Animal Exposure Model for Mapping Crimean-Congo Hemorrhagic Fever Virus Emergence Risk.

To estimate the determinants of spatial variation in Crimean-Congo hemorrhagic fever virus (CCHFV) transmission and to create a risk map as a preventive public health tool, we designed a survey of small domestic ruminants in Andalusia, Spain. To assess CCHFV exposure spatial distribution, we analyzed serum from 2,440 sheep and goats by using a double-antigen ELISA and modeled exposure probability with environmental predictors by using generalized linear mixed models. CCHFV antibodies detected in 84 samples confirmed low CCHFV prevalence in small domestic ruminants in the region. The best-fitted statistical model indicated that the most significant predictors of virus exposure risk were cattle/horse density and the normalized difference vegetation index. Model validation showed 99.7% specificity and 10.2% sensitivity for identifying CCHFV circulation areas. To map CCHFV exposure risk, we projected the model at a 1 × 1-km spatial resolution. Our study provides insight into CCHFV ecology that is useful for preventing virus transmission.

Mapping the risk of exposure to Crimean-Congo haemorrhagic fever virus in the Iberian Peninsula using Eurasian wild boar (Sus scrofa) as a model.

Crimean-Congo haemorrhagic fever (CCHF) virus (CCHFV) is a tick-borne zoonotic pathogen that can cause a lethal haemorrhagic disease in humans. Although the virus appears to be endemically established in the Iberian Peninsula, CCHF is an emerging disease in Spain. Clinical signs of CCHFV infection are mainly manifested in humans, but the virus replicates in several animal species. Understanding the determinants of CCHFV exposure risk from animal models is essential to predicting high-risk exposure hotspots for public health action. With this objective in mind, we designed a cross-sectional study of Eurasian wild boar (Sus scrofa) in Spain and Portugal. The study analysed 5,291 sera collected between 2006 and 2022 from 90 wild boar populations with a specific double-antigen ELISA to estimate CCHFV serum prevalence and identify the main determinants of exposure probability. To do so, we statistically modelled exposure risk with host- and environment-related predictors and spatially projected it at a 10 × 10 km square resolution at the scale of the Iberian Peninsula to map foci of infection risk. Fifty-seven (63.3 %) of the 90 populations had at least one seropositive animal, with seroprevalence ranging from 0.0 to 88.2 %. Anti-CCHFV antibodies were found in 1,026 of 5,291 wild boar (19.4 %; 95 % confidence interval: 18.3-20.5 %), with highest exposure rates in southwestern Iberia. The most relevant predictors of virus exposure risk were wild boar abundance, local rainfall regime, shrub cover, winter air temperature and soil temperature variation. The spatial projection of the best-fit model identified high-risk foci as occurring in most of western and southwestern Iberia and identified recently confirmed risk foci in eastern Spain. The results of the study demonstrate that serological surveys of CCHFV vector hosts are a powerful, robust and highly informative tool for public health authorities to take action to prevent human cases of CCHF in enzootic and emergency settings.

Large scale spatio-temporal modelling of risk factors associated with tuberculosis exposure at the wildlife-livestock interface.

The management of animal tuberculosis (TB) is a priority for European Union animal health authorities. However, and despite all the efforts made to date, a significant part of Spain has as yet been unable to obtain the officially tuberculosis-free (OTF) status. Information regarding wildlife disease status is usually scarce, signifying that the role played by wildlife is usually ignored or poorly assessed in large-scale TB risk factor studies. The National Wildlife Health Surveillance Plan in Spain now provides information on infection rates in wildlife reservoirs at a national level, but there are limitations as regards the sample size, the spatio-temporal distribution of the samples, and the lack of homogeneity of the diagnostic techniques employed. The objective of the study described herein was, therefore, to employ a Bayesian approach with the intention of identifying the risk factors associated with four TB rates in cattle: prevalence, incidence, maintenance and persistence in Spain during the period 2014-2019. The modeling approach included highly informative spatio-temporal latent effects with which to control the limitations of the data. Variation partitioning procedures were carried out, and the pure effect of each factor was mapped in order to identify the most relevant factors associated with TB dynamics in cattle in each region. This made it possible to disclose that the movement of cattle, particularly from counties with herd incidence > 1%, was the main driver of the TB dynamics in cattle. The abundance of herds bred for bullfighting was retained in all four models, but had less weight than the movements. After accounting for farm-related factors, the TB prevalence in wild boar was retained in all the models and was significantly related to incidence, maintenance and persistence. With regard to the incidence, variation partitioning revealed that wildlife was the most explicative factor, thus suggesting that it plays a role in the introduction of the pathogen into uninfected herds, and consequently highlighting its importance in breakdowns. These results show, for the first time on a national scale, that wild ungulates play a relevant role in the spatio-temporal variability of TB in cattle, particularly as regards their disease status. Moreover, the spatial representation of the pure effect of each factor made it possible to identify which factors are driving the disease dynamics in each region, thus showing that it is a valuable tool with which to focus efforts towards achieving the OTF status.

Environmental factors driving fine-scale ixodid tick abundance patterns.

Tick abundance is an essential demographic parameter to infer tick-borne pathogen transmission risks. Spatiotemporal patterns of tick abundance are heterogeneous, so its determinants at small spatial scales need to be understood to reduce their negative effects on hosts. Current knowledge of these determinants is scarce, especially in Mediterranean environments, limiting the possibilities for designing efficient tick control strategies. With the goal of unravelling tick abundance determinants and informing new tick management strategies, we estimated tick burdens on 1965 wild ungulates in Doñana National Park, Spain, annually between 2010 and 2020. Under the hypothesis of a predominant host influence on tick abundance, we modelled the burdens of Rhipicephalus annulatus, Hyalomma lusitanicum, and Ixodes ricinus with relevant predictors grouped into four factors: i) environment; ii) host population; iii) host individual; and iv) land-use. Generalized linear mixed models with a zero-inflated negative binomial distribution were built. Additionally, we analysed the differential contribution to abundance of each factor by deviance partitioning. We finally estimated the similarity in the environmental space of tick species by analysing their niche overlap with the environmental principal component analysis method. Our work hypothesis was confirmed for R. annulatus and H. lusitanicum, but we found that tick abundance at a fine spatial scale is jointly driven by multiple drivers, including all four factors considered in this study. This result points out that understanding the demography of ticks is a complex multifactorial issue, even at small spatial scales. We found no niche differences between the three tick species at the study spatial scale, thus showing similar host and environmental dependencies. Overall results identify that host aggregation areas displaying environmentally favourable traits for ticks are relevant tick and vector-borne pathogen transmission hotspots. Our findings will facilitate the design of new strategies to reduce the negative effects of tick parasitism.