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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., Competing Interests: The authors have declared that no competing interests exist.

Aedes aegypti and Ae. albopictus are the main vectors transmitting dengue and chikungunya viruses. Despite being pathogens of global public health importance, knowledge of their vectors' global distribution remains patchy and sparse. A global geographic database of known occurrences of Ae. aegypti and Ae. albopictus between 1960 and 2014 was compiled. Herein we present the database, which comprises occurrence data linked to point or polygon locations, derived from peer-reviewed literature and unpublished studies including national entomological surveys and expert networks. We describe all data collection processes, as well as geo-positioning methods, database management and quality-control procedures. This is the first comprehensive global database of Ae. aegypti and Ae. albopictus occurrence, consisting of 19,930 and 22,137 geo-positioned occurrence records respectively. Both datasets can be used for a variety of mapping and spatial analyses of the vectors and, by inference, the diseases they transmit.

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.

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 VECMAP™ software.

Bluetongue (BT) is a commonly cited example of a disease with a distribution believed to have recently expanded in response to global warming. The BT virus is transmitted to ruminants by biting midges of the genus Culicoides, and it has been hypothesized that the emergence of BT in Mediterranean Europe during the last two decades is a consequence of the recent colonization of the region by Culicoides imicola and linked to climate change. To better understand the mechanism responsible for the northward spread of BT, we tested the hypothesis of a recent colonization of Italy by C. imicola, by obtaining samples from more than 60 localities across Italy, Corsica, Southern France, and Northern Africa (the hypothesized source point for the recent invasion of C. imicola), and by genotyping them with 10 newly identified microsatellite loci. The patterns of genetic variation within and among the sampled populations were characterized and used in a rigorous approximate Bayesian computation framework to compare three competing historical hypotheses related to the arrival and establishment of C. imicola in Italy. The hypothesis of an ancient presence of the insect vector was strongly favoured by this analysis, with an associated P ≥ 99%, suggesting that causes other than the northward range expansion of C. imicola may have supported the emergence of BT in southern Europe. Overall, this study illustrates the potential of molecular genetic markers for exploring the assumed link between climate change and the spread of diseases., (© 2013 Blackwell Publishing Ltd.)

Background: Culicoides imicola KIEFFER, 1913 (Diptera: Ceratopogonidae) is the principal vector of Bluetongue disease in the Mediterranean basin, Africa and Asia. Previous studies have identified a range of eco-climatic variables associated with the distribution of C. imicola, and these relationships have been used to predict the large-scale distribution of the vector. However, these studies are not temporally-explicit and can not be used to predict the seasonality in C. imicola abundances. Between 2001 and 2006, longitudinal entomological surveillance was carried out throughout Italy, and provided a comprehensive spatio-temporal dataset of C. imicola catches in Onderstepoort-type black-light traps, in particular in Sardinia where the species is considered endemic., Methods: We built a dynamic model that allows describing the effect of eco-climatic indicators on the monthly abundances of C. imicola in Sardinia. Model precision and accuracy were evaluated according to the influence of process and observation errors., Results: A first-order autoregressive cofactor, a digital elevation model and MODIS Land Surface Temperature (LST)/or temperatures acquired from weather stations explained ~77% of the variability encountered in the samplings carried out in 9 sites during 6 years. Incorporating Normalized Difference Vegetation Index (NDVI) or rainfall did not increase the model's predictive capacity. On average, dynamics simulations showed good accuracy (predicted vs. observed r corr = 0.9). Although the model did not always reproduce the absolute levels of monthly abundances peaks, it succeeded in reproducing the seasonality in population level and allowed identifying the periods of low abundances and with no apparent activity. On that basis, we mapped C. imicola monthly distribution over the entire Sardinian region., Conclusions: This study demonstrated prospects for modelling data arising from Culicoides longitudinal entomological surveillance. The framework explicitly incorporates the influence of eco-climatic factors on population growth rates and accounts for observation and process errors. Upon validation, such a model could be used to predict monthly population abundances on the basis of environmental conditions, and hence can potentially reduce the amount of entomological surveillance.

In recent years the vector-borne diseases (VBD) are (re)-emerging and spreading across the world having a profound impact on human and veterinary health, ecology, socio-economics and disease management. Arguably the best-documented example of veterinary importance is the recent twofold invasion of bluetongue (BT) in Europe. Much attention has been devoted to derive presence-absence habitat distribution models and to model transmission through direct contact. Limited research has focused on the dynamic modelling of wind mediated BT spread. This paper shows the results of a stochastic predictive model used to assess the spread of bluetongue by vectors considering both wind-independent and wind-mediated movement of the vectors. The model was parameterised using epidemiological knowledge from the BTV8 epidemic in 2006/2007 and the BTV1 epidemic in 2008 in South-France. The model correctly reflects the total surface of the infected zone (overall accuracy=0.77; sensitivity=0.94; specificity=0.65) whilst slightly overestimating spatial case density. The model was used operationally in spring 2009 to predict further spread of BTV1. This allowed veterinary officers in Belgium to decide whether there was a risk of introduction of BTV1 from France into Belgium and thus, whether there was a need for vaccination. Given the far distance from the predicted infected zone to the Belgian border, it was decided not to vaccinate against BTV1 in 2009 in Belgium., (Copyright © 2011 Elsevier B.V. All rights reserved.)

The distribution, prevalence and impact of vector-borne diseases are often affected by anthropogenic environmental changes that alter the interactions between the host, the parasite and the vector. In the case of tsetse-transmitted livestock trypanosomiasis these changes are a result of the encroachment of people and their livestock into tsetse-infected wild areas. This has created a sequence of new epidemiological settings that is changing the relative importance of the domestic or sylvatic trypanosome transmission cycles and is causing concomitant changes in the impact of the disease on livestock. These changes in the dynamics of the epidemiology have an important impact on the factors that need to be considered when developing area-specific strategies for the future management of tsetse-transmitted livestock trypanosomiasis.

Increased transport and trade as well as climate shifts play an important role in the introduction, establishment and spread of new pathogens. Arguably, the introduction of bluetongue virus (BTV) serotype 8 in Benelux, Germany and France in 2006 is such an example. After its establishment in receptive local vector and host populations the continued spread of such a disease in a suitable environment will mainly depend on movement of infected vectors and animals. In this paper we explore how wind models can contribute to explain the spread of BTV in a temperate eco-climatic setting. Based on previous work in Greece and Bulgaria filtered wind density maps were computed using data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Six hourly forward wind trajectories were computed at pressure levels of 850 hPa for each infected farm as from the recorded onset of symptoms. The trajectories were filtered to remove wind events that do not contribute to possible spread of the vector. The suitable wind events were rastered and aggregated on a weekly basis to obtain weekly wind density maps. Next to this, cumulated wind density maps were also calculated to assess the overall impact of wind dispersal of vectors. A strong positive correlation was established between wind density data and the horizontal asymmetrical spread pattern of the 2006 BTV8 epidemic. It was shown that short (<5 km), medium (5-31 km) and long (>31 km) distance spread had a different impact on disease spread. Computed wind densities were linked to the medium/long-distance spread whilst short range spread was mainly driven by active Culicoides flight. Whilst previous work in the Mediterranean basin showed that wind driven spread of Culicoides over sea occurred over distances of up to 700 km, this phenomenon was not observed over land. Long-distance spread over land followed a hopping pattern, i.e. with intermediary stops and establishment of local virus circulation clusters at distances of 35-85 km. Despite suitable wind densities, no long range spread was recorded over distances of 300-400 km. Factors preventing spread Eastwards to the UK and Northwards to Denmark during the 2006 epidemic are discussed. Towards the east both elevation and terrain roughness, causing air turbulences and drop down of Culicoides, were major factors restricting spread. It is concluded that the proposed approach opens new avenues for understanding the spread of vector-borne viruses in Europe. Future developments should take into consideration both physical and biological factors affecting spread.