Environment, animal hosts and human activity on predicting space-time variations of Lyme Borreliosis incidence in France: a Bayesian two-part model

BackgroundLyme 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. MethodsWe 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. FindingsLB presence was associated with a high vegetation index ([&ge;]0{middle dot}6). LB incidence increased in areas highly suitable for deer ([&ge;]80% cover per area), with mild soil temperatures (10-15{degrees}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. InterpretationThis 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. FundingWF 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 dAvenir>> program (ANR-11-LABX-0002-01, Lab of Excellence ARBRE).