Infection rate of Borrelia burgdorferi genospecies in human-biting Ixodes ricinus ticks: models for surveillance based on the French citizen science programme CiTIQUE

Infection rate of Borrelia burgdorferi genospecies in human-biting Ixodes ricinus ticks: models for surveillance based on the French citizen science programme CiTIQUE | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Infection rate of Borrelia burgdorferi genospecies in human-biting Ixodes ricinus ticks: models for surveillance based on the French citizen science programme CiTIQUE View ORCID Profile Thierno Madiou Bah , View ORCID Profile Jonas Durand , View ORCID Profile Arnaud Cougoul , View ORCID Profile William Wint , View ORCID Profile Francesca Dagostin , View ORCID Profile Thomas Opitz , View ORCID Profile Xavier Bailly , View ORCID Profile Pascale Frey-Klett , View ORCID Profile Karine Chalvet-Monfray doi: https://doi.org/10.1101/2025.09.18.25336063 Thierno Madiou Bah 1 University of Clermont Auvergne, INRAE, VetAgro Sup , UMR EPIA, Saint-Genès Champanelle, France 2 University of Lyon, INRAE, VetAgro Sup, UMR EPIA , Marcy l’Etoile, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Thierno Madiou Bah For correspondence: bah_thierno_madiou{at}yahoo.com Jonas Durand 3 Université de Lorraine, INRAE, IAM, Laboratoire Tous Chercheurs , F-54000 Nancy, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jonas Durand Arnaud Cougoul 1 University of Clermont Auvergne, INRAE, VetAgro Sup , UMR EPIA, Saint-Genès Champanelle, France 2 University of Lyon, INRAE, VetAgro Sup, UMR EPIA , Marcy l’Etoile, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Arnaud Cougoul William Wint 4 Environmental Research Group Oxford Ltd , Oxford, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for William Wint Francesca Dagostin 5 Research and Innovation Centre, Fondazione Edmund Mach , San Michele all’Adige (TN), Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Francesca Dagostin Thomas Opitz 6 Biostatistics and Spatial Processes (UR 546), INRAE , Avignon, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Thomas Opitz Xavier Bailly 1 University of Clermont Auvergne, INRAE, VetAgro Sup , UMR EPIA, Saint-Genès Champanelle, France 2 University of Lyon, INRAE, VetAgro Sup, UMR EPIA , Marcy l’Etoile, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Xavier Bailly Pascale Frey-Klett 3 Université de Lorraine, INRAE, IAM, Laboratoire Tous Chercheurs , F-54000 Nancy, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Pascale Frey-Klett Karine Chalvet-Monfray 1 University of Clermont Auvergne, INRAE, VetAgro Sup , UMR EPIA, Saint-Genès Champanelle, France 2 University of Lyon, INRAE, VetAgro Sup, UMR EPIA , Marcy l’Etoile, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Karine Chalvet-Monfray Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract In Europe, Lyme borreliosis is the most common vector-borne human disease, caused mainly by Borrelia afzelii and Borrelia garinii , two species of the Borrelia burgdorferi sensu lato (Bbsl) complex transmitted by the tick Ixodes ricinus . Accurately assessing the spatial risk of human exposure to these pathogens is essential for efficient public health surveillance. However conventional monitoring often struggles to produce geographically explicit, large-scale data that capture the heterogeneity of human exposure and its drivers. Focusing on continental France, we leveraged data from the French CiTIQUE citizen science programme to analyse spatial variation of Bbsl infection in georeferenced human-biting I. ricinus ticks and to model the relationship between Bbsl distribution and environmental, ecological, and anthropogenic factors. From 2017–2019, 1,891 ticks were analysed, of which 15% tested positive for Bbsl. The most prevalent genospecies were B. afzelii (7.2%) and B. garinii (4.2%). Infection rates varied spatially, with distinct distribution patterns across pathogen groups. Tick habitat suitability was the most consistent predictor for overall Bbsl infection probability, genospecies-specific models revealed the importance of their respective reservoir hosts: B. afzelii occurrence was positively associated with rodent species richness, whereas B. garinii was associated with Turdidae species and showed potential traces of a dilution effect due to rodents. Our findings demonstrate the value of citizen science for complementing formal surveillance and provide the first geographically explicit, large-scale insights into Bbsl eco-epidemiology in France. This scalable approach offers an adaptable framework for monitoring vector-borne disease risk and guiding public health strategies. Importance Lyme borreliosis, caused by Borrelia burgdorferi sensu lato, is the most common human vector-borne disease in Europe. Accurate assessment of spatial exposure risk is essential for effective public health surveillance and interventions. Using data from the French CiTIQUE citizen science program, we reveal pathogen-specific spatial patterns and identify the factors shaping them, at a geographic resolution not previously studied. Our findings demonstrate that citizen science can provide a scalable and adaptive framework for long-term surveillance of vector-borne disease risk, offering valuable insights to guide targeted prevention and control measures. Introduction Lyme borreliosis (Lb) is the most prevalent vector-borne human disease in Europe, with approximately 129,000 cases reported annually ( 1 ). Though incidence is likely underestimated as Lb is not notifiable in many countries. In France, 39,000 cases were reported in 2023, but recent analyses suggest that the actual incidence may have been closer to 191,000 cases during the same year ( 2 ). Lb is caused by pathogenic spirochetes of the Borrelia burgdorferi sensu lato (Bbsl) species complex, primarily transmitted by the hard tick Ixodes ricinus ( 3 , 4 ). In Europe, Borrelia afzelii and Borrelia garinii are the most common genospecies ( 5 ), predominantly maintained by rodents and birds, respectively ( 6 ). Their distinct eco-epidemiological cycles, combined with environmental and anthropogenic factors drive spatial heterogeneity in Bbsl distribution ( 7 ). Since genospecies differ in clinical outcomes, B. garinii being more often linked to neuroborreliosis ( 8 ), mapping this variation is critical. Human risk likewise varies widely across landscapes, underscoring the need to map this heterogeneity for surveillance, prevention, and public health action. Assessing spatial Lb risk typically relies on two layers of information : acarological hazard, defined by the density of host-seeking Bbsl-infected ticks, and human exposure, often approximated by incidence data ( 5 , 9 ). Both are essential for modelling the local states of the host–vector–pathogen system and predicting infection risk ( 10 ). Two complementary approaches are used: mechanistic models, which explicitly represent life-cycle and transmission processes ( 11 ), and statistical models ( 12 ), which link infection patterns to environmental or socio-economic factors ( 10 , 13 ). Field methods, such as host sampling, provide insights into pathogen ecology but rarely capture the full diversity of reservoirs within a single study (e.g., Rataud et al., 2022 for birds in France). Standardised questing tick sampling offers a good proxy for tick density and pathogen hazard (see for instance Wongnak et al., 2022 and Perez et al., 2023 ). However, tick sampling is often not standardized at country level, is limited to small areas or accessible environments and lacks information on human exposure. Human case reports from medical practitioners can provide broader spatial coverage of human risk of infection but are often limited by coarse spatial resolution ( 17 ). To overcome these limitations, citizen science initiatives have emerged as promising tools to generate geographically informed, large-scale data on human-tick encounters. Data that would otherwise be extremely difficult,if not impossible, to obtain through conventional research means, while also fostering engagement and raising awareness ( 18 ). In particular, the collection of human-biting ticks is directly associated to human exposure ( 19 ), and can be used to study spatial variations in the probability of Bbsl genospecies infection in human-biting ticks. France hosts diverse climates and environments ( 20 ) that support both tick populations and pathogen reservoirs ( 15 , 16 , 21 ). Previous studies on Bbsl distribution drivers in France were conducted at highly localised scales ( 22 , 23 ), making extrapolation to the contiguous national level difficult. At the other end of the spectrum, European-scale models of Bbsl distribution have been developed ( 7 ), but these rely on literature data which are from France limited and at a very local scale, highlighting the need for extensive data and studies to better understand and predict Bbsl risk in this country suffering high Lyme borreliosis incidence ( 24 ). Here, we used human-biting ticks collected through the CiTIQUE citizen-science programme between 2017 and 2019, to study the spatial distribution of Bbsl and its major genospecies across con-tinental France. Combining statistical relative risk mapping, which quantifies the relative density of pathogen presence in relation to its absence, and generalised additive models (GAMs), we characterised the spatial heterogeneity of tick infection risks and identified environmental and ecological drivers shaping the distribution of Bbsl in human-biting ticks, providing a detailed, data-driven picture of Lb eco-epidemiology across continental France. Materials and methods Tick acquisition Ticks were collected through the CiTIQUE citizen science programme( www.citique.fr ), launched in July 2017 as a collaboration between INRAE (French National Research Institute for Agriculture, Food and Environment), the Laboratory of Excellence ARBRE, Anses (French Agency for Food, Environmental and Occupational Health & Safety), and the CPIE network (Permanent Centre for Environmental Initiatives). This programme aims to improve the understanding of the ecology of ticks and the tick-borne diseases they transmit, particularly Lyme Borreliosis, in order to support prevention efforts based on monitoring. Citizens can participate in the CiTIQUE programme choosing among various levels of involvement, ranging from promoting the programme to actively contributing to research through activities in the open lab “Tous Chercheurs”, or by reporting tick bites and collecting and sending biting ticks to the tick bank maintained by the national programme. Tick bites can be reported via website, mobile application, or paper form, with information on date, GPS location, basic personal data (age, sex, activity, bite localisation), and environmental context (e.g., forest, garden, meadow). These data are linked to the tick samples upon reception. Between 2017 and 2020, more than 17,000 human tick-bite reports were submitted, with over 4,500 of these associated with one or more tick specimens. Pathogen identifications A total of 2009 ticks, collected between 2017 to 2019 (inclusive), were randomly selected for DNA extraction, with the objective of including at least 150 human-biting ticks for each French NUTS-1 regions (Nomenclature of Territorial Units for Statistics - major socio-economic regions) except for Provence-Alpes-Côte d’Azur (PAC) with 59 records. From this dataset, 1 891 Ixodes ricinus ticks were retained for the modelling analyses presented in this study, including 221 Adults, 1324 Nymphs and 110 Larvae, with 236 undetermined specimens. Details on the overall sample, selection procedure, and dataset structure can be found in ( 25 ). The tick stages and species were identified morphologically using identification keys by Pérez-Eid (2007) and Estrada-Pena et al. (2018) . Species identification was confirmed using specific primers and probes on the microfluidic real-time PCR assay described below. Tick DNA was extracted and screened for pathogens using the method described in Melis et al. (2024) . Briefly, tick DNA was extracted using NucleoSpin® Tissue kit (Macherey-Nagel, Germany), following manufacturer’s instructions. Then, all samples underwent a pre-amplification step by PCR with the Preamp Master Mix (Standard BioTools, USA). High-throughput microfluidic real-time PCR was then performed on a BioMark™ real-time PCR system (Standard BioTools, USA), using the 48.48 Dynamic Array™ (Standard BioTools, USA) to detect pathogens and identify ticks. Only results for the different Bbsl genospecies are presented and used in this paper: Borrelia afzelii, Borrelia garinii, Borrelia burgdorferi sensu stricto (s.s.), Borrelia valaisiana, Borrelia spielmanii, Borrelia bissettii, Borrelia lusitaniae . This method cannot detect coinfections between different Borrelia genospecies. The sequences of the primers and probes are provided in Michelet et al. (2014) . Covariates acquisition To explain Bbsl spatial distribution, we extracted covariates related to the density of host-seeking I. ricinus , pathogen occurrence and persistence, and human exposure. Climatic variables were included for their effect on tick habitat suitability ( 30 ), along with habitat suitability indices for I. ricinus and host-related variables. Non-competent hosts (e.g., roe deer) were also considered for their role as tick amplifiers ( 31 ). Because pathogens were identified in human-biting ticks, we further included variables reflecting human exposure, such as population density and human pressure. In total, 103 covariates were extracted at a 5-km grid resolution across continental France (Cf. Supplementary table 1). To reduce collinearity and overfitting, covariates were first grouped into seven categories: bioclimatic, land cover/soil, human pressure, and host-related (deer, birds, rodents, species richness). Then, within each category, hierarchical clustering on principal components (HCPC) was applied, with clusters defined by the largest relative loss of inertia. The most relevant covariates within each cluster were selected based on literature and prior hypotheses (Cf. Table 1 ). View this table: View inline View popup Table 1: Covariates selected for the models with their descriptions and associated hypotheses on their effects on Lyme borreliosis occurrence. For each tick, selected covariate values were extracted within a 5 km radius around the GPS coordinates to account for environmental heterogeneity and geolocation imprecision, and the weighted median, accounting for the proportion of each cell covered by the buffer, was retained (Cf. Table 1 ). All covariates were centred and scaled before analysis. To further limit collinearity between selected covariates, variance inflation factors (VIFs) were assessed, using a cut-off value of 5, to remove collinear covariates ( car package, version 3.1.3; Fox and Weisberg, 2018 ). Modelling We used Generalised Additive Models (GAMs) to investigate the variation in Bbsl and Bbsl genospecies distribution, used as the response variable, in relation to the selected set of continuous explanatory covariates (see Table 1 ), while accounting for the spatial distribution of the observations. The general structure of the models was: where f n are spline functions applied to the explanatory covariates x n,i , and te is a bivariate tensor product function accounting for the spatial structure based on the longitude and latitude. Given the large number of 23 explanatory covariates, we applied a double penalty approach to control smoothness of the model terms (i.e., curves f n and spatial surfaces te (long,lat), and perform covariate selection ( 72 ). The first penalty controlled the complexity of smooth functions to prevent overfitting by ensuring that relationships remained smooth and interpretable. The second penalty shrunk entire uninformative smooth functions towards zero and effectively removing them from the model. The degree of smoothing was selected using the restricted maximum likelihood (REML) method, and the number of basis functions for smooth terms was limited to the default of 10 simple terms to avoid overfitting. Three GAM specifications were fitted. The first model (M0) investigated factors associated with the presence of Bbsl across all observations, encompassing both presence ( y i = 1) and absence ( y i = 0) of Bbsl genospecies. In a second step, we focused the modelling process to locations where a Borrelia genospecies was detected, to identify factors associated with the infection probability of specific genospecies. Only B. afzelii and B. garinii had sufficient data for the whole territory, as they were the most frequent genospecies. Therefore, two separate GAMs were constructed (M1 for B. afzelii and M2 for B. garinii ), using the subset of 285 individuals where a Bbsl genospecies was present. In these models, infection with B. garinii or B. afzelii (depending on the model) was coded as presence ( y i = 1), while infection with another Bbsl genospecies was coded as absence ( y i = 0). The predicted infection rate of B. afzelii and B. garinii was calculated by the product of the predicted general bbsl presence (M0) with the species-specific relative probability (M1 or M2 depending on the genospecies). Model fitting was performed using the mgcv package (v1.9.1; Wood, 2011 ). All final models were checked for residuals validity using the Dharma package. (v0.4.7 ; Hartig et al., 2024 ). In addition to GAMs, we used spatial relative risk analysis to identify areas of elevated risk (i.e., “hot spots”) where tick infection by Bbsl and its genospecies was higher than expected, while accounting for underlying tick distribution. Spatial relative risk was estimated using the sparr package in R (v2.3.15; Davies et al., 2018 ). This method estimates the relative density of pathogen presence versus absence points. To account for our spatial sampling, we applied an adaptive bandwidth, allowing finer resolution in densely sampled regions and smoother estimates in sparsely sampled areas. The relative risk surface was estimated asymmetrically, meaning that different bandwidths were applied to the case and control point distributions, as recommended by Davies and Hazelton (2010) . Edge effects were corrected using the default settings described by Diggle (1985) . Statistical significance of elevated and diminished risk areas was assessed using asymptotic tolerance contours based on p-values, generated via the Monte Carlo method with 1,000 simulations. All analyses were performed in R version 4.3.2 ( 78 ). Results Descriptive analysis A total of 1,891 I. ricinus ticks were screened for pathogens, of which 291 (15%) were found to be infected with Borrelia burgdorferi sensu lato (Bbsl). Among these, B. afzelii and B. garinii were the most prevalent genospecies, infecting 136 (7.2%) and 80 (4.2%) individual ticks, respectively. Infections with other genospecies were less common, with 37 ticks (2%) infected with B. valaisiana , 25 (1.3%) with B. burgdorferi ss, 8 (0.4%) with B. spielmanii , and 5 (0.3%) with B. lusitaniae . No ticks were found to be infected with B. bissettii . I. ricinus ticks were collected across the continental French territory, and Bbsl was detected in all NUTS-1 regions. The distribution of infected ticks, however, was uneven: more infected samples relative to the sampling effort were reported in the eastern and central regions, particularly in Grand Est (GES) with the highest infection rate whereas southern regions such as Occitanie (OCC) and Provence-Alpes-Côte d’Azur (PAC) had fewer infected samples ( Figure 1 ). Download figure Open in new tab Figure 1: Original location of the 1891 human-biting ticks sent for pathogen identification across the French territory, and associated infection status. Left panel: Red points correspond to sample locations where one Borrelia was identified. Blue points mark sample locations where no Borrelia was detected. Darker blue shading represents overlapping points due to multiple reports from the same area. Right panel: detailed description of the borrelia genospecies found at each presence location. Each colour indicates the presence of a specific Borrelia burgdorferi genospecies. Major socio-economic (NUTS-1) are as follow : ARA: Auvergne-Rhône-Alpes; BFC: Bourgogne-Franche-Comté; BRE: Bretagne, CVL: Centre-Val de Loire; GES: Grand-Est; HDF: Haut-de-France; IDF: Ile-de-France; NOR: Normandie; NAQ: Nouvelle-Aquitaine; OCC: Occitanie; PDL: Pays-de-la-Loire; PAC: Provence Alpes Côte d’Azur. Factors associated with Bbsl distribution The results of the first GAM model (M0), which investigated factors associated with Bbsl infection rate, are presented in Figure 2 . After penalisation, only two variables were retained as significant predictors: the I. ricinus habitat suitability index and the grass cover fraction ( Table 1 ). The I. ricinus suitability index showed a positive association with Bbsl infection rate (p-value = 0.007), indicating that higher habitat suitability values correspond to an increased probability of Bbsl infection, with this relationship plateauing at higher suitability values ( Figure 2D ). Bbsl infection rate exhibited a convex relationship with grass cover fraction (p-value = 0.026). At lower grass cover values, an initial increase was associated with a decline in Bbsl infection rate, reaching a minimum, followed by a slight increase at higher grass cover values. However, this latter trend was characterised by greater uncertainty, likely due to the limited number of observations in areas with high grass cover ( Figure 2E ). Download figure Open in new tab Figure 2: Model predictions for Borrelia burgdorferi sensu lato (Bbsl) infection rate in Ixodes ricinus tick across France. A: Bbsl relative risk surface using the Bbsl presence and absence data. Significance contours represent lower risk areas in blue and higher risk areas in red. B: Predicted infection rate of Bbsl based on the M0 GAM, with values ranging from low (dark blue) to high (yellow-green) infection rate. C: Corresponding standard error of the GAM predictions, with lower uncertainty indicated in dark blue and higher uncertainty in yellow. D-E: Each plot demonstrates the marginal effects of I. ricinus habitat suitability index (D) and grass cover fraction (E) on Bbsl predicted infection rate, with 95 % confidence intervals shown in shaded areas while values for which the slope is significantly different from zero are highlighted in red. Black ticks along the x-axis represent observed values of the covariables. The relative risk map ( Figure 2A ) highlights significant low-risk areas (blue contours), which include the Bretagne (BRE) and Normandie (NOR) regions in northwestern France, and significant high-risk areas (red contours), concentrated in the Grand Est (GES), Bourgogne-Franche-Comté (BFC), and Centre-Val de Loire (CVL) regions in the east and centre of the country. These high-risk regions correspond to areas with higher Bbsl infection rate as predicted by the GAM model ( Figure 2B ). Conversely, Bretagne (BRE) and Normandie (NOR) exhibited lower predicted infection rates, consistently with the low-risk areas identified by the relative risk analysis. Additional regions, particularly in the southwest and Auvergne-Rhône-Alpes (ARA), also displayed elevated Bbsl infection rates according to the GAM model predictions ( Figure 2B ). The associated uncertainty in infection rate predictions was highest in the Rhône Valley (southeastern France) and the Alpine regions (eastern France) where sample density was lower and environmental heterogeneity may be greater ( Figure 2C ). Factors associated with Borrelia afzelii distribution The results of the M1 GAM model assessing factors associated with B. afzelii distribution are presented in Figure 3 . This model focused on sites where a Borrelia genospecies was detected, with the presence probability of B. afzelii (conditional on overall Borrelia presence) as the response variable. Therefore, the model highlights determinants of the variability in relative incidence of B. afzelii among all Borrelia occurrences. Four covariates were identified as significant predictors: cattle density, I. ricinus habitat suitability index, rodent species richness, and grass cover fraction. Download figure Open in new tab Figure 3: Model predictions for Borrelia afzelii infection rate in Ixodes ricinus tick across France. A: B. afzelii relative risk surface using the B. afzelii presence and absence data . Significance contours represent lower risk areas in blue and higher risk areas in red. B: Predicted infection rate of B. afzelii based on the product of the predicted general Borrelia burgdorferi sensu lato (Bbsl) presence (M0) with the B. afzelii relative probability knowing presence (M1). Infection rate values are ranging from low (dark blue) to high (yellow-green) infection rate. C: Corresponding standard error of the predicted infection rate of B. afzelii based on the product of M0 and M1, with lower uncertainty indicated in dark blue and higher uncertainty in yellow. D-G: Each plot demonstrates the marginal effects of the model on the relative probability of having B. afzelii knowing Bbsl presence (M1), with Cattle density (D), I. ricinus habitat suitability index (E), indices of rodent species richness (F) and grass cover fraction (G). 95 % confidence intervals are shown in shaded areas while values for which the slope is significantly different from zero are highlighted in red. Black ticks along the x-axis represent observed values of the covariables. Cattle density was negatively associated with B. afzelii presence probability (p-value = 0.035), although uncertainty increased at higher cattle densities ( Figure 3D ). The relationship between B. afze lii presence and I. ricinus habitat suitability index was convex (p-value =0.009), with a decline in presence probability observed at lower suitability values, reaching a minimum around 0 on the x-axis, followed by an increase at higher suitability values. However, both trends were accompanied by high uncertainty ( Figure 3E ). A concave relationship was observed with rodent species richness (p-value =0.015), peaking around a richness value of one, before slightly declining. Uncertainty was highest at both low and high richness values ( Figure 3F ). Grass cover fraction also showed a concave relationship (p-value =0.041), with B. afzelii presence probability increasing to a maximum at intermediate grass cover values (around 2 on the x-axis), followed by a slight decline and higher uncertainty at greater grass cover fractions ( Figure 3G ). The relative risk map ( Figure 3A ) revealed that areas of significant low (blue contours) and high (red contours) B. afzelii risk broadly overlapped with those identified for Bbsl. However, an additional high-risk area was detected in the northern part of the Occitanie (OCC) region. Predictions from the GAM aligned with the relative risk surface, with the highest B. afzelii infection rates in northeastern regions, including GES, BFC and CVL Grand Est, Bourgogne-Franche-Comté, and Centre-Val de Loire. High infection risk was also predicted in regions such as Nouvelle-Aquitaine and Auvergne-Rhône-Alpes ( Figure 3B ). Prediction uncertainty was greatest in the Rhône Valley (southeastern France), the Alps (eastern France), and the eastern part of the Grand Est region, which also correspond to areas of high predicted infection rates ( Figure 3C ). Factors associated with Borrelia garinii distribution The predicted infection rate of B. afzelii and B. garinii was calculated by the product of the predicted general bbsl presence (M0) with the species-specific relative probability (M1 or M2 depending on the genospecies) The results of the M2 GAM assessing factors associated with B. garinii distribution are presented in Figure 4 . This model, again restricted to locations where Borrelia was detected, used the presence probability of B. garinii (conditional on Borrelia presence ) as the response variable. Therefore, the model highlights determinants of the variability in relative incidence of B. garinii among all Borrelia occurrences. Two covariates were identified as significant predictors: rodent species richness and Turdidae abundance. Download figure Open in new tab Figure 4: Model predictions for Borrelia garinii infection rate in Ixodes ricinus tick across France. A: B. garinii relative risk surface using the B. garinii presence and absence data . Significance contours represent lower risk areas in blue and higher risk areas in red. B: Predicted infection rate of B. garinii based on the product of the predicted general Borrelia burgdorferi sensu lato (Bbsl) presence (M0) with the B. garinii relative probability knowing presence (M2). Infection rate values are ranging from low (dark blue) to high (yellow-green) prevalence. C: Corresponding standard error of the predicted infection rate of B. garinii based on the product of M0 and M2, with lower uncertainty indicated in dark blue and higher uncertainty in yellow. D-F: Each plot shows the marginal effects of the model on the relative probability of having B. garinii knowing Bbsl presence (M2) with indices of rodent species richness (D), Turdidae abundance (E) on the probability of having B. garinii knowing Bbsl presence. 95 % confidence intervals are shown in shaded areas while values for which the slope is significantly different from zero are highlighted in red. Black ticks along the x-axis represent observed values of the covariables. Rodent species richness showed a significant negative association with B. garinii presence probability (p-value = 0.002), with increased uncertainty at lower values along the x-axis ( Figure 4D ). In contrast, Turdidae abundance had a significant positive effect on B. garinii presence probability (p-value = 0.049), although uncertainty increased markedly at higher abundance values ( Figure 4E ). The relative risk map of B. garinii ( Figure 4A ) revealed localised high-risk areas (red contours) in the western part of the Bretagne BRE region and the western part of the Centre-Val de Loire CVL region. Low-risk areas (blue contours) were concentrated in the southwestern regions, including Occitanie and Nouvelle-Aquitaine ( Figure 4A ). GAM model predictions ( Figure 4B ) were consistent with the relative risk map, showing the highest B. garinii infection probabilities in the identified high-risk areas. Additional suitable areas for B. garinii were predicted in northwestern France, particularly in Bretagne BRE, Normandie NOR, and Hauts-de-France HDF, as well as in the northern parts of Nouvelle-Aquitaine NAQ and Centre-Val de Loire CVL. Uncertainty associated with the infection probability predictions was generally low but increased in areas with the highest predicted infection probability, notably in the high-risk zones highlighted on the map ( Figure 4C ). Discussion Assessing spatial risk of Lyme borreliosis is challenging, as localized surveys lack coverage and broad incidence data miss ecological drivers. By leveraging CiTIQUE citizen-science data, our study provides the first geographically explicit, large-scale view of Borrelia burgdorferi sensu lato distribution in France, linking infection risk with key environmental, ecological, and anthropogenic factors. Spatial heterogeneity of the tick infection rates B. afzelii and B. garinii were the most prevalent genospecies in the biting ticks which is consistent with the dominant genospecies and their relative frequencies reported across European countries, primarily based on questing tick studies ( 5 , 79 ). However, the pathogen detection method only identified the dominant genospecies in co-infected ticks, potentially underestimating the prevalence of other species. For example, if B. afzelii dominates in co-infected ticks, it may have been disproportionately reported in areas with high tick abundance and higher frequencies of co-infection. Previous work at the European scale ( 7 ), using a rough resolution (around 28 km × 28 km per cell) reported high prevalence of B. afzelii across France, particularly in eastern Auvergne-Rhône-Alpes. In contrast, our analyses at a finer resolution (5km x5km per cells) revealed a more heterogeneous pattern, with higher infection rates in eastern and central regions (Grand Est, Bourgogne-FrancheComté, Centre-Val de Loire) and lower rates in western, northern, and southern regions (Bretagne, Normandie, Occitanie). For B. garinii , they found very high prevalence in these western, southern and northern regions, whereas our analyses revealed a more even distribution nationwide, while still confirming higher infection rates in the western and northern part. Influence of habitats and hosts on tick infection rates Our GAMs highlighted distinct environmental and ecological factors associated with the distribution of Bbsl and its two main genospecies, B. afzelii and B. garinii . At the overall Bbsl level, infection probability was positively associated with the I. ricinus habitat suitability index, a composite indicator derived from a multi-criteria analysis integrating climate, altitude, land cover and wild ungulates density, which are factors known to influence tick abundance ( 21 ). This result suggests that, in areas where human exposure occurs, favourable environments for ticks also promote higher Bbsl circulation and prevalence. This finding aligns with theoretical models predicting a positive, yet non-linear, relationship between tick density and pathogen prevalence, modulated by host community composition, including the presence of non-competent host for Bbsl transmission ( 80 , 81 ). Observational studies in comparable ecological settings are consistent with these results. In Belgium, tick density was identified as a key driver of Bbsl-infected nymph density, with limited evidence for dilution effects from non-competent hosts ( 82 ). Similarly, in the Netherlands, increased ungulate abundance led to higher tick density and a non-linear, accelerating rise in the density of Bbsl-infected nymphs, without clear evidence for dilution effects attributable to ungulates ( 83 ). Long-term monitoring in southern England further showed that habitats favourable to ticks, such as structurally diverse forests and woodland edges, sustain higher densities of both questing and Bbsl-infected I. ricinus nymphs ( 84 ). At the genospecies level, habitat and host associations differed, reflecting the distinct ecological characteristics of B. afzelii and B. garinii ( 6 , 85 , 86 ). Borrelia garinii infection probability was positively associated with Turdidae bird abundance, consistent with its known reliance on avian hosts ( 87 , 88 ), and negatively associated with rodent species richness, possibly reflecting a dilution effect from these non-specific host ( 83 ). Conversely, B. afzelii infection probability was positively associated with rodent richness, consistent with the role of small mammals as primary reservoirs for this genospecies ( 39 ). Rodent richness was also positively associated with the presence of Bbsl, likely reflecting both the dominance of B. afzelii in our dataset and the association of several minor Bbsl genospecies, as B. burgdorferi sensu stricto , with small mammal hosts ( 89 ). However these results on B. afzelii could be either because B. afzelii is very dominant especially in the core areas of Borrelia presence or it could be a consequence of a method bias toward B. afzelii in co infected ticks, linking its presence with areas of high infection rate. Grass cover exhibited a non-linear association with Bbsl and B. afzelii infection probabilities, with a negative trend across most observed values. Elevated risk was observed at low grass cover levels, characteristic of forest, fragmented woodlands, and associated ecotones, including adjacent private gardens, which are all known to favour both tick and Borrelia presence ( 90 ). In contrast, extensive grass cover, typical of open meadows or pastures, was associated with lower infection probabilities, likely due to reduced tick survival in open, hot, and dry environments, and a lower density of competent hosts ( 91 , 92 ). Present limitations and perspectives offered by the continuous engagement of citizens Our modelling approach prioritised interpretability using a parsimonious set of covariates, selected to limit collinearity and capture the main ecological drivers, while accounting for the current sampling structure. Although additional covariates, such as vegetation indices ( 7 ), forest structural complexity ( 93 ) or soil properties ( 94 ), have been highlighted in previous studies, our sampling resolution likely limits the reliable detection of such fine-scale environmental effects. Sampling constraints also restricted our capacity to investigate several relevant aspects of Bbsl infection risk, including variation across tick developmental stages, seasonal and inter-annual dynamics, as well as infection rates across specific environmental contexts. Despite efforts to mitigate sampling bias, by analysing a minimum of 150 ticks per region, a substantial proportion of the analysed samples originated from densely populated areas, leaving gaps in remote and sparsely populated regions in France. A further challenge in estimating Lyme disease risk is the limited understanding of the components leading host seeking infected Bbsl tick to human populations ( 95 ). In this regard, our analysis on human-biting ticks provides valuable insights ( 96 ). However, a more precise assessment of risk would require comparison with infection rates in questing ticks, to disentangle ecological hazard from human exposure. Finally, integrating CiTIQUE tick-bite reports could provide complementary information on the socio-behavioral and ecological factors shaping human exposure thereby improving risk mapping ( 97 ). Despite these limitations, our study demonstrates the substantial potential of citizen-generated data for monitoring tick-borne pathogen distribution at a national scale. The ticks analysed here represent approximately 40 % of all human-biting ticks submitted to CiTIQUE along with a tick-bite report during the study period. Since its inception, the CiTIQUE programme has grown considerably, with over 60,000 human and animal-biting ticks currently stored in the national tick bank. This continuously expanding resource offers unique opportunities to refine surveillance and research on tick-borne diseases in France. As data and biting ticks continues to be collected, targeted sub-sampling strategies could be implemented to improve spatial representativeness, assess temporal dynamics, compare infection rates across tick developmental stages, and address refined ecological questions regarding Bbsl infection risk. Such approaches will contribute to a more detailed understanding of the eco-epidemiology of Bbsl genospecies and other tick-borne pathogens in human-biting ticks. In the context of global environmental change and potential shifts in ticks and tick-borne pathogen distributions, the continued development of CiTIQUE provides a scalable, adaptable, citizen-driven tool to support large scale surveillance, environmental risk assessment, and public health preparedness. Declaration of generative AI in scientific writing During the preparation of this work the authors used ChatGPT in order to improve English writing. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article. Data availability The dataset will be made publicly available through the Recherche Data Gouv repository ( https://entrepot.recherche.data.gouv.fr ) upon publication of this study. Ethics approval Approval of the ethics committee was not required since participation in sending ticks was voluntary and the short questionnaire online was anonymous (no identification possible). The participants are given information about the study on the website of the project. Acknowledgement Our thoughts are with Jean-François Cosson and Béatrice Palin, who both passed away. Jean-François Cosson co-created the CiTIQUE project in 2016 with Pascale Frey-Klett and Muriel Vayssier-Taussat. Béatrice Palin was the first “tick librarian” of the program. We extend our gratitude to the current and former members of the CiTIQUE team, particularly Irene Carravieri, Cyril Galley, Julien Marchand, Sandrine Capizzi, Gwenaël Vourc’h, Philippe Lecomte, Sara Moutailler, Clémence Galon and all the tick librarians, as well as to all the citizens and CiTIQUE partners who made this study possible by reporting tick bites and sending the collected ticks to the tick library. We are also grateful for the significant financial support that helped collecting ticks and providing tick analyses, including from INRAE, ANSES, the Ministry of Health and Access to Care, the Laboratory of Excellence ARBRE (ANR-11-LABX-0002-01), the Grand Est Region, the European Regional Development Fund, the “Des Hommes et Des Arbres” investment program (Territoire d’Innovation), the Groupama Foundation, and the Fondation de France. The post-doctoral grant of Thierno Madiou Bah was supported by the INRAE scientific divisions Animal Health and Ecology and Biodiversity. Footnotes The authors affiliations were updated, and an Importance section as well as a declaration of generative AI use in scientific writing were added. The Introduction and Discussion sections were slightly shortened while maintaining their flow and meaning. References 1. ↵ Burn L , Tran TMP , Pilz A , Vyse A , Fletcher MA , Angulo FJ , Gessner BD , Moïsi JC , Jodar L , Stark JH . 2023 . 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