Abrial, D. | Search bioRxiv

bioRxiv2025

Map simulator of tick abundance in heterogeneous agricultural landscapes

Among vector-borne diseases, tick-borne diseases (TBD) are a major concern for human health. Mapping the distribution of important tick species is thus a major challenge for efficient prevention. Due to its specific ecological requirements, Ixodes ricinus, the main tick species in Europe responsible for TBD transmission, lives mostly in woodlands but also at the interface between woodlands and pastures or crops and along hedgerows. At the landscape scale, extensive variations in tick densities are observed but remain poorly understood. In that aim, we built a statistical model to identify the landscape variables influencing the abundance of questing I. ricinus nymphs, using GLMM approaches and MCMC estimates. This model was fitted on a data set based on a field sampling of ticks conducted during 3 years in 2 different agricultural landscapes in northwest and southwest France, for a total of 5390 sampling units. Among 12 variables investigated, 4 were finally kept in the model: woodland perimeter, woodland distance, road distance and building perimeter. Then, we developed a R package that simulates the abundance of questing nymphs within a given agricultural landscape, taking into account the influence of the different habitats as determined by the above statistical model. The maps obtained as an output from this simulator will be a useful tool for visualizing TBD risk, notably for stake-holders involved in landscape management and public health decisions. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=88 SRC="FIGDIR/small/663759v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@f950aborg.highwire.dtl.DTLVardef@1f0f27org.highwire.dtl.DTLVardef@11bde05org.highwire.dtl.DTLVardef@8d2202_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsIxodes ricinus abondance is influenced by landscape characteristics Tick sampling was carried out in heterogeneous agricultural landscapes Informative variables related to habitats were identified by statistical analysis Woodlands, roads and buildings influence tick densities The resulting model was used to build a simulator of tick at-risk zones

ecology↗

bioRxiv2019

Bayesian hierarchical models for disease mapping applied to contagious pathologies

Disease mapping aims to determine the underlying disease risk from scattered epidemiological data and to represent it on a smoothed colored map. This methodology is based on Bayesian inference and is classically dedicated to non-infectious diseases whose incidence is low and whose cases distribution is spatially (and eventually temporally) structured. Over the last decades, disease mapping has received many major improvements to extend its scope of application: integrating the temporal dimension, dealing with missing data, taking into account various a prioris (environmental and population covariates, assumptions concerning the repartition and the evolution of the risk), dealing with overdispersion, etc. We aim to adapt this approach to rare infectious diseases. In the context of a contagious disease, the outcome of a primary case can in addition generate secondary occurrences of the pathology in a close spatial and temporal neighborhood; this can result in local overdispersion and in higher spatial and temporal dependencies due to direct and/or indirect transmission. We have proposed and tested 60 Bayesian hierarchical models on 400 simulated datasets and bovine tuberculosis real data. This analysis shows the relevance of the CAR (Conditional AutoRegressive) processes to deal with the structure of the risk. We can also conclude that the negative binomial models outperform the Poisson models with a Gaussian noise to handle overdispersion. In addition our study provided relevant maps which are congruent with the real risk (simulated data) and with the knowledge concerning bovine tuberculosis (real data).\n\nAuthor summaryDisease mapping is dedicated to non-infectious diseases whose incidence is low and whose distribution is spatially (and eventually temporally) structured. In this paper, we aim to adapt this approach to rare infectious pathologies. In the context of a contagious disease, the outcome of a primary case can in addition generate secondary occurrences of the pathology in a close spatial and temporal neighborhood, resulting in local overdispersion and in high spatial and temporal dependencies. We thus explored different adapted spatial, temporal and spatiotemporal links and highlight the most adapted to likely risk structures for infectious diseases. We also conclude that the negative binomial models outperform the Poisson models with a Gaussian noise to handle overdispersion. Our study also provided relevant maps which are congruent with the real risk (in case of simulated data) and with the knowledge concerning bovine tuberculosis (when applying to real data). Thus disease mapping appears as a promising way to investigate rare infectious diseases.

scientific communication and education↗