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Abstract Background In Europe, canine babesiosis is most frequently caused by Babesia canis and Babesia vogeli, and occasionally by Babesia gibsoni.. In Germany, B. canis is recognized as endemic. The aims of this study were to assess how often Babesia spp. infections were diagnosed in a commercial laboratory in samples from dogs from Germany, and to evaluate potential risk factors for infection. Methods The database of the LABOKLIN laboratory was screened for Babesia spp.-positive polymerase chain reaction (PCR) tests for dogs for the period January 2007–December 2020. Sequencing was performed for positive tests from 2018 and 2019. Binary logistic regression analysis was performed to determine the effects of sex, season, and year of testing. Questionnaires were sent to the submitting veterinarians to obtain information on travel abroad, tick infestation, and ectoparasite prophylaxis of the respective dogs. Fisher’s exact test was used to calculate statistical significance and P

Abstract Background Ticks are responsible for transmitting several notable pathogens worldwide. Finland lies in a zone where two human-biting tick species co-occur: Ixodes ricinus and Ixodes persulcatus. Tick densities have increased in boreal regions worldwide during past decades, and tick-borne pathogens have been identified as one of the major threats to public health in the face of climate change. Methods We used species distribution modelling techniques to predict the distributions of I. ricinus and I. persulcatus, using aggregated historical data from 2014 to 2020 and new tick occurrence data from 2021. By aiming to fill the gaps in tick occurrence data, we created a new sampling strategy across Finland. We also screened for tick-borne encephalitis virus (TBEV) and Borrelia from the newly collected ticks. Climate, land use and vegetation data, and population densities of the tick hosts were used in various combinations on four data sets to estimate tick species’ distributions across mainland Finland with a 1-km resolution. Results In the 2021 survey, 89 new locations were sampled of which 25 new presences and 63 absences were found for I. ricinus and one new presence and 88 absences for I. persulcatus. A total of 502 ticks were collected and analysed; no ticks were positive for TBEV, while 56 (47%) of the 120 pools, including adult, nymph, and larva pools, were positive for Borrelia (minimum infection rate 11.2%, respectively). Our prediction results demonstrate that two combined predictor data sets based on ensemble mean models yielded the highest predictive accuracy for both I. ricinus (AUC = 0.91, 0.94) and I. persulcatus (AUC = 0.93, 0.96). The suitable habitats for I. ricinus were determined by higher relative humidity, air temperature, precipitation sum, and middle-infrared reflectance levels and higher densities of white-tailed deer, European hare, and red fox. For I. persulcatus, locations with greater precipitation and air temperature and higher white-tailed deer, roe deer, and mountain hare densities were associated with higher occurrence probabilities. Suitable habitats for I. ricinus ranged from southern Finland up to Central Ostrobothnia and North Karelia, excluding areas in Ostrobothnia and Pirkanmaa. For I. persulcatus, suitable areas were located along the western coast from Ostrobothnia to southern Lapland, in North Karelia, North Savo, Kainuu, and areas in Pirkanmaa and Päijät-Häme. Conclusions This is the first study conducted in Finland that estimates potential tick species distributions using environmental and host data. Our results can be utilized in vector control strategies, as supporting material in recommendations issued by public health authorities, and as predictor data for modelling the risk for tick-borne diseases.

The MODIRISK project studied mosquito biodiversity and monitored and predicted biodiversity changes, to actively prepare to address issues of biodiversity change, especially invasive species and new pathogen risks. This work is essential given continuing global changes that may create suitable conditions for invasive species spread and the (re-)emergence of vector-borne diseases in Europe. Key strengths of MODIRISK, in the context of sustainable development, were the links between biodiversity and health and the environment, and its contribution to the development of tools for describing the spatial distribution of mosquito biodiversity. MODIRISK addressed key topics of the global Diversitas initiative, which was a main driver of the Belspo ‘Science for a Sustainable Development’ research program. Three different MODIRISK datasets were published in the Global Biodiversity Information Facility (GBIF): the Collection dataset (the Culicidae collection of the Museum of Natural History in Brussels); the Inventory dataset (data from the MODIRISK inventory effort); and the Longitudinal dataset (experiment data used for risk assessments).

Public and animal health authorities face many challenges in surveillance and control of vector-borne diseases. Those challenges are principally due to the multitude of interactions between vertebrate hosts, pathogens, and vectors in continuously changing environments. VectorNet, a joint project of the European Food Safety Authority (EFSA) and the European Centre for Disease Prevention and Control (ECDC) facilitates risk assessments of VBD threats through the collection, mapping and sharing of distribution data for ticks, mosquitoes, sand flies, and biting midges that are vectors of pathogens of importance to animal and/or human health in Europe. We describe the development and maintenance of this One Health network that celebrated its 10th anniversary in 2020 and the value of its most tangible outputs, the vector distribution maps, that are freely available online and its raw data on request. VectorNet encourages usage of these maps by health professionals and participation, sharing and usage of the raw data by the network and other experts in the science community. For the latter, a more complete technical description of the mapping procedure will be submitted elsewhere.

Anaplasma phagocytophilum is an obligate intracellular bacterium that causes granulocytic anaplasmosis in domestic animals, wildlife, and humans and is primarily transmitted by ticks of the Ixodes persulcatus complex. This retrospective study aims to determine the percentages of dogs that tested positive for A. phagocytophilum in Germany. It included the results of direct (polymerase chain reaction [PCR]) and indirect (immunofluorescence antibody test [IFAT], antibody-enzyme-linked immunosorbent assay [ELISA]) detection methods performed in the laboratory LABOKLIN on canine samples provided by German veterinarians from 2008 to 2020. Out of a total of 27,368 dogs tested by PCR, 1332 (4.9%) tested positive, while 24,720 (27.4%) of the 90,376 dogs tested by IFAT/ELISA had positive serology. High rates of positive PCR results were observed in months with known peaks in vector activity, showing that the dynamics of A. phagocytophilum infections in dogs in Germany are consistent with vector activity. In dogs with a positive PCR result, peaks in serology could be observed four weeks after initial testing. Male and senior dogs had higher rates of positive serology. A possible impact of environmental factors such as changes in climate should be investigated further. Overall, the upward trend in positive test results over the years indicates that canine granulocytic anaplasmosis will continue to become increasingly important for veterinary medicine.

Background Tsetse flies are the major vectors of human trypanosomiasis of the form Trypanosoma brucei rhodesiense and T.b.gambiense. They are widely spread across the sub-Saharan Africa and rendering a lot of challenges to both human and animal health. This stresses effective agricultural production and productivity in Africa. Delimiting the extent and magnitude of tsetse coverage has been a challenge over decades due to limited resources and unsatisfactory technology. In a bid to overcome these limitations, this study attempted to explore modelling skills that can be applied to spatially estimate tsetse abundance in the country using limited tsetse data and a set of remote-sensed environmental variables. Methodology Entomological data for the period 2008–2018 as used in the model were obtained from various sources and systematically assembled using a structured protocol. Data harmonisation for the purposes of responsiveness and matching was carried out. The key tool for tsetse trapping was itemized as pyramidal trap in many instances and biconical trap in others. Based on the spatially explicit assembled data, we ran two regression models; standard Poisson and Zero-Inflated Poisson (ZIP), to explore the associations between tsetse abundance in Uganda and several environmental and climatic covariates. The covariate data were constituted largely by satellite sensor data in form of meteorological and vegetation surrogates in association with elevation and land cover data. We finally used the Zero-Inflated Poisson (ZIP) regression model to predict tsetse abundance due to its superiority over the standard Poisson after model fitting and testing using the Vuong Non-Nested statistic. Results A total of 1,187 tsetse sampling points were identified and considered as representative for the country. The model results indicated the significance and level of responsiveness of each covariate in influencing tsetse abundance across the study area. Woodland vegetation, elevation, temperature, rainfall, and dry season normalised difference vegetation index (NDVI) were important in determining tsetse abundance and spatial distribution at varied scales. The resultant prediction map shows scaled tsetse abundance with estimated fitted numbers ranging from 0 to 59 flies per trap per day (FTD). Tsetse abundance was found to be largest at low elevations, in areas of high vegetative activity, in game parks, forests and shrubs during the dry season. There was very limited responsiveness of selected predictors to tsetse abundance during the wet season, matching the known fact that tsetse disperse most significantly during wet season. Conclusions A methodology was advanced to enable compilation of entomological data for 10 years, which supported the generation of tsetse abundance maps for Uganda through modelling. Our findings indicate the spatial distribution of the G. f. fuscipes as; low 0–5 FTD (48%), medium 5.1–35 FTD (18%) and high 35.1–60 FTD (34%) grounded on seasonality. This approach, amidst entomological data shortages due to limited resources and absence of expertise, can be adopted to enable mapping of the vector to provide better decision support towards designing and implementing targeted tsetse and tsetse-transmitted African trypanosomiasis control strategies. Author summary Tsetse flies are the major vectors of human and animal trypanosomiasis. To support control efforts, there is need to understand the location of these vectors in terms of distribution and abundance. Due to limited resources and unsatisfactory technology, delimiting the extent and magnitude of tsetse coverage remains a challenge. In a bid to overcome these limitations, this study attempted to explore modelling skills that can be applied to inform decision makers about the status of tsetse abundance using limited historical tsetse data and a set of remote-sensed environmental variables. Two regression models; standard Poisson and Zero-Inflated Poisson (ZIP) were fitted and evaluated for superiority. The results indicated the level of responsiveness of each covariate in influencing the vector across the study area. Tsetse abundance was found to be largest at low elevations in areas of high vegetative activity, in game parks, forests and permanently flooded shrubs during the dry season. This approach can be adopted to enable mapping of any tsetse species to provide better decision support towards designing and implementing targeted tsetse and tsetse-transmitted African trypanosomiasis control strategies amidst entomological data shortages due to limited resources and absence of expertise.

Aedes albopictus is a known vector of dengue and chikungunya. Understanding the population dynamics characteristics of vector species is of pivotal importance to optimise surveillance and control activities, to estimate risk for pathogen-transmission, and thus to enhance support of public health decisions. In this paper we used a seasonal activity model to simulate the start (spring hatching) and end (autumn diapause) of the vector season. In parallel, the peak abundance of the species was assessed using both VectorNet field survey data complemented with field studies obtained from literature across the Mediterranean Basin. Our results suggest that spring hatching of eggs in the current distribution area can start at the beginning of March in southern Europe and in April in western Europe. In northern Europe, where the species is not (yet) present, spring hatching would occur from late April to late May. Aedes albopictus can remain active up to 41 weeks in southern Europe whilst the climatic conditions in northern Europe are limiting its potential activity to a maximum of 23 weeks. The peak of egg density is found during summer months from end of July until end of September. During these two months the climatic conditions for species development are optimal, which implies a higher risk for arbovirus transmission by Ae. albopictus and occurrence of epidemics.

Abstract Background Aedes-borne diseases as dengue, zika, chikungunya and yellow fever are an emerging problem worldwide, being transmitted by Aedes aegypti and Aedes albopictus. Lack of up to date information about the distribution of Aedes species hampers surveillance and control. Global databases have been compiled but these did not capture data in the WHO Eastern Mediterranean Region (EMR), and any models built using these datasets fail to identify highly suitable areas where one or both species may occur. The first objective of this study was therefore to update the existing Ae. aegypti (Linnaeus, 1762) and Ae. albopictus (Skuse, 1895) compendia and the second objective was to generate species distribution models targeted to the EMR. A final objective was to engage the WHO points of contacts within the region to provide feedback and hence validate all model outputs. Methods The Ae. aegypti and Ae. albopictus compendia provided by Kraemer et al. (Sci Data 2:150035, 2015; Dryad Digit Repos, 2015) were used as starting points. These datasets were extended with more recent species and disease data. In the next step, these sets were filtered using the Köppen–Geiger classification and the Mahalanobis distance. The occurrence data were supplemented with pseudo-absence data as input to Random Forests. The resulting suitability and maximum risk of establishment maps were combined into hard-classified maps per country for expert validation. Results The EMR datasets consisted of 1995 presence locations for Ae. aegypti and 2868 presence locations for Ae. albopictus. The resulting suitability maps indicated that there exist areas with high suitability and/or maximum risk of establishment for these disease vectors in contrast with previous model output. Precipitation and host availability, expressed as population density and night-time lights, were the most important variables for Ae. aegypti. Host availability was the most important predictor in case of Ae. albopictus. Internal validation was assessed geographically. External validation showed high agreement between the predicted maps and the experts’ extensive knowledge of the terrain. Conclusion Maps of distribution and maximum risk of establishment were created for Ae. aegypti and Ae. albopictus for the WHO EMR. These region-specific maps highlighted data gaps and these gaps will be filled using targeted monitoring and surveillance. This will increase the awareness and preparedness of the different countries for Aedes borne diseases.

A harmonized sampling approach in combination with spatial modelling is required to update current knowledge of fasciolosis in dairy cattle in Europe. Within the scope of the EU project GLOWORM, samples from 3,359 randomly selected farms in 849 municipalities in Belgium, Germany, Ireland, Poland and Sweden were collected and their infection status assessed using an indirect bulk tank milk (BTM) enzyme-linked immunosorbent assay (ELISA). Dairy farms were considered exposed when the optical density ratio (ODR) exceeded the 0.3 cut-off. Two ensemble-modelling techniques, Random Forests (RF) and Boosted Regression Trees (BRT), were used to obtain the spatial distribution of the probability of exposure to Fasciola hepatica using remotely sensed environmental variables (1-km spatial resolution) and interpolated values from meteorological stations as predictors. The median ODRs amounted to 0.31, 0.12, 0.54, 0.25 and 0.44 for Belgium, Germany, Ireland, Poland and southern Sweden, respectively. Using the 0.3 threshold, 571 municipalities were categorized as positive and 429 as negative. RF was seen as capable of predicting the spatial distribution of exposure with an area under the receiver operation characteristic (ROC) curve (AUC) of 0.83 (0.96 for BRT). Both models identified rainfall and temperature as the most important factors for probability of exposure. Areas of high and low exposure were identified by both models, with BRT better at discriminating between low-probability and high-probability exposure; this model may therefore be more useful in practise. Given a harmonized sampling strategy, it should be possible to generate robust spatial models for fasciolosis in dairy cattle in Europe to be used as input for temporal models and for the detection of deviations in baseline probability. Further research is required for model output in areas outside the eco-climatic range investigated.

Fasciolosis is generally a subclinical infection of dairy cows and can cause marked economic losses. This study investigated the prevalence and spatial distribution of fasciolosis in dairy cow herds in Ireland using an in-house antibodydetection enzyme-linked immunosorbent assay applied to bulk tank milk (BTM) samples collected during the autumn of 2012. A total of 5,116 BTM samples were collected from 4,602 different herds, with 514 farmers submitting BTM samples in two consecutive months. Analysis of the BTM samples showed that 82% (n = 3,764) of the dairy herds had been exposed to Fasciola hepatica. A total of 108 variables, including averaged climatic data for the period 1981-2010 and contemporary meteorological data for the year 2012, such as soil, subsoil, land cover and habitat maps, were investigated for a possible role as predictor of fasciolosis. Using mainly climatic variables as the major predictors, a model of the predicted risk of fasciolosis was created by Random Forest modelling that had 95% sensitivity and 100% specificity. The most important predictors in descending order of importance were: average of annual total number of rain-days for the period 1981-2010, total rainfall during September, winter and autumn of 2012, average of annual total number of wet-days for the period 1981- 2010 and annual mean temperature of 2012. The findings of this study confirm the high prevalence of fasciolosis in Irish dairy herds and suggest that specific weather and environmental risk factors support a robust and precise distribution model.

Abstract Background The trematode parasite Fasciola hepatica causes important economic losses in ruminants worldwide. Current spatial distribution models do not provide sufficient detail to support farm-specific control strategies. A technology to reliably assess the spatial distribution of intermediate host snail habitats on farms would be a major step forward to this respect. The aim of this study was to conduct a longitudinal field survey in Flanders (Belgium) to (i) characterise suitable small water bodies (SWB) for Galba truncatula and (ii) describe the population dynamics of G. truncatula. Methods Four F. hepatica-infected farms from two distinct agricultural regions were examined for the abundance of G. truncatula from the beginning (April 2012) until the end (November 2012) of the grazing season. Per farm, 12 to 18 SWB were selected for monthly examination, using a 10 m transect analysis. Observations on G. truncatula abundance were coupled with meteorological and (micro-)environmental factors and the within-herd prevalence of F. hepatica using simple comparison or negative binomial regression models. Results A total of 54 examined SWB were classified as a pond, ditch, trench, furrow or moist area. G. truncatula abundance was significantly associated with SWB-type, region and total monthly precipitation, but not with monthly temperature. The clear differences in G. truncatula abundance between the 2 studied regions did not result in comparable differences in F. hepatica prevalence in the cattle. Exploration of the relationship of G. truncatula abundance with (micro)-environmental variables revealed a positive association with soil and water pH and the occurrence of Ranunculus sp. and a negative association with mowed pastures, water temperature and presence of reed-like plant species. Conclusions Farm-level predictions of G. truncatula risk and subsequent risk for F. hepatica occurrence would require a rainfall, soil type (representing the agricultural region) and SWB layer in a geographic information system. While rainfall and soil type information is easily accessible, the recent advances in very high spatial resolution cameras carried on board of satellites, planes or drones should allow the delineation of SWBs in the future.

Culicoides imicola is the main vector of the bluetongue virus in the Mediterranean Basin. Spatial distribution models for this species traditionally employ either climatic data or remotely sensed data, or a combination of both. Until now, however, no studies compared the accuracies of C. imicola distribution models based on climatic versus remote sensing data, even though remotely sensed datasets may offer advantages over climatic datasets with respect to spatial and temporal resolution. This study performs such an analysis for datasets over the peninsula of Calabria, Italy. Spatial distribution modelling based on climatic data using the random forests machine learning technique resulted in a percentage of correctly classified C. imicola trapping sites of nearly 88%, thereby outperforming the linear discriminant analysis and logistic regression modelling techniques. When replacing climatic data by remote sensing data, random forests modelling accuracies decreased only slightly. Assessment of the different variables’ importance showed that precipitation during late spring was the most important amongst 48 climatic variables. The dominant remotely sensed variables could be linked to climatic variables. Notwithstanding the slight decrease in predictive performance in this study, remotely sensed datasets could be preferred over climatic datasets for the modelling of C. imicola. Unlike climatic observations, remote sensing provides an equally high spatial resolution globally. Additionally, its high temporal resolution allows for investigating changes in species’ presence and changing environment.

To assess the risk of (zoonotic) disease transmission in developing countries, decision makers generally rely on distribution estimates of animals from survey records or projections of historical enumeration results. Given the high cost of large-scale surveys, the sample size is often restricted and the accuracy of estimates is therefore low, especially when spatial high-resolution is applied. This study explores possibilities of improving the accuracy of livestock distribution maps without additional samples using spatial modelling based on regression tree forest models, developed using subsets of the Uganda 2008 Livestock Census data, and several covariates. The accuracy of these spatial models as well as the accuracy of an ensemble of a spatial model and direct estimate was compared to direct estimates and “true” livestock figures based on the entire dataset. The new approach is shown to effectively increase the livestock estimate accuracy (median relative error decrease of 0.166-0.037 for total sample sizes of 80-1,600 animals, respectively). This outcome suggests that the accuracy levels obtained with direct estimates can indeed be achieved with lower sample sizes and the multimodel approach presented here, indicating a more efficient use of financial resources.

Mapping and modelling helminth infections in cattle and sheep in Europe through advanced geospatial research was one of the main task of GLOWORM, a three year project (2012-2014) funded under the European Commission’s (EC) seventh framework programme (FP7). Liver flukes as Fasciola hepatica and gastrointestinal nematodes, such as Haemonchus contortus were chosen for the project since these parasites constitute a major cause of lost productivity in small and large ruminants. The output of the GLOWORM project delivered guidelines for standardized and harmonized cross-sectional surveys of helminth parasites in ruminants allowing the development of updated prevalence maps and multi-scale, spatial models for the European area.

In this paper we present a novel methodology applied in Spain to model spatial abundance patterns of potential vectors of disease at a medium spatial resolution of 5 x 5 km using a countrywide database with abundance data for five Culicoides species, random regression Forest modelling and a spatial dataset of ground measured and remotely sensed eco-climatic and environmental predictor variables. First the probability of occurrence was computed. In a second step a direct regression between the probability of occurrence and trap abundance was established to verify the linearity of the relationship. Finally the probability of occurrence was used in combination with the set of predictor variables to model abundance. In each case the variable importance of the predictors was used to biologically interpret results and to compare both model outputs, and model performance was assessed using four different accuracy measures. Results are shown for C. imicola, C. newsteadii, C. pulicaris group, C. punctatus and C. obsoletus group. In each case the probability of occurrence is a good predictor of abundance at the used spatial resolution of 5 x 5 km. In addition, the C. imicola and C. obsoletus group are highly driven by summer rainfall. The spatial pattern is inverse between the two species, indicating that the lower and upper thresholds are different. C. pulicaris group is mainly driven by temperature. The patterns for C. newsteadii and C. punctatus are less clear. It is concluded that the proposed methodology can be used as an input to transmission-infection-recovery (TIR) models and R0 models. The methodology will become available to the general public as part of the VECMAPTM software.

Infections with parasitic helminths (nematodes and trematodes) represent a significant economic and welfare burden to the global ruminant livestock industry. The increasing prevalence of anthelmintic resistance means that current control programmes are costly and unsustainable in the long term. Recent changes in the epidemiology, seasonality and geographic distribution of helminth infections have been attributed to climate change. However, other changes in environment (e.g., land use) and in livestock farming, such as intensification and altered management practices, will also have an impact on helminth infections. Sustainable control of helminth infections in a changing world requires detailed knowledge of these interactions. In particular, there is a need to devise new, sustainable strategies for the effective control of ruminant helminthoses in the face of global change. In this paper, we consider the impact of helminth infections in grazing ruminants, taking a European perspective, and identify scientific and applied priorities to mitigate these impacts. These include the development and deployment of efficient, high-throughput diagnostic tests to support targeted intervention, modelling of geographic and seasonal trends in infection, more thorough economic data and analysis of the impact of helminth infections and greater translation and involvement of end-users in devising and disseminating best practices. Complex changes in helminth epidemiology will require innovative solutions. By developing and using new technologies and models, the use of anthelmintics can be optimised to limit the development and spread of drug resistance and to reduce the overall economic impact of helminth infections. This will be essential to the continued productivity and profitability of livestock farming in Europe and its contribution to regional and global food security.

In South Africa, communal livestock farming is predominant in the foot and mouth disease control zone adjacent to the Greater Kruger National Park (KNP), where infected African buffaloes are common. During routine veterinary inspections of cattle in this area, a large amount of production and demographic parameters were being recorded. These data were collated for a five-year period (2003-2007) in three study sites to better understand the temporal dynamics and spatial heterogeneity in this system. A decreasing gradient from South to North with respect to both human and cattle population densities was observed. Rainfall and human population density alone could explain 71% of the variation in cattle density. Northern and central sites showed an overall decrease in total cattle numbers (15.1 and 2.9%, respectively), whereas a 28.6% increase was recorded in the South. The number of cattle owners in relation to cattle numbers remained stable during the study period. Only 4.0% of households in the South own cattle, compared to 13.7 and 12.7% in the North and Centre. The overall annual calving rate was 23.8%. Annual mortality rates ranged from 2.4 to 3.2%. Low calf mortality (2.1%) was recorded in the North compared to the South (11.6%). Annual off-take in the form of slaughter averaged 0.2, 11.7, and 11.0% in the North, Central and South sites, respectively. These figures provide valuable baseline data and demonstrate considerable spatial heterogeneity in cattle demography and production at this wildlife-livestock interface, which should be taken into consideration when performing disease risk assessments or designing disease control systems.

Animal distribution maps serve many purposes such as estimating transmission risk of zoonotic pathogens to both animals and humans. The reliability and usability of such maps is highly dependent on the quality of the input data. However, decisions on how to perform livestock surveys are often based on previous work without considering possible consequences. A better understanding of the impact of using different sample designs and processing steps on the accuracy of livestock distribution estimates was acquired through iterative experiments using detailed survey. The importance of sample size, sample design and aggregation is demonstrated and spatial interpolation is presented as a potential way to improve cattle number estimates. As expected, results show that an increasing sample size increased the precision of cattle number estimates but these improvements were mainly seen when the initial sample size was relatively low (e.g. a median relative error decrease of 0.04% per sampled parish for sample sizes below 500 parishes). For higher sample sizes, the added value of further increasing the number of samples declined rapidly (e.g. a median relative error decrease of 0.01% per sampled parish for sample sizes above 500 parishes. When a two-stage stratified sample design was applied to yield more evenly distributed samples, accuracy levels were higher for low sample densities and stabilised at lower sample sizes compared to one-stage stratified sampling. Aggregating the resulting cattle number estimates yielded significantly more accurate results because of averaging under- and over-estimates (e.g. when aggregating cattle number estimates from subcounty to district level, P

In order to improve the spatial resolution of current risk maps for fasciolosis in cattle, more knowledge is needed with respect to farm-level factors that determine infection risk. In this study, we visited 39 dairy farms within a predefined low- and high-risk area for fasciolosis in Belgium and assessed their infection status by an indirect bulk tank milk enzyme-linked immunosorbent assay (ELISA). Management factors were collected and all pastured lands of the farms were visited to identify and georeference potential snail habitats. The habitats were visually characterised, investigated for the presence of the intermediate host snails of Fasciola hepatica (i.e.Galba truncatula and Radix spp.) and used in a geographical information system (GIS) to construct overlays including information on soil and hydrology. A linear regression model was used to evaluate associations between bulk tank milk ELISA results and farm level management and habitat factors. A logistic, mixed model was used to identify possible risk factors for the presence of intermediate host snails on a potential habitat. Potential snail habitats were found in 35 out of 39 farms. A total of 87 potential habitats were identified and on 29% of these, intermediate host snails were found. The number of potential habitats, the presence of snails, drainage of pastures, month of turnout of the cows, stocking rate, type of watering place and risk area were significantly associated with the bulk tank milk ELISA result and explained 85% of the observed variation. Intermediate host snails were more likely to be present with increasing surface of the potential habitat and on loamy soils. This study confirms the importance of farm management factors in the infection risk for F. hepatica in cattle and highlights that the combination of management factors with characterization of snail habitats is a powerful means to predict the infection risk with F. hepatica at the individual farm level. Further research is needed to investigate how this knowledge can be incorporated in nation-wide spatial distribution models of the parasite.

Dengue and chikungunya are increasing global public health concerns due to their rapid geographical spread and increasing disease burden. Knowledge of the contemporary distribution of their shared vectors, Aedes aegypti and Aedes albopictus remains incomplete and is complicated by an ongoing range expansion fuelled by increased global trade and travel. Mapping the global distribution of these vectors and the geographical determinants of their ranges is essential for public health planning. Here we compile the largest contemporary database for both species and pair it with relevant environmental variables predicting their global distribution. We show Aedes distributions to be the widest ever recorded; now extensive in all continents, including North America and Europe. These maps will help define the spatial limits of current autochthonous transmission of dengue and chikungunya viruses. It is only with this kind of rigorous entomological baseline that we can hope to project future health impacts of these viruses.

The visualization of vector occurrence in space and time is an important aspect of studying vector-borne diseases. Detailed maps of possible vector habitats provide valuable information for the prediction of infection risk zones but are currently lacking for most parts of the world. Nonetheless, monitoring vector habitats from the finest scales up to farm level is of key importance to refine currently existing broad-scale infection risk models. Using Fasciola hepatica, a parasite liver fluke as a case in point, this study illustrates the potential of very high resolution (VHR) optical satellite imagery to efficiently and semi-automatically detect detailed vector habitats. A WorldView2 satellite image capable of

For the first time a Bayesian geostatistical version of the Moran Curve, a logarithmic form of the Ricker stock recruitment curve, is proposed that is able to give an estimate of net change in population demographic rates considering components such as fertility and density dependent and density independent mortalities. The method is applied to spatio-temporally referenced count data of tsetse flies obtained from fly-rounds. The model is a linear regression with three components: population rate of change estimated from the Moran curve, an explicit spatio-temporal covariance, and the observation error optimised within a Bayesian framework. The model was applied to the three main climate seasons of Zambia (rainy--January to April, cold-dry--May to August, and hot-dry--September to December) taking into account land surface temperature and (seasonally changing) cattle distribution. The model shows a maximum positive net change during the hot-dry season and a minimum between the rainy and cold-dry seasons. Density independent losses are correlated positively with day-time land surface temperature and negatively with night-time land surface temperature and cattle distribution. The inclusion of density dependent mortality increases considerably the goodness of fit of the model. Cross validation with an independent dataset taken from the same area resulted in a very accurate estimate of tsetse catches. In general, the overall framework provides an important tool for vector control and eradication by identifying vector population concentrations and local vector demographic rates. It can also be applied to the case of sustainable harvesting of natural populations.

A 0.125 degree raster or grid-based Geographic Information System with data on tsetse, trypanosomosis, animal production, agriculture and land use has recently been developed in Togo. This paper addresses the problem of generating tsetse distribution and abundance maps from remotely sensed data, using a restricted amount of field data. A discriminant analysis model is tested using contemporary tsetse data and remotely sensed, low resolution data acquired from the National Oceanographic and Atmospheric Administration and Meteosat platforms. A split sample technique is adopted where a randomly selected part of the field measured data (training set) serves to predict the other part (predicted set). The obtained results are then compared with field measured data per corresponding grid-square. Depending on the size of the training set the percentage of concording predictions varies from 80 to 95 for distribution figures and from 63 to 74 for abundance. These results confirm the potential of satellite data application and multivariate analysis for the prediction, not only of the tsetse distribution, but more importantly of their abundance. This opens up new avenues because satellite predictions and field data may be combined to strengthen or substitute one another and thus reduce costs of field surveys.

Des cas de sclérectasie ont été décrits dans un élevage porcin à Sotouboua, Togo. Le virus de la peste porcine pourrait en être l'origine, la consanguinité étant un facteur prédisposant. Les éleveurs doivent rester vigilants.