responsible for the correctness of the data or data management, data merging, and data collation after the provision of the data. ECDC shall not be held liable for improper or incorrect use of the data.

## Ethical statement

The analyses conducted in this study were based on aggregated data without personal identifiers, consequently not requiring an ethical review prior to implementation.

## Funding statement

This work has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement number 874850. The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the European Commission. DE acknowledges support from the Fonds National de la Recherche Scientifique (F.R.S.-FNRS, Belgium). WVB is a member of ITM's Outbreak Research Team, financially supported by the Department of Economy, Science and Innovation of the Flemish government. SD also acknowledges support from the Fonds National de la Recherche Scientifique (F.R.S.-FNRS, Belgium; grant number F.4515.22) and from the Research Foundation — Flanders (Fonds voor Wetenschappelijk Onderzoek — Vlaanderen, FWO, Belgium; grant number G098321N).

## Use of artificial intelligence tools

None declared.

## Data availability

West Nile virus infection records from The European Surveillance System (TESSy) were provided by the European Centre for Disease Prevention and Control (ECDC). The data that support the findings of this study are available from ECDC, but restrictions apply to the availability of these data, which were used under licence for the current study and are not publicly available. The EBS PADI-web dataset generated and analysed during the current study is available in the CIRAD Dataverse repository at: https://doi.org/10.18167/DVN1/ZNVEPK.

## Acknowledgements

We thank the data providers of ECDC and PADI-web.

## Conflict of interest

None declared.

## Authors' contributions

KS, DE, EA and SD: conceptualisation and methodology. KS, DE and GD: data curation, formal analysis, validation, original draft preparation, writing, review and editing. MVG: manuscript editing and review. WVB, EA and SD: research design, manuscript writing, review and editing.

## References

1. 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

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

<figure><img src="image_10.png" /></figure>
10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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.00008320 PMID: 32392244

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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.onehelt.2021.100315 PMID: 34485672

24. 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

25. 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

26. Eurostat. NUTS- Nomenclature of Territorial Units for Statistics. Luxemburg: Eurostat; 2021. Available from: https://ec.europa.eu/eurostat

27. 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

28. Hernangómez D, giscoR: Download Map Data from GISCO API - Eurostat. Geneve: Zenodo; 28Aug2024. Available from: https://zenodo.org/doi/10.5281/zenodo.4317946

29. 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

30. 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

31. 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

32. 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

33. 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

34. 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

35. 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

36. 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

37. 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

38. 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

39. 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

40. 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

41. 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

42. 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

43. 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

44. 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

45. 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

46. 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

47. 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

<figure><img src="image_11.png" /></figure>
48. 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

49. 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

50. 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

51. 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

52. 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

53. 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

## License, supplementary material and copyright

This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0) Licence. You may share and adapt the material, but must give appropriate credit to the source, provide a link to the licence and indicate if changes were made.

Any supplementary material referenced in the article can be found in the online version.

This article is copyright of the authors or their affiliated institutions, 2024.

<figure><img src="image_12.png" /></figure>