Simulating spread of Bluetongue Virus by flying vectors between hosts on pasture

Analysis of bluetongue disease epizootics in sheep of Andhra Pradesh, India using spatial and temporal autocorrelation

Bluetongue (BT) disease poses a constant risk to the livestock population around the world. A better understanding of the risk factors will enable a more accurate prediction of the place and time of high-risk events. Mapping the disease epizootics over a period in a particular geographic area will identify the spatial distribution of disease occurrence. A Geographical Information System (GIS) based methodology to analyze the relationship between bluetongue epizootics and spatial-temporal patterns was used for the years 2000 to 2015 in sheep of Andhra Pradesh, India. Autocorrelation (ACF), partial autocorrelation (PACF), and cross-correlation (CCF) analyses were carried out to find the self-dependency between BT epizootics and their dependencies on environmental factors and livestock population. The association with climatic or remote sensing variables at different months lag, including wind speed, temperature, rainfall, relative humidity, normalized difference vegetation index (NDVI), normalized difference water index (NDWI), land surface temperature (LST), was also examined. The ACF & PACF of BT epizootics with its lag showed a significant positive autocorrelation with a month's lag (r = 0.41). Cross-correlations between the environmental variables and BT epizootics indicated the significant positive correlations at 0, 1, and 2 month's lag of rainfall, relative humidity, normalized difference water index (NDWI), and normalized difference vegetation index (NDVI). Spatial autocorrelation analysis estimated the univariate global Moran's I value of 0.21. Meanwhile, the local Moran's I value for the year 2000 (r = 0.32) showed a high degree of spatial autocorrelation. The spatial autocorrelation analysis revealed that the BT epizootics in sheep are having considerable spatial association among the outbreaks in nearby districts, and have to be taken care of while making any forecasting or disease prediction with other risk factors.

A Practical Introduction to Mechanistic Modeling of Disease Transmission in Veterinary Science

Computer-based disease spread models are frequently used in veterinary science to simulate disease spread. They are used to predict the impacts of the disease, plan and assess surveillance, or control strategies, and provide insights about disease causation by comparing model outputs with real life data. There are many types of disease spread models, and here we present and describe the implementation of a particular type: individual-based models. Our aim is to provide a practical introduction to building individual-based disease spread models. We also introduce code examples with the goal to make these techniques more accessible to those who are new to the field. We describe the important steps in building such models before, during and after the programming stage, including model verification (to ensure that the model does what was intended), validation (to investigate whether the model results reflect the modeled system), and convergence analysis (to ensure models of endemic diseases are stable before outputs are collected). We also describe how sensitivity analysis can be used to assess the potential impact of uncertainty about model parameters. Finally, we provide an overview of some interesting recent developments in the field of disease spread models.

Prediction Models in Veterinary and Human Epidemiology: Our Experience With Modeling Sars-CoV-2 Spread

The worldwide outbreak of Sars-CoV-2 resulted in modelers from diverse fields being called upon to help predict the spread of the disease, resulting in many new collaborations between different institutions. We here present our experience with bringing our skills as veterinary disease modelers to bear on the field of human epidemiology, building models as tools for decision makers, and bridging the gap between the medical and veterinary fields. We describe and compare the key steps taken in modeling the Sars-CoV-2 outbreak: criteria for model choices, model structure, contact structure between individuals, transmission parameters, data availability, model validation, and disease management. Finally, we address how to improve on the contingency infrastructure available for Sars-CoV-2.

Assessing the potential of plains zebra to maintain African horse sickness in the Western Cape Province, South Africa

African horse sickness (AHS) is a disease of equids that results in a non-tariff barrier to the trade of live equids from affected countries. AHS is endemic in South Africa except for a controlled area in the Western Cape Province (WCP) where sporadic outbreaks have occurred in the past 2 decades. There is potential that the presence of zebra populations, thought to be the natural reservoir hosts for AHS, in the WCP could maintain AHS virus circulation in the area and act as a year-round source of infection for horses. However, it remains unclear whether the epidemiology or the ecological conditions present in the WCP would enable persistent circulation of AHS in the local zebra populations. Here we developed a hybrid deterministic-stochastic vector-host compartmental model of AHS transmission in plains zebra (Equus quagga), where host populations are age- and sex-structured and for which population and AHS transmission dynamics are modulated by rainfall and temperature conditions. Using this model, we showed that populations of plains zebra present in the WCP are not sufficiently large for AHS introduction events to become endemic and that coastal populations of zebra need to be >2500 individuals for AHS to persist >2 years, even if zebras are infectious for more than 50 days. AHS cannot become endemic in the coastal population of the WCP unless the zebra population involves at least 50,000 individuals. Finally, inland populations of plains zebra in the WCP may represent a risk for AHS to persist but would require populations of at least 500 zebras or show unrealistic duration of infectiousness for AHS introduction events to become endemic. Our results provide evidence that the risk of AHS persistence from a single introduction event in a given plains zebra population in the WCP is extremely low and it is unlikely to represent a long-term source of infection for local horses.

Quantifying the potential for bluetongue virus transmission in Danish cattle farms

We used a mechanistic transmission model to estimate the number of infectious bites (IBs) generated per bluetongue virus (BTV) infected host (cattle) using estimated hourly microclimatic temperatures at 22,004 Danish cattle farms for the period 2000–2016, and Culicoides midge abundance based on 1,453 light-trap collections during 2007–2016. We used a range of published estimates of the duration of the hosts’ infectious period and equations for the relationship between temperature and four key transmission parameters: extrinsic incubation period, daily vector survival rate, daily vector biting rate and host-to-vector transmission rate resulting in 147,456 combinations of daily IBs. More than 82% combinations of the parameter values predicted > 1 IBs per host. The mean IBs (10–90^th percentiles) for BTV per infectious host were 59 (0–73) during the transmission period. We estimated a maximum of 14,954 IBs per infectious host at some farms, while a best-case scenario suggested transmission was never possible at some farms. The use of different equations for the vector survival rate and host-to-vector transmission rates resulted in large uncertainty in the predictions. If BTV is introduced in Denmark, local transmission is very likely to occur. Vectors infected as late as mid-September (early autumn) can successfully transmit BTV to a new host until mid-November (late autumn).

The Role of Movement Restrictions in Limiting the Economic Impact of Livestock Infections

Movements are essential for the economic success of the livestock industry. These movements however bring the risk of long-range spread of infection, potentially bringing infection to previously disease-free areas where subsequent localised transmission can be devastating. Mechanistic predictive models usually consider controls that minimize the number of livestock affected without considering other costs of an ongoing epidemic. However, it is more appropriate to consider the economic burden, as movement restrictions have major consequences for the economic revenue of farms. Using mechanistic models of foot-and-mouth disease (FMD), bluetongue virus (BTV) and bovine tuberculosis (bTB) in the UK, we contrast the economically optimal control strategies for these diseases. We show that for FMD, the optimal strategy is to ban movements in a small radius around infected farms; the balance between disease control and maintaining 'business as usual' varies between regions. For BTV and bTB, we find that the cost of any movement ban is more than the epidemiological benefits due to the low within-farm prevalence and slow rate of disease spread. This work suggests that movement controls need to be carefully matched to the epidemiological and economic consequences of the disease, and optimal movement bans are often far shorter than existing policy.

A network-based approach to modelling bluetongue spread in France

Bluetongue virus serotype 8 (BTV-8) was reported for the first time in Europe in 2006, causing the largest bluetongue outbreak ever recorded. France was mostly impacted in 2007/09. Trade restrictions were implemented all along. Vaccination became available from 2008: a limited number of doses was first administered in an emergency vaccination campaign, followed by two nationwide compulsory vaccination campaigns in 2009 and 2010. France regained a disease-free status in December 2012, but BTV may have kept circulating undetected as infected herds have been reported again since August 2015. We developed a stochastic dynamic compartmental model of BTV transmission in cattle and sheep to analyze the relative importance of vector active flight and host movements in disease spread, and assess the effectiveness of control measures. We represented BTV transmission both within and between French administrative subdivisions called cantons, during the 2007/09 outbreak and until the end of 2010, when compulsory vaccination was interrupted. Within-canton transmission was vector-borne, and between canton transmission could occur through three contact networks that accounted for movements of: (i) vectors between pastures located at close distance; (ii) cattle and sheep between pastures of the same farm; (iii) traded cattle. We estimated the model parameters by approximate Bayesian computation, using data from the 2007 French outbreak. With this framework, we were able to reproduce the BTV-8 epizootic wave. Host movements between distant pastures of the same farm were found to have a major contribution to BTV spread to disease-free areas, thus raising practical questions about herd management during outbreaks. We found that cattle trade restrictions had been well complied with; without them, the whole French territory would have been infected by winter 2007. The 2008 emergency vaccination campaign had little impact on disease spread as almost half vaccine doses had likely been administered to already immune cattle. Alternatively, establishing a vaccination buffer zone would have allowed a better control of BTV in 2008: limiting its spatial expansion and decreasing the number of infected cattle and sheep. We also showed a major role of compulsory vaccination in controlling the outbreak in 2009 and 2010, though we predicted a possible low-level circulation after the last detection.

Simulation of the Transmission by Vectors of Bluetongue Disease and Analysis of the Control Strategy

Bluetongue disease is an infectious non-contagious disease of domestic and wild ruminants, transmitted by hematophagous insects of the genus Culicoides. In endemic areas the disease has a seasonal character, occurs usually in summer when the population of vectors is at its peak. Culicoides are active at temperatures in the range from 13oto 35oC. The replication of the virus stops when the environmental temperature is below 13oC. It has been reported that the temperature and humidity of the environment affect to a great extent the biology of the vector and the survival of the virus in the reservoirs. During the summer, the number of infected cattle and sheep is directly dependent on the density of the population of the vector, the length of vectors’ life-span, the temperature of the environment and by precipitation, the affi nity of the vector to different hosts, and the ability of the vector to locate the host. Bluetongue has been spreading worldwide due to climatic changes and increasing average daily temperatures. The seasonal occurrences of the disease and the climate change have conditioned the need for adopting new strategies. The stochastic SEIRD mathematical model has been developed in order to simulate the transmission of the Bluetongue virus through the susceptible ruminant population on the territory of the Republic of Serbia, as well as to investigate the effect of climatic factors on the vector population and the magnitude of a possible epizootia. Besides the effects of climatic factors, we have analyzed a number of different approaches in the control of the disease based upon the vaccination of ruminants and control of vectors.

Bluetongue transmission and control in Europe: A systematic review of compartmental mathematical models

The growing frequency of bluetongue virus (BTV) incursions in Europe in recent years led to the largest BTV outbreak ever recorded in 2006/09, with a dramatic impact on the cattle and sheep industries. The complex epidemiology of this vector-borne disease of ruminants and its recent emergence need to be better understood to identify and implement efficient control strategies. Mathematical models provide useful tools for that purpose; many of them have been developed in the light of the 2006/09 outbreak. We aimed to provide a systematic review of compartmental mathematical models dedicated to BTV occurrence or transmission in European countries, to assess robustness of findings to different modelling approaches and assumptions. We identified relevant papers from PubMed and Scopus databases, 21 of which were included in the review following the selection process laid out in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. We systematically extracted data from these papers to address the diversity and evolution of modelling approaches, and to identify important characteristics for future model development. Then, we summarized the main insights provided into bluetongue epidemiology, and discussed the relevance of these models as tools for risk mapping and for the design of surveillance and control systems. On the whole, the mechanistic models reviewed provided flexible frameworks, yielding mostly epidemiological insights specific to geographical areas and study periods. Despite the limitations of these models that sometimes relied on strong assumptions, we advocate their use to facilitate and inform evidence-based decision-making in animal health.

The impact of temperature changes on vector-borne disease transmission: Culicoides midges and bluetongue virus

It is a long recognized fact that climatic variations, especially temperature, affect the life history of biting insects. This is particularly important when considering vector-borne diseases, especially in temperate regions where climatic fluctuations are large. In general, it has been found that most biological processes occur at a faster rate at higher temperatures, although not all processes change in the same manner. This differential response to temperature, often considered as a trade-off between onward transmission and vector life expectancy, leads to the total transmission potential of an infected vector being maximized at intermediate temperatures. Here we go beyond the concept of a static optimal temperature, and mathematically model how realistic temperature variation impacts transmission dynamics. We use bluetongue virus (BTV), under UK temperatures and transmitted by Culicoides midges, as a well-studied example where temperature fluctuations play a major role. We first consider an optimal temperature profile that maximizes transmission, and show that this is characterized by a warm day to maximize biting followed by cooler weather to maximize vector life expectancy. This understanding can then be related to recorded representative temperature patterns for England, the UK region which has experienced BTV cases, allowing us to infer historical transmissibility of BTV, as well as using forecasts of climate change to predict future transmissibility. Our results show that when BTV first invaded northern Europe in 2006 the cumulative transmission intensity was higher than any point in the last 50 years, although with climate change such high risks are the expected norm by 2050. Such predictions would indicate that regular BTV epizootics should be expected in the UK in the future.

Quantifying the roles of host movement and vector dispersal in the transmission of vector-borne diseases of livestock

The role of host movement in the spread of vector-borne diseases of livestock has been little studied. Here we develop a mathematical framework that allows us to disentangle and quantify the roles of vector dispersal and livestock movement in transmission between farms. We apply this framework to outbreaks of bluetongue virus (BTV) and Schmallenberg virus (SBV) in Great Britain, both of which are spread by Culicoides biting midges and have recently emerged in northern Europe. For BTV we estimate parameters by fitting the model to outbreak data using approximate Bayesian computation, while for SBV we use previously derived estimates. We find that around 90% of transmission of BTV between farms is a result of vector dispersal, while for SBV this proportion is 98%. This difference is a consequence of higher vector competence and shorter duration of viraemia for SBV compared with BTV. For both viruses we estimate that the mean number of secondary infections per infected farm is greater than one for vector dispersal, but below one for livestock movements. Although livestock movements account for a small proportion of transmission and cannot sustain an outbreak on their own, they play an important role in establishing new foci of infection. However, the impact of restricting livestock movements on the spread of both viruses depends critically on assumptions made about the distances over which vector dispersal occurs. If vector dispersal occurs primarily at a local scale (99% of transmission occurs <25 km), movement restrictions are predicted to be effective at reducing spread, but if dispersal occurs frequently over longer distances (99% of transmission occurs <50 km) they are not.

Diseases which are transmitted by the bites of insects can be spread to new locations through the movement of both infected insects and infected hosts. The importance of these routes has implications for disease control, because we can often restrict host movement, and so potentially reduce spread, but cannot easily restrict insect movements. Despite this, the importance of host movements has been little studied. Here we develop a mathematical model which allows us to disentangle and quantify transmission by insect dispersal and by host movement. We apply the model to two diseases of cattle and sheep transmitted by biting midges that have emerged in northern Europe in the past decade, bluetongue virus (BTV) and Schmallenberg virus (SBV). For both viruses, we show insect movements account for a majority of spread between farms. Although they cannot sustain an epidemic on their own, animal movements play an important role in introducing disease to new areas.

Mechanistic model for predicting the seasonal abundance of Culicoides biting midges and the impacts of insecticide control

BACKGROUND

Understanding seasonal patterns of abundance of insect vectors is important for optimisation of control strategies of vector-borne diseases. Environmental drivers such as temperature, humidity and photoperiod influence vector abundance, but it is not generally known how these drivers combine to affect seasonal population dynamics.

METHODS

In this paper, we derive and analyse a novel mechanistic stage-structured simulation model for Culicoides biting midges-the principle vectors of bluetongue and Schmallenberg viruses which cause mortality and morbidity in livestock and impact trade. We model variable life-history traits as functional forms that are dependent on environmental drivers, including air temperature, soil temperature and photoperiod. The model is fitted to Obsoletus group adult suction-trap data sampled daily at five locations throughout the UK for 2008.

RESULTS

The model predicts population dynamics that closely resemble UK field observations, including the characteristic biannual peaks of adult abundance. Using the model, we then investigate the effects of insecticide control, showing that control strategies focussing on the autumn peak of adult midge abundance have the highest impact in terms of population reduction in the autumn and averaged over the year. Conversely, control during the spring peak of adult abundance leads to adverse increases in adult abundance in the autumn peak.

CONCLUSIONS

The mechanisms of the biannual peaks of adult abundance, which are important features of midge seasonality in northern Europe and are key determinants of the risk of establishment and spread of midge-borne diseases, have been hypothesised over for many years. Our model suggests that the peaks correspond to two generations per year (bivoltine) are largely determined by pre-adult development. Furthermore, control strategies should focus on reducing the autumn peak since the immature stages are released from density-dependence regulation. We conclude that more extensive modelling of Culicoides biting midge populations in different geographical contexts will help to optimise control strategies and predictions of disease outbreaks.

Blue tongue - A modelling examination of fundamentals - Seasonality and chaos

A deterministic mathematical model is developed for the dynamics of bluetongue disease within a single farm. The purpose is to examine widely the possible behaviours which may occur. This is important because of the increasing impact of blue tongue due to global warming. The model incorporates a recently suggested modification of logistic growth for the vectors which can greatly affect early disease dynamics and employs a variable number of up to 10 sequential pools for incubating vectors and for incubating and infectious hosts. Ten sequential pools represent the possible loss of immunity of recovered hosts over a 3-year period. After formally describing the model, the impact of the two logistic growth scenarios considered is examined in Section 3.1. The scenarios are applied with parameters that give identical long-term consequences but the early dynamics can be greatly affected. In the two scenarios, the effect of varying the assumed constant birth rate (scenario 1) or constant mortality rates (scenario 2) is considered. If the recovered (and immune) hosts, are assumed to lose their immunity, then, given particular values of the host-vector coupling constants, the system can exhibit autonomous oscillations (Section 3.2). Seasonality is represented by air temperature, and it is assumed that air temperatures below a threshold can increase vector mortality (Section 3.3). Adding seasonal effects on mortality to the autonomous oscillations resulting from recovered and resistant hosts losing immunity can give rise to chaos (Section 3.4). This could help explain the unusual persistence and re-occurrence of the disease. Finally (Section 3.5), the roles of host birth and mortality rates in examined, particularly in relation to placental transmission of the virus to offspring. It is concluded that the latter does not make an appreciable contribution to disease dynamics.

The Interaction between Vector Life History and Short Vector Life in Vector-Borne Disease Transmission and Control

Epidemiological modelling has a vital role to play in policy planning and prediction for the control of vectors, and hence the subsequent control of vector-borne diseases. To decide between competing policies requires models that can generate accurate predictions, which in turn requires accurate knowledge of vector natural histories. Here we highlight the importance of the distribution of times between life-history events, using short-lived midge species as an example. In particular we focus on the distribution of the extrinsic incubation period (EIP) which determines the time between infection and becoming infectious, and the distribution of the length of the gonotrophic cycle which determines the time between successful bites. We show how different assumptions for these periods can radically change the basic reproductive ratio (R₀) of an infection and additionally the impact of vector control on the infection. These findings highlight the need for detailed entomological data, based on laboratory experiments and field data, to correctly construct the next-generation of policy-informing models.

The basic reproductive ratio (R₀) is a crucial measure of transmission intensity, lying at the interface between mathematical modelling and policy decision making. If control measures can induce a situation where R₀ ≤ 1 for a sustained period of time then the pathogen must be eradicated. For diseases spread by short-lived insect vectors a modeller can not calculate R₀ without addressing questions of chance such as, “What percentage of vectors will survive their extrinsic incubation period (EIP) to become infectious?”. Classical Ross-Macdonald theory provides answers for the modeller by making certain concrete assumptions, such as a fixed length EIP and exponentially distributed times between vector blood-meals. Using bluetongue virus spread by biting midges as an exemplar we demonstrate that biologically plausible alterations to the classical assumptions can significantly change the modeller’s prediction of R₀ with both serious over-estimation and under-estimation being possible. The important modelling/control question, “How does R₀ respond to increased vector per-capita mortality?”, is also found to depend strongly on details of the vector life-cycle expressed in the language of probability distributions.

Bionomics of temperate and tropical Culicoides midges: knowledge gaps and consequences for transmission of Culicoides-borne viruses

Culicoides midges are abundant hematophagous flies that vector arboviruses of veterinary and medical importance. Dramatic changes in the epidemiology of Culicoides-borne arboviruses have occurred since 1998, including the emergence of exotic viruses in northern temperate regions, increases in global disease incidence, and enhanced virus diversity in tropical zones. Drivers may include changes in climate, land use, trade, and animal husbandry. New Culicoides species and new wild reservoir hosts have been implicated in transmission, highlighting the dynamic nature of pathogen-vector-host interactions. Focusing on potential vector species worldwide and key elements of vectorial capacity, we review the sensitivity of Culicoides life cycles to abiotic and biotic factors. We consider implications for designing control measures and understanding impacts of environmental change in different ecological contexts. Critical geographical, biological, and taxonomic knowledge gaps are prioritized. Recent developments in genomics and mathematical modeling may enhance ecological understanding of these complex arbovirus systems.

Towards a resource-based habitat approach for spatial modelling of vector-borne disease risks

Given the veterinary and public health impact of vector-borne diseases, there is a clear need to assess the suitability of landscapes for the emergence and spread of these diseases. Current approaches for predicting disease risks neglect key features of the landscape as components of the functional habitat of vectors or hosts, and hence of the pathogen. Empirical-statistical methods do not explicitly incorporate biological mechanisms, whereas current mechanistic models are rarely spatially explicit; both methods ignore the way animals use the landscape (i.e. movement ecology). We argue that applying a functional concept for habitat, i.e. the resource-based habitat concept (RBHC), can solve these issues. The RBHC offers a framework to identify systematically the different ecological resources that are necessary for the completion of the transmission cycle and to relate these resources to (combinations of) landscape features and other environmental factors. The potential of the RBHC as a framework for identifying suitable habitats for vector-borne pathogens is explored and illustrated with the case of bluetongue virus, a midge-transmitted virus affecting ruminants. The concept facilitates the study of functional habitats of the interacting species (vectors as well as hosts) and provides new insight into spatial and temporal variation in transmission opportunities and exposure that ultimately determine disease risks. It may help to identify knowledge gaps and control options arising from changes in the spatial configuration of key resources across the landscape. The RBHC framework may act as a bridge between existing mechanistic and statistical modelling approaches.

A Comparison of Dynamics in Two Models for the Spread of a Vector-Borne Disease

In 2007, bluetongue virus (BTV) was introduced to both Denmark (DK) and the United Kingdom (UK). For this reason, simulation models were built to predict scenarios for future incursions. The DK and UK models have a common description of within-herd dynamics, but differ greatly in their descriptions of between-herd spread, one using an explicit representation of vector dispersal, the other a transmission kernel. Here, we compare model predictions for the dynamics of bluetongue in the UK, based on the 2007 incursion and vaccination rollout in 2008. We demonstrate how an agent-based model shows greater sensitivity to the level of vaccine uptake and has lower variability compared with a kernel-based model. However, a model using a transmission kernel requires less detailed data and is often faster.

Modelling the continental-scale spread of Schmallenberg virus in Europe: approaches and challenges

Following its emergence in northern Europe in 2011 Schmallenberg virus (SBV), a vector-borne disease transmitted by the bites of Culicoides midges, has spread across much of the continent. Here we develop simple models to describe the spread of SBV at a continental scale and, more specifically, within and between NUTS2 regions in Europe. The model for the transmission of SBV between regions suggests that vector dispersal is the principle mechanism for transmission, even at the continental scale. The within-region model indicates that there is substantial heterogeneity amongst regions in the force of infection for cattle and sheep farms. Moreover, there is considerable under-ascertainment of SBV-affected holdings, though the level of under-ascertainment varies between regions. We contrast the relatively simple approach adopted in this study with the more complex continental-scale micro-simulation models which have been developed for pandemic influenza and discuss the strengths, weaknesses and data requirements of both approaches.

Seasonal and spatial heterogeneities in host and vector abundances impact the spatiotemporal spread of bluetongue

Bluetongue (BT) can cause severe livestock losses and large direct and indirect costs for farmers. To propose targeted control strategies as alternative to massive vaccination, there is a need to better understand how BT virus spread in space and time according to local characteristics of host and vector populations. Our objective was to assess, using a modelling approach, how spatiotemporal heterogeneities in abundance and distribution of hosts and vectors impact the occurrence and amplitude of local and regional BT epidemics. We built a reaction-diffusion model accounting for the seasonality in vector abundance and the active dispersal of vectors. Because of the scale chosen, and movement restrictions imposed during epidemics, host movements and wind-induced passive vector movements were neglected. Four levels of complexity were addressed using a theoretical approach, from a homogeneous to a heterogeneous environment in abundance and distribution of hosts and vectors. These scenarios were illustrated using data on abundance and distribution of hosts and vectors in a real geographical area. We have shown that local epidemics can occur earlier and be larger in scale far from the primary case rather than close to it. Moreover, spatial heterogeneities in hosts and vectors delay the epidemic peak and decrease the infection prevalence. The results obtained on a real area confirmed those obtained on a theoretical domain. Although developed to represent BTV spatiotemporal spread, our model can be used to study other vector-borne diseases of animals with a local to regional spread by vector diffusion.

Quantifying dispersal of european culicoides (Diptera: Ceratopogonidae) vectors between farms using a novel mark-release-recapture technique

Studying the dispersal of small flying insects such as Culicoides constitutes a great challenge due to huge population sizes and lack of a method to efficiently mark and objectively detect many specimens at a time. We here describe a novel mark-release-recapture method for Culicoides in the field using fluorescein isothiocyanate (FITC) as marking agent without anaesthesia. Using a plate scanner, this detection technique can be used to analyse thousands of individual Culicoides specimens per day at a reasonable cost. We marked and released an estimated 853 specimens of the Pulicaris group and 607 specimens of the Obsoletus group on a cattle farm in Denmark. An estimated 9,090 (8,918-9,260) Obsoletus group specimens and 14,272 (14,194-14,448) Pulicaris group specimens were captured in the surroundings and subsequently analysed. Two (0.3%) Obsoletus group specimens and 28 (4.6%) Pulicaris group specimens were recaptured. The two recaptured Obsoletus group specimens were caught at the release point on the night following release. Eight (29%) of the recaptured Pulicaris group specimens were caught at a pig farm 1,750 m upwind from the release point. Five of these were recaptured on the night following release and the three other were recaptured on the second night after release. This is the first time that movement of Culicoides vectors between farms in Europe has been directly quantified. The findings suggest an extensive and rapid exchange of disease vectors between farms. Rapid movement of vectors between neighboring farms may explain the the high rate of spatial spread of Schmallenberg and bluetongue virus (BTV) in northern Europe.