Biology subjects

Bravo de Guenni, L.

Publications and source records attributed to Bravo de Guenni, L..

3 recordsLinked to original sources

bioRxiv2026

Bayesian spatial prediction of three medically important tick species in Illinois

Tick-borne diseases are now reported from nearly every county in Illinois, and three vector tick species (Amblyomma americanum, Dermacentor variabilis, and Ixodes scapularis) are of particular concern because these are responsible for most of the tick-borne disease transmission in the state. However, active surveillance is patchy, many counties have little or no sampling, and there is no statewide, quantitative map of relative abundance that can be used to anticipate risk in unsampled areas. To address these gaps, we developed Bayesian hierarchical spatial models to estimate the county-level abundance of these three vector tick species in Illinois. Using active surveillance data from 2019-2022, we modeled county-level abundance as a function of climate, land cover, forest fragmentation, and deer habitat suitability. Spatial dependence was captured using a Besag-York-Mollie 2 (BYM2) prior implemented in INLA, along with spatial 5-fold cross-validation to assess predictive performance. A. americanum showed the highest predicted abundance in southern and central Illinois, D. variabilis was widespread but diffuse, and I. scapularis was concentrated in northern and selected central counties. Together, these models provide the first spatial, statewide, uncertainty-aware assessment of tick abundance in Illinois, highlighting priority counties where surveillance lags disease risk.

ecology↗

bioRxiv2026

Filling surveillance gaps: Bayesian INLA models for predicting tick distributions in data-sparse regions

Fragmented and geographically uneven surveillance limits the ability to target tick monitoring and control in many livestock systems. We evaluated whether heterogeneous published surveillance could be converted into district-level, uncertainty-aware abundance predictions for Rhipicephalus microplus and Hyalomma anatolicum across Punjab and Khyber Pakhtunkhwa, Pakistan. Species-specific tick counts were standardized by the number of animals examined and sampling duration and expressed as ticks per 100,000 animal-months. Environmental covariates were summarized using principal component analysis, with livestock density included separately. Bayesian Gaussian models, with and without a district-level Besag-York-Mollie 2 spatial effect, were fitted in R using Integrated Nested Laplace Approximation (INLA) and compared. Leave-one-district-out validation was performed to assess out-of-sample predictive performance. Spatial models improved model fit for both species. For R. microplus, PC1 and PC4 showed credible negative associations with abundance, whereas no fixed-effect 95% credible interval excluded zero for H. anatolicum. Predicted R. microplus abundance was more spatially heterogeneous, with high but uncertain estimates in several northern districts, whereas H. anatolicum predictions were more homogeneous and relatively higher toward southern Punjab. Mapping posterior uncertainty identified districts where additional standardized surveillance would be most informative. This approach provides a transferable framework for extracting spatial surveillance information from heterogeneous veterinary data while explicitly representing uncertainty.

ecology↗

bioRxiv2026

Using machine learning to overcome mosquito collections missing data for malaria modeling

Entomological surveillance plays a crucial role in areas where malaria remains endemic, yet gathering data on mosquito populations is often expensive and complicated, particularly in remote locations with challenging logistics and inconsistent sampling schedules. Access to extensive time series data on mosquito species at specific sites would greatly enhance insights into seasonal trends and the biting habits of vectors of malaria parasites. Gaps in mosquito count records pose a significant challenge for researchers and public health officials seeking to establish early warning systems and effective vector control programs. In this study, we apply quantitative machine learning techniques to address missing data in estimates of mosquito abundance collected from 2009 to 2016 in Bolivar State, Venezuela. We evaluated Linear Regression, Stochastic Linear Regression, K Nearest-Neighbor, and Gradient Boosting methods for imputing missing counts of Anopheles mosquitoes, employing a leave-one-out cross-validation strategy. Additionally, we developed a predictive malaria transmission model incorporating mosquito abundance and climate variables (El Nino 3.4 Index, rainfall, and mean air temperature) as covariates. Our generalized time series model forecasts malaria incidence of Plasmodium vivax and Plasmodium falciparum based on climate dynamics and imputed mosquito data. Model performance was assessed using root mean square error, mean absolute error, and mean absolute percentage error. The final results demonstrated that machine learning imputation significantly improved the accuracy and reliability of P. vivax malaria incidence predictions but failed to predict P. falciparum incidence. The study demonstrates that method choice significantly influences the reconstruction of seasonal abundance patterns and the performance of malaria incidence models. Nevertheless, the proposed models strengthen the foundation for targeted interventions and surveillance in endemic regions. Despite limitations in data continuity and coverage, the findings highlight the value of combining multiyear entomological data sets with robust imputation and sensitivity analyses to improve predictive modeling in resource-constrained, malaria-endemic settings.

bioinformatics↗