In recent decades, the incidence of Lyme borreliosis (LB) in Europe seems to have increased, underpinning a growing public health concern. LB surveillance systems across the continent are heterogeneous, and the spatial and temporal patterns of LB reports have been little documented. In this study, we explored the spatio-temporal patterns of LB cases reported in France from 2016 to 2019, to describe high-risk clusters and generate hypotheses on their occurrence. The space–time K-function and the Kulldorf’s scan statistic were implemented separately for each year to evaluate space–time interaction between reported cases and searching clusters. The results show that the main spatial clusters, of radius size up to 97 km, were reported in central and northeastern France each year. In 2017–2019, spatial clusters were also identified in more southern areas (near the Alps and the Mediterranean coast). Spatio-temporal clustering occurred between May and August, over one-month to three-month windows in 2016–2017 and in 2018–2019. A strong spatio-temporal interaction was identified in 2018 within 16 km and seven days, suggesting a potential local and intense pathogen transmission process. Ongoing improved surveillance and accounting for animal hosts, vectors, meteorological factors and human behaviors are keys to further elucidate LB spatio-temporal patterns.

Background Lyme Borreliosis (LB) is the most widespread hard tick-borne zoonosis in the Northern Hemisphere and shows a seasonal pattern. Existing studies in Europe mainly focused on acarological risk assessment, with very limited investigations exploring human LB occurrence. We aimed to highlight areas and seasons of higher risk for LB occurrence in mainland France, integrating information on meteorological, environmental, animal hosts and human exposure to quantify the associated spatial and temporal risk factors. Methods We fitted 2016—19 French LB surveillance data to a two-part spatiotemporal statistical model, defined with binomial and gamma distributions, to explore the factors associated with the presence and increased LB incidence. Shared spatial and temporal random effects were specified using a Besag-York-Mollie model and a seasonal model, respectively. Coefficients were estimated in a Bayesian framework using integrated nested Laplace approximation. Projections and data for 2020 were used for model validation. Findings LB presence was associated with a high vegetation index (>0.6). LB incidence increased in areas highly suitable for deer (>80% cover per area), with mild soil temperatures (10—15 °C) in the season preceding the onset, moderate air saturation deficits (3—5 mmHg), and higher proportion of tick bite reports. Prediction maps showed a higher risk of LB in spring and summer (April-September). Substantial geographical variation in LB incidence was found. Higher incidence was reported in parts of eastern, midwestern, and southwestern France. Interpretation This is the first national-level assessment of seasonal human LB occurrence in Europe allowing to disentangle factors associated with LB presence and increased incidence. This model illustrates a spatial integrated analysis of meteorological, hosts, and anthropogenic factors for a zoonotic and vector-borne infection of major public health concern, and can be used as a reference model to be calibrated in other LB-affected areas. Funding WF is funded by a Sorbonne University PhD fellowship, JD is supported by a grant overseen by the French National Research Agency (ANR) as part of the <Investissements d'Avenir> program (ANR-11-LABX-0002-01, Lab of Excellence ARBRE).

Background Lyme borreliosis (LB) is the most widespread hard tick-borne zoonosis in the northern hemisphere. Existing studies in Europe have focused mainly on acarological risk assessment, with few investigations exploring human LB occurrence. Aim We explored the determinants of spatial and seasonal LB variations in France from 2016 to 2021 by integrating environmental, animal, meteorological and anthropogenic factors, and then mapped seasonal LB risk predictions. Methods We fitted 2016–19 LB national surveillance data to a two-part spatio-temporal statistical model. Spatial and temporal random effects were specified using a Besag-York-Mollie model and a seasonal model, respectively. Coefficients were estimated in a Bayesian framework using integrated nested Laplace approximation. Data from 2020–21 were used for model validation. Results A high vegetation index (≥ 0.6) was positively associated with seasonal LB presence, while the index of deer presence (> 60%), mild soil temperature (15–22 °C), moderate air saturation deficit (1.5–5 mmHg) and higher tick bite frequency were associated with increased incidence. Prediction maps show a higher risk of LB in spring and summer (April–September), with higher incidence in parts of eastern, midwestern and south-western France. Conclusion We present a national level spatial assessment of seasonal LB occurrence in Europe, disentangling factors associated with the presence and increased incidence of LB. Our findings yield quantitative evidence for national public health agencies to plan targeted prevention campaigns to reduce LB burden, enhance surveillance and identify further data needs. This approach can be tested in other LB endemic areas.