A modeling framework to describe the transmission of bluetongue virus within and between farms in Great Britain

Parameterisation of a bluetongue virus mathematical model using a systematic literature review

Bluetongue virus (BT) is a vector-borne virus that causes a disease, called bluetongue, which results in significant economic loss and morbidity in sheep, cattle, goats and wild ungulates across all continents of the world except Antarctica. Despite the geographical breadth of its impact, most BT epidemiological models are informed by parameters derived from the 2006-2009 BTV-8 European outbreak. The aim of this study was to develop a highly adaptable model for BT which could be used elsewhere in the world, as well as to identify the parameters which most influence outbreak dynamics, so that policy makers can be properly informed with the most current information to aid in disease planning. To provide a framework for future outbreak modelling and an updated parameterisation that reflects natural variation in infections, a newly developed and parameterised two-host, two-vector species ordinary differential equation model was formulated and analysed. The model was designed to be adaptable to be implemented in any region of the world and able to model both epidemic and endemic scenarios. It was parameterised using a systematic literature review of host-to-vector and vector-to-host transmission rates, host latent periods, host infectious periods, and vaccine protection factors. The model was demonstrated using the updated parameters, with South Africa as a setting based on the Western Cape's known cattle and sheep populations, local environmental parameters, and Culicoides spp. presence data. The sensitivity analysis identified that the duration of the infectious period for sheep and cows had the greatest impact on the outbreak length and number of animals infected at the peak of the outbreak. Transmission rates from cows and sheep to C. imicola midges greatly influenced the day on which the peak of the outbreak occurred, along with the duration of incubation period, and infectious period for cows. Finally, the protection factor of the vaccine had the greatest influence on the total number of animals infected. This knowledge could aid in the development of control measures. Due to gradual climate and anthropological change resulting in alterations in vector habitat suitability, BT outbreaks are likely to continue to increase in range and frequency. Therefore, this research provides an updated BT modelling framework for future outbreaks around the world to explore transmission, outbreak dynamics and control measures.

Modeling the 2014-2015 Vesicular Stomatitis Outbreak in the United States Using an SEIR-SEI Approach

Vesicular stomatitis (VS) is a vector-borne livestock disease caused by the vesicular stomatitis New Jersey virus (VSNJV). This study presents the first application of an SEIR-SEI compartmental model to analyze VSNJV transmission dynamics. Focusing on the 2014-2015 outbreak in the United States, the model integrates vertebrate hosts and insect vector demographics while accounting for heterogeneous competency within the populations and observation bias in documented disease cases. Key epidemiological parameters were estimated using Bayesian inference and Markov chain Monte Carlo (MCMC) methods, including the force of infection, effective reproduction number (Rt), and incubation periods. The model revealed significant underreporting, with only 10-24% of infections documented, 23% of which presented with clinical symptoms. These findings underscore the importance of including competence and imperfect detection in disease models to depict outbreak dynamics and inform effective control strategies accurately. As a baseline model, this SEIR-SEI implementation is intended to serve as a foundation for future refinements and expansions to improve our understanding of VS dynamics. Enhanced surveillance and targeted interventions are recommended to manage future VS outbreaks.

Spatial Transmission Characteristics of the Bluetongue Virus Serotype 3 Epidemic in The Netherlands, 2023

A devastating bluetongue (BT) epidemic caused by bluetongue virus serotype 3 (BTV-3) has spread throughout most of the Netherlands within two months since the first infection was officially confirmed in the beginning of September 2023. The epidemic comes with unusually strong suffering of infected cattle through severe lameness, often resulting in mortality or euthanisation for welfare reasons. In total, tens of thousands of sheep have died or had to be euthanised. By October 2023, more than 2200 locations with ruminant livestock were officially identified to be infected with BTV-3, and additionally, ruminants from 1300 locations were showing BTV-associated clinical symptoms (but not laboratory-confirmed BT). Here, we report on the spatial spread and dynamics of this BT epidemic. More specifically, we characterized the distance-dependent intensity of the between-holding transmission by estimating the spatial transmission kernel and by comparing it to transmission kernels estimated earlier for BTV-8 transmission in Northwestern Europe in 2006 and 2007. The 2023 BTV-3 kernel parameters are in line with those of the transmission kernel estimated previously for the between-holding spread of BTV-8 in Europe in 2007. The 2023 BTV-3 transmission kernel has a long-distance spatial range (across tens of kilometres), evidencing that in addition to short-distance dispersal of infected midges, other transmission routes such as livestock transports probably played an important role.

Modelling African horse sickness emergence and transmission in the South African control area using a deterministic metapopulation approach

African horse sickness is an equine orbivirus transmitted by Culicoides Latreille biting midges. In the last 80 years, it has caused several devastating outbreaks in the equine population in Europe, the Far and Middle East, North Africa, South-East Asia, and sub-Saharan Africa. The disease is endemic in South Africa; however, a unique control area has been set up in the Western Cape where increased surveillance and control measures have been put in place. A deterministic metapopulation model was developed to explore if an outbreak might occur, and how it might develop, if a latently infected horse was to be imported into the control area, by varying the geographical location and months of import. To do this, a previously published ordinary differential equation model was developed with a metapopulation approach and included a vaccinated horse population. Outbreak length, time to peak infection, number of infected horses at the peak, number of horses overall affected (recovered or dead), re-emergence, and Rv (the basic reproduction number in the presence of vaccination) were recorded and displayed using GIS mapping. The model predictions were compared to previous outbreak data to ensure validity. The warmer months (November to March) had longer outbreaks than the colder months (May to September), took more time to reach the peak, and had a greater total outbreak size with more horses infected at the peak. Rv appeared to be a poor predictor of outbreak dynamics for this simulation. A sensitivity analysis indicated that control measures such as vaccination and vector control are potentially effective to manage the spread of an outbreak, and shortening the vaccination window to July to September may reduce the risk of vaccine-associated outbreaks.

Culicoides (Diptera: Ceratopogonidae) Abundance Is Influenced by Livestock Host Species and Distance to Hosts at the Micro Landscape Scale

The vector/host ratio and host preference are important parameters for the modelling of vector-borne livestock diseases. It can be anticipated that Culicoides abundance is not homogeneously distributed in the landscape. We investigated the influence of host species (dairy cow, sheep, and a light-trap (LT) as a surrogate host) and distance of measurement to hosts on Culicoides abundance using a randomized block-design with 12 measuring days and seven 3-min aerial sweep-netting sessions per whole hour at three distances to the host (0, 10, and 25 m), from five hours before to and including one hour after sunset. Dairy cows were found to be a far stronger attractor of Culicoides midges than sheep, while both hosts were far stronger attractors of midges than the LT. Culicoides abundance declined significantly with increasing distance from the livestock hosts; this phenomenon was much stronger for dairy cows than for ewes. In contrast, Culicoides abundance increased with increasing distance from the LT, pin-pointing the apparent shortcomings of the LT as a surrogate host to lure midges. Our data indicate that livestock host species and the distance from these hosts have a profound effect on Culicoides abundance in the landscape.

Host preference of bluetongue virus vectors, Culicoides species associated with livestock in West Bengal, India: Potential relevance on bluetongue epidemiology

Determination of host choice of Culicoides species (Diptera: Ceratopogonidae), the vectors of bluetongue virus (BTV), is pivotal to ascertain the role of each species in the transmission of pathogens, pest management and enumeration of disease prediction models. Host preference of livestock associated Culicoides midges was investigated in West Bengal, India with four replicates of a 3 × 3 Latin square design during August and September 2021. Adult Culicoides were mouth aspirated from three BTV hosts viz., cattle, sheep and goats. Mouth aspirating was validated by the sweep net collections. The host-baited collections recorded seven Culicoides species; with the highest landing rate on cattle (n = 5,667; 92.9%) followed by sheep (n = 365; 6.0%) and goat (n = 67; 1.1%). Based on the Jacob's selectivity index, all midge species, except for Culicoides fulvus Sen & Das Gupta, encountered, preferred cattle over other mammalian hosts. Culicoides oxystoma Kieffer, the subgenus Trithecoides Wirth & Hubert and Culicoides actoni Smith, predominated on the ventral region (belly/flank) of the cattle. However, Culicoides peregrinus Kieffer and C. actoni were observed to be prevalent in the leg region of sheep. A significantly higher percentage of female (99.9%) with only 0.3% of male were trapped in aspiration based animal baited collections. On the other hand sweep net and light trap catch comprises of 50.7%, 89.7% female and 49.2%, 10.2% male respectively. Surprisingly, DNA based blood meal analysis revealed human blood from the midges trapped in UV-LED light traps. Supplying the first evidence that Culicoides similis Carter, Ingram & Macfie, C. fulvus and Culicoides palpifer Das Gupta & Ghosh, feed on humans.

Determining the effects of wind-aided midge movement on the outbreak and coexistence of multiple bluetongue virus serotypes in patchy environments

Bluetongue virus (BTV) has 27 serotypes with some of them coexisting in different environments which make its control difficult. Wind-aided midge movement is a known mechanism in the spread of BTV. However, its effects on the dynamics of multiple BTV serotypes are not clear. Ordinary differential equation (ODE) and continuous-time Markov chain (CTMC) models for two BTV serotypes in an environment divided into two patches depending on the risk of infection are formulated and analysed. By approximating the CTMC model with a multitype branching process, an estimate for the probability of a major outbreak of two BTV serotypes is obtained. It is shown that without movement a major outbreak occurs in the high-risk patch, but with cattle or midge movement it occurs in both patches. When a major outbreak occurs, numerical simulations of the ODE model illustrate possible coexistence in both patches if the patches are connected by midge or cattle movement. Sensitivity analysis, based on the Latin hypercube sampling method, identified midge mortality and biting rates as being the most important in determining the magnitude of the probability of a major outbreak. These results indicate the significance of wind-aided midge movement on the outbreak and coexistence of multiple BTV serotypes in patchy environments.

Assessment of the control measures of the category A diseases of Animal Health Law: peste des petits ruminants

EFSA received a mandate from the European Commission to assess the effectiveness of some of the control measures against diseases included in the Category A list according to Regulation (EU) 2016/429 on transmissible animal diseases ('Animal Health Law'). This opinion belongs to a series of opinions where these control measures will be assessed, with this opinion covering the assessment of control measures for peste des petits ruminants (PPR). In this opinion, EFSA and the AHAW Panel of experts review the effectiveness of: (i) clinical and laboratory sampling procedures, (ii) monitoring period and (iii) the minimum radii of the protection and surveillance zones, and the minimum length of time the measures should be applied in these zones. The general methodology used for this series of opinions has been published elsewhere; nonetheless, the transmission kernels used for the assessment of the minimum radii of the protection and surveillance zones are shown. Several scenarios for which these control measures had to be assessed were designed and agreed prior to the start of the assessment. The monitoring period of 21 days was assessed as effective, except for the first affected establishments detected, where 33 days is recommended. It was concluded that beyond the protection (3 km) and the surveillance zones (10 km) only 9.6% (95% CI: 3.1-25.8%) and 2.3% (95% CI: 1-5.5%) of the infections from an affected establishment may occur, respectively. This may be considered sufficient to contain the disease spread (95% probability of containing transmission corresponds to 5.3 km). Recommendations provided for each of the scenarios assessed aim to support the European Commission in the drafting of further pieces of legislation, as well as for plausible ad-hoc requests in relation to PPR.

Within-farm transmission characteristics of bluetongue virus serotype 8 in cattle and sheep in the Netherlands, 2007-2008

In 2006 and 2007, sheep and cattle farms in the Netherlands were affected by an epidemic of bluetongue virus serotype 8 (BTV-8). In order to obtain insight into the within-farm spread of the virus, five affected cattle and five affected sheep farms were longitudinally monitored between early 2007 and mid or late 2008. The farms were visited between four and seven times to collect blood samples. During each visit, all animals present in the flock or herd were sampled. The samples were analysed for the presence of BTV-8 antibodies (ELISA) and BTV-8 antigen (rRT-PCR). The observed patterns of RT-PCR positives indicate a rapid within-farm virus spread during the vector season. During vector-free periods we observed a complete rRT-PCR positivity decline within a few months. During the vector season a lower bound estimate of the basic reproduction number (R0) ranges from 2.9-6.9 in the cattle herds (one herd not analysed), and from 1.3-3.2 in the sheep flocks in this study.

Biting midge dynamics and bluetongue transmission: a multiscale model linking catch data with climate and disease outbreaks

Bluetongue virus (BTV) serotype 8 has been circulating in Europe since a major outbreak occurred in 2006, causing economic losses to livestock farms. The unpredictability of the biting activity of midges that transmit BTV implies difficulty in computing accurate transmission models. This study uniquely integrates field collections of midges at a range of European latitudes (in Sweden, The Netherlands, and Italy), with a multi-scale modelling approach. We inferred the environmental factors that influence the dynamics of midge catching, and then directly linked predicted midge catches to BTV transmission dynamics. Catch predictions were linked to the observed prevalence amongst sentinel cattle during the 2007 BTV outbreak in The Netherlands using a dynamic transmission model. We were able to directly infer a scaling parameter between daily midge catch predictions and the true biting rate per cow per day. Compared to biting rate per cow per day the scaling parameter was around 50% of 24 h midge catches with traps. Extending the estimated biting rate across Europe, for different seasons and years, indicated that whilst intensity of transmission is expected to vary widely from herd to herd, around 95% of naïve herds in western Europe have been at risk of sustained transmission over the last 15 years.

Rift Valley Fever - assessment of effectiveness of surveillance and control measures in the EU

Effectiveness of surveillance and control measures against Rift Valley Fever (RVF) in Mayotte (overseas France) and in continental EU were assessed using mathematical models. Surveillance for early detection of RVF virus circulation implies very low design prevalence values and thus sampling a high number of animals, so feasibility issues may rise. Passive surveillance based on notified abortions in ruminants is key for early warning and at present the only feasible surveillance option. The assessment of vaccination and culling against RVF in Mayotte suggests that vaccination is more effective when quickly implemented throughout the population, e.g. at a rate of 200 or 2,000 animals vaccinated per day. Test and cull is not an option for RVF control in Mayotte given the high number of animals that would need to be tested. If the risk of RVFV introduction into the continental EU increases, ruminant establishments close to possible points of disease incursion should be included in the surveillance. An enhanced surveillance on reproductive disorders should be applied during summer in risk areas. Serosurveillance targets of 0.3% animals should be at least considered. RVF control measures possibly applied in the continental EU have been assessed in the Netherlands, as an example. Culling animals on farms within a 20 km radius of detected farms appears as the most effective measure to control RVF spread, although too many animals should be culled. Alternative measures are vaccination in a 50 km radius around detection, ring vaccination between 20 and 50 km and culling of detected farms. The assessment of zoning showed that, following RVFV introduction and considering an R0 = 2, a mean vector dispersal of 10 km and 10 farms initially detected, RVFV would spread beyond a radius of up to 100 km or 50 km from the infected area with 10% or 55% probability, respectively.

A quantitative analysis of the spatial and temporal evolution patterns of the bluetongue virus outbreak in the island of Lesvos, Greece, in 2014

Bluetongue virus (BTV) causes an infectious disease called bluetongue, a vector-borne viral disease of ruminants, which has major implications and causes severe economic damage due to its effect on livestock. These economic costs are mostly ascribed to the trade restrictions imposed during the epidemic period. In August 2014, an epidemic of bluetongue occurred in the island of Lesvos, Greece. The epidemic was severe and evolved over time, lasting until December 2014. The total cases of infected farms were 490, including a total number of 136,368 small ruminants. In this paper, we describe a bluetongue virus serotype 4 (BTV-4) epidemic and utilize Bayesian epidemic models to capture the spatio-temporal spread of the disease. Our study provides important insights into the drivers of BTV transmission and has implications for designing control strategies. The results showed strong spatial autocorrelations, with BTV being more likely to spread between farms located nearby. The spatial modelling results proposed a certain spatial radius (~12 km) around the onset of a similar epidemic for imposing restrictions on animal movement, which can be sufficient for the control of the disease and limit economic damage.

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.

Inferences about the transmission of lumpy skin disease virus between herds from outbreaks in Albania in 2016

Lumpy skin disease has recently emerged as a major threat to cattle populations outside of Africa, where it is endemic. In 2015 the first ever European outbreaks occurred in Greece, which were followed by spread across much of the Balkans in 2016. Here we use a simple mathematical model for the transmission of lumpy skin disease virus (LSDV) between herds to explore factors influencing its spread by fitting it to data on outbreaks in Albania in 2016. We show that most transmission occurs over short distances (<5 km), but with an appreciable probability of transmission at longer distances. We also show that there is evidence for seasonal variation in the force of infection associated with temperature, possibly through its influence on the relative abundance of the stable fly, Stomoxys calcitrans. These two results together are consistent with LSDV being transmitted by the bites of blood-feeding insects, though further work is required to incriminate specific species as vectors. Finally, we show that vaccination has a significant impact on spread and estimate the vaccine effectiveness to be 76%.

One model to rule them all? Modelling approaches across OneHealth for human, animal and plant epidemics

One hundred years after the 1918 influenza outbreak, are we ready for the next pandemic? This paper addresses the need to identify and develop collaborative, interdisciplinary and cross-sectoral approaches to modelling of infectious diseases including the fields of not only human and veterinary medicine, but also plant epidemiology. Firstly, the paper explains the concepts on which the most common epidemiological modelling approaches are based, namely the division of a host population into susceptible, infected and removed (SIR) classes and the proportionality of the infection rate to the size of the susceptible and infected populations. It then demonstrates how these simple concepts have been developed into a vast and successful modelling framework that has been used in predicting and controlling disease outbreaks for over 100 years. Secondly, it considers the compartmental models based on the SIR paradigm within the broader concept of a 'disease tetrahedron' (comprising host, pathogen, environment and man) and uses it to review the similarities and differences among the fields comprising the 'OneHealth' approach. Finally, the paper advocates interactions between all fields and explores the future challenges facing modellers. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.

Realistic assumptions about spatial locations and clustering of premises matter for models of foot-and-mouth disease spread in the United States

Spatially explicit livestock disease models require demographic data for individual farms or premises. In the U.S., demographic data are only available aggregated at county or coarser scales, so disease models must rely on assumptions about how individual premises are distributed within counties. Here, we addressed the importance of realistic assumptions for this purpose. We compared modeling of foot and mouth disease (FMD) outbreaks using simple randomization of locations to premises configurations predicted by the Farm Location and Agricultural Production Simulator (FLAPS), which infers location based on features such as topography, land-cover, climate, and roads. We focused on three premises-level Susceptible-Exposed-Infectious-Removed models available from the literature, all using the same kernel approach but with different parameterizations and functional forms. By computing the basic reproductive number of the infection (R₀) for both FLAPS and randomized configurations, we investigated how spatial locations and clustering of premises affects outbreak predictions. Further, we performed stochastic simulations to evaluate if identified differences were consistent for later stages of an outbreak. Using Ripley’s K to quantify clustering, we found that FLAPS configurations were substantially more clustered at the scales relevant for the implemented models, leading to a higher frequency of nearby premises compared to randomized configurations. As a result, R₀ was typically higher in FLAPS configurations, and the simulation study corroborated the pattern for later stages of outbreaks. Further, both R₀ and simulations exhibited substantial spatial heterogeneity in terms of differences between configurations. Thus, using realistic assumptions when de-aggregating locations based on available data can have a pronounced effect on epidemiological predictions, affecting if, where, and to what extent FMD may invade the population. We conclude that methods such as FLAPS should be preferred over randomization approaches.

When modeling the spread of infectious livestock diseases such as foot-and-mouth disease (FMD), the distance between premises is an important aspect. In the U.S., locations of premises are not available, forcing modelers to make assumptions about their coordinates. Such assumptions can be more or less crude and will impact the conclusions drawn from the model. To investigate the impact of such assumptions, we modeled outbreaks of FMD within the cattle population of the U.S. under two assumptions about premises locations. Their position was either randomly distributed within counties or informed by a state-of-the-art method developed specifically to simulate realistic locations of agricultural operations. We found that the higher degree of spatial clustering of premises associated with more realistic assumptions about locations leads to a substantially higher risk of outbreaks. Our results also show that the amount with which the risk is under-estimated by randomizing locations is unevenly distributed across the landscape. Together, these findings show a clear support for using informed methods to determine the spatial locations of premises and highlight the importance of spatial clustering when modeling FMD-like diseases.

Reviewing age-structured epidemiological models of cattle diseases tailored to support management decisions: Guidance for the future

Mechanistic simulation models are being increasingly used as tools to assist with animal health decision-making in the cattle sector. We reviewed scientific literature for studies reporting age-structured cattle management models in application to infectious diseases. Our emphasis was on papers dedicated to support decision making in the field. In this systematic review we considered 1290 manuscripts and identified 76 eligible studies. These are based on 52 individual models from 10 countries addressing 9 different pathogens. We provide an overview of these models and present in detail their theoretical foundations, design paradigms and incorporated processes. We propose a structure of the characteristics of cattle disease models using three main features: [1] biological processes, [2] farming-related processes and [3] pathogen-related processes. It would be of benefit if future cattle disease models were to follow this structure to facilitate science communication and to allow increased model transparency.

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.

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.

Role of animal movement and indirect contact among farms in transmission of porcine epidemic diarrhea virus

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The emergence of porcine epidemic diarrhea virus (PEDv) caused a major epidemic.

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We developed a model simulating the between-farm spread of PEDv.

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Probabilities of each transmission mode were calibrated to match observed dynamics.

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Transmission was mostly between neighboring farms or through pig movements.

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However, long-distance jumps were primarily due to contaminated fomites and feed.

Epidemiological models of the spread of pathogens in livestock populations primarily focus on direct contact between farms based on animal movement data, and in some cases, local spatial spread based on proximity between premises. The roles of other types of indirect contact among farms is rarely accounted for. In addition, data on animal movements is seldom available in the United States. However, the spread of porcine epidemic diarrhea virus (PEDv) in U.S. swine represents one of the best documented emergences of a highly infectious pathogen in the U.S. livestock industry, providing an opportunity to parameterize models of pathogen spread via direct and indirect transmission mechanisms in swine. Using observed data on pig movements during the initial phase of the PEDv epidemic, we developed a network-based and spatially explicit epidemiological model that simulates the spread of PEDv via both indirect and direct movement-related contact in order to answer unresolved questions concerning factors facilitating between-farm transmission. By modifying the likelihood of each transmission mechanism and fitting this model to observed epidemiological dynamics, our results suggest that between-farm transmission was primarily driven by direct mechanisms related to animal movement and indirect mechanisms related to local spatial spread based on geographic proximity. However, other forms of indirect transmission among farms, including contact via contaminated vehicles and feed, were responsible for high consequence transmission events resulting in the introduction of the virus into new geographic areas. This research is among the first reports of farm-level animal movements in the U.S. swine industry and, to our knowledge, represents the first epidemiological model of commercial U.S. swine using actual data on farm-level animal movement.

Need for speed: An optimized gridding approach for spatially explicit disease simulations

Numerical models for simulating outbreaks of infectious diseases are powerful tools for informing surveillance and control strategy decisions. However, large-scale spatially explicit models can be limited by the amount of computational resources they require, which poses a problem when multiple scenarios need to be explored to provide policy recommendations. We introduce an easily implemented method that can reduce computation time in a standard Susceptible-Exposed-Infectious-Removed (SEIR) model without introducing any further approximations or truncations. It is based on a hierarchical infection process that operates on entire groups of spatially related nodes (cells in a grid) in order to efficiently filter out large volumes of susceptible nodes that would otherwise have required expensive calculations. After the filtering of the cells, only a subset of the nodes that were originally at risk are then evaluated for actual infection. The increase in efficiency is sensitive to the exact configuration of the grid, and we describe a simple method to find an estimate of the optimal configuration of a given landscape as well as a method to partition the landscape into a grid configuration. To investigate its efficiency, we compare the introduced methods to other algorithms and evaluate computation time, focusing on simulated outbreaks of foot-and-mouth disease (FMD) on the farm population of the USA, the UK and Sweden, as well as on three randomly generated populations with varying degree of clustering. The introduced method provided up to 500 times faster calculations than pairwise computation, and consistently performed as well or better than other available methods. This enables large scale, spatially explicit simulations such as for the entire continental USA without sacrificing realism or predictive power.

Numerical models for simulating the outbreak of infectious disease are powerful tools that can be used to inform policy decisions by simulating outbreaks and control actions. However, they rely on considerable computational power to explore all outcomes and scenarios of interest. Focusing on model types commonly used for livestock diseases, we here introduce novel algorithms for efficient computation, alongside techniques to optimize them based on simplifying assumptions. Through simulations of FMD outbreak in the US, the UK and Sweden, as well as in computer generated landscapes, we test how these methods perform under realistic conditions. We find that our optimization techniques works well, and when the introduced algorithms are implemented with these optimizations, computation time can be reduced by more than two orders of magnitude compared to pairwise calculations. We propose that the considered algorithms—which are straight forward to implement—will be useful for simulation of a wide range of diseases, and will promote the use of simulation models for policy recommendation.

Dynamics of Zika virus outbreaks: an overview of mathematical modeling approaches

BACKGROUND

The Zika virus was first discovered in 1947. It was neglected until a major outbreak occurred on Yap Island, Micronesia, in 2007. Teratogenic effects resulting in microcephaly in newborn infants is the greatest public health threat. In 2016, the Zika virus epidemic was declared as a Public Health Emergency of International Concern (PHEIC). Consequently, mathematical models were constructed to explicitly elucidate related transmission dynamics.

SURVEY METHODOLOGY

In this review article, two steps of journal article searching were performed. First, we attempted to identify mathematical models previously applied to the study of vector-borne diseases using the search terms "dynamics," "mathematical model," "modeling," and "vector-borne" together with the names of vector-borne diseases including chikungunya, dengue, malaria, West Nile, and Zika. Then the identified types of model were further investigated. Second, we narrowed down our survey to focus on only Zika virus research. The terms we searched for were "compartmental," "spatial," "metapopulation," "network," "individual-based," "agent-based" AND "Zika." All relevant studies were included regardless of the year of publication. We have collected research articles that were published before August 2017 based on our search criteria. In this publication survey, we explored the Google Scholar and PubMed databases.

RESULTS

We found five basic model architectures previously applied to vector-borne virus studies, particularly in Zika virus simulations. These include compartmental, spatial, metapopulation, network, and individual-based models. We found that Zika models carried out for early epidemics were mostly fit into compartmental structures and were less complicated compared to the more recent ones. Simple models are still commonly used for the timely assessment of epidemics. Nevertheless, due to the availability of large-scale real-world data and computational power, recently there has been growing interest in more complex modeling frameworks.

DISCUSSION

Mathematical models are employed to explore and predict how an infectious disease spreads in the real world, evaluate the disease importation risk, and assess the effectiveness of intervention strategies. As the trends in modeling of infectious diseases have been shifting towards data-driven approaches, simple and complex models should be exploited differently. Simple models can be produced in a timely fashion to provide an estimation of the possible impacts. In contrast, complex models integrating real-world data require more time to develop but are far more realistic. The preparation of complicated modeling frameworks prior to the outbreaks is recommended, including the case of future Zika epidemic preparation.

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.

Microclimatic temperatures increase the potential for vector-borne disease transmission in the Scandinavian climate

We quantified the difference between the meteorological temperature recorded by the Danish Meteorological Institute (DMI) weather stations and the actual microclimatic temperatures at two or three different heights at six potential insect habitats. We then compared the impact of the hourly temperature on the extrinsic incubation period (EIP) of six pathogens. Finally, we developed a regression model, enabling us to predict the microclimatic temperatures of different habitats based on five standard meteorological parameters readily available from any meteorological institution. Microclimatic habitats were on average 3.5-5 °C warmer than the DMI recorded temperatures during midday and 1-3 °C cooler at midnight. The estimated EIP for five of the six microclimatic habitats was shorter than the estimates based on DMI temperatures for all pathogens studied. The microclimatic temperatures also predicted a longer season for virus development compared to DMI temperatures. Based on DMI data of hourly temperature, solar radiation, wind speed, rain and humidity, we were able to predict the microclimatic temperature of different habitats with an R² of 0.87-0.96. Using only meteorological temperatures for vector-borne disease transmission models may substantially underestimate both the daily potential for virus development and the duration of the potential transmission season.

Spatial dynamics and control of a crop pathogen with mixed-mode transmission

Trade or sharing that moves infectious planting material between farms can, for vertically-transmitted plant diseases, act as a significant force for dispersal of pathogens, particularly where the extent of material movement may be greater than that of infected vectors or inoculum. The network over which trade occurs will then effect dispersal, and is important to consider when attempting to control the disease. We consider the difference that planting material exchange can make to successful control of cassava brown streak disease, an important viral disease affecting one of Africa's staple crops. We use a mathematical model of smallholders' fields to determine the effect of informal trade on both the spread of the pathogen and its control using clean-seed systems, determining aspects that could limit the damage caused by the disease. In particular, we identify the potentially detrimental effects of markets, and the benefits of a community-based approach to disease control.

Bayesian epidemic models for spatially aggregated count data

Epidemic data often possess certain characteristics, such as the presence of many zeros, the spatial nature of the disease spread mechanism, environmental noise, serial correlation and dependence on time-varying factors. This paper addresses these issues via suitable Bayesian modelling. In doing so, we utilize a general class of stochastic regression models appropriate for spatio-temporal count data with an excess number of zeros. The developed regression framework does incorporate serial correlation and time-varying covariates through an Ornstein-Uhlenbeck process formulation. In addition, we explore the effect of different priors, including default options and variations of mixtures of g-priors. The effect of different distance kernels for the epidemic model component is investigated. We proceed by developing branching process-based methods for testing scenarios for disease control, thus linking traditional epidemiological models with stochastic epidemic processes, useful in policy-focused decision making. The approach is illustrated with an application to a sheep pox dataset from the Evros region, Greece. Copyright © 2017 John Wiley & Sons, Ltd.

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.

Bluetongue: control, surveillance and safe movement of animals

The performance of different bluetongue control measures related to both vaccination and protection from bluetongue virus (BTV) vectors was assessed. By means of a mathematical model, it was concluded that when vaccination is applied on 95% of animals even for 3 years, bluetongue cannot be eradicated and is able to re‐emerge. Only after 5 years of vaccination, the infection may be close to the eradication levels. In the absence of vaccination, the disease can persist for several years, reaching an endemic condition with low level of prevalence of infection. Among the mechanisms for bluetongue persistence, the persistence in the wildlife, the transplacental transmission in the host, the duration of viraemia and the possible vertical transmission in vectors were assessed. The criteria of the current surveillance scheme in place in the EU for demonstration of the virus absence need revision, because it was highlighted that under the current surveillance policy bluetongue circulation might occur undetected. For the safe movement of animals, newborn ruminants from vaccinated mothers with neutralising antibodies can be considered protected against infection, although a protective titre threshold cannot be identified. The presence of colostral antibodies interferes with the vaccine immunisation in the newborn for more than 3 months after birth, whereas the minimum time after vaccination of animal to be considered immune can be up to 48 days. The knowledge about vectors ecology, mechanisms of over‐wintering and criteria for the seasonally vector‐free period was updated. Some Culicoides species are active throughout the year and an absolute vector‐free period may not exist at least in some areas in Europe. To date, there is no evidence that the use of insecticides and repellents reduce the transmission of BTV in the field, although this may reduce host/vector contact. By only using pour‐on insecticides, protection of animals is lower than the one provided by vector‐proof establishments.

This publication is linked to the following EFSA Supporting Publications article: http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2017.EN-1182/full, http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2017.EN-1171/full

Assessing the potential for Bluetongue virus 8 to spread and vaccination strategies in Scotland

Europe has seen frequent outbreaks of Bluetongue (BT) disease since 2006, including an outbreak of BT virus serotype 8 in central France during 2015 that has continued to spread in Europe during 2016. Thus, assessing the potential for BTv-8 spread and determining the optimal deployment of vaccination is critical for contingency planning. We developed a spatially explicit mathematical model of BTv-8 spread in Scotland and explored the sensitivity of transmission to key disease spread parameters for which detailed empirical data is lacking. With parameters at mean values, there is little spread of BTv-8 in Scotland. However, under a “worst case” but still feasible scenario with parameters at the limits of their ranges and temperatures 1 °C warmer than the mean, we find extensive spread with 203,000 sheep infected given virus introduction to the south of Scotland between mid-May and mid-June. Strategically targeted vaccine interventions can greatly reduce BT spread. Specifically, despite BT having most clinical impact in sheep, we show that vaccination can have the greatest impact on reducing BTv infections in sheep when administered to cattle, which has implications for disease control policy.

A Deterministic Model to Quantify Risk and Guide Mitigation Strategies to Reduce Bluetongue Virus Transmission in California Dairy Cattle

The global distribution of bluetongue virus (BTV) has been changing recently, perhaps as a result of climate change. To evaluate the risk of BTV infection and transmission in a BTV-endemic region of California, sentinel dairy cows were evaluated for BTV infection, and populations of Culicoides vectors were collected at different sites using carbon dioxide. A deterministic model was developed to quantify risk and guide future mitigation strategies to reduce BTV infection in California dairy cattle. The greatest risk of BTV transmission was predicted within the warm Central Valley of California that contains the highest density of dairy cattle in the United States. Temperature and parameters associated with Culicoides vectors (transmission probabilities, carrying capacity, and survivorship) had the greatest effect on BTV's basic reproduction number, R0. Based on these analyses, optimal control strategies for reducing BTV infection risk in dairy cattle will be highly reliant upon early efforts to reduce vector abundance during the months prior to peak transmission.

Challenges and priorities for modelling livestock health and pathogens in the context of climate change

Climate change has the potential to impair livestock health, with consequences for animal welfare, productivity, greenhouse gas emissions, and human livelihoods and health. Modelling has an important role in assessing the impacts of climate change on livestock systems and the efficacy of potential adaptation strategies, to support decision making for more efficient, resilient and sustainable production. However, a coherent set of challenges and research priorities for modelling livestock health and pathogens under climate change has not previously been available. To identify such challenges and priorities, researchers from across Europe were engaged in a horizon-scanning study, involving workshop and questionnaire based exercises and focussed literature reviews. Eighteen key challenges were identified and grouped into six categories based on subject-specific and capacity building requirements. Across a number of challenges, the need for inventories relating model types to different applications (e.g. the pathogen species, region, scale of focus and purpose to which they can be applied) was identified, in order to identify gaps in capability in relation to the impacts of climate change on animal health. The need for collaboration and learning across disciplines was highlighted in several challenges, e.g. to better understand and model complex ecological interactions between pathogens, vectors, wildlife hosts and livestock in the context of climate change. Collaboration between socio-economic and biophysical disciplines was seen as important for better engagement with stakeholders and for improved modelling of the costs and benefits of poor livestock health. The need for more comprehensive validation of empirical relationships, for harmonising terminology and measurements, and for building capacity for under-researched nations, systems and health problems indicated the importance of joined up approaches across nations. The challenges and priorities identified can help focus the development of modelling capacity and future research structures in this vital field. Well-funded networks capable of managing the long-term development of shared resources are required in order to create a cohesive modelling community equipped to tackle the complex challenges of climate change.

Spatio-temporal modelling of foot-and-mouth disease outbreaks

We present and analyse data collected during a severe epidemic of foot-and-mouth disease (FMD) that occurred between July and September 2000 in a region of northeastern Greece with strategic importance since it represents the southeastern border of Europe and Asia. We implement generic Bayesian methodology, which offers flexibility in the ability to fit several realistically complex models that simultaneously capture the presence of ‘excess’ zeros, the spatio-temporal dependence of the cases, assesses the impact of environmental noise and controls for multicollinearity issues. Our findings suggest that the epidemic was mostly driven by the size and the animal type of each farm as well as the distance between farms while environmental and other endemic factors were not important during this outbreak. Analyses of this kind may prove useful to informing decisions related to optimal control measures for potential future FMD outbreaks as well as other acute epidemics such as FMD.

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.

Epidemic predictions in an imperfect world: modelling disease spread with partial data

‘Big-data’ epidemic models are being increasingly used to influence government policy to help with control and eradication of infectious diseases. In the case of livestock, detailed movement records have been used to parametrize realistic transmission models. While livestock movement data are readily available in the UK and other countries in the EU, in many countries around the world, such detailed data are not available. By using a comprehensive database of the UK cattle trade network, we implement various sampling strategies to determine the quantity of network data required to give accurate epidemiological predictions. It is found that by targeting nodes with the highest number of movements, accurate predictions on the size and spatial spread of epidemics can be made. This work has implications for countries such as the USA, where access to data is limited, and developing countries that may lack the resources to collect a full dataset on livestock movements.

Space-time modelling of the spread of pancreas disease (PD) within and between Norwegian marine salmonid farms

Infectious diseases are a constant threat to industrialised farming, which is characterised by high densities of farms and farm animals. Several mathematical and statistical models on spatio-temporal dynamics of infectious diseases in various farmed host populations have been developed during the last decades. Here we present a spatio-temporal stochastic model for the spread of a disease between and within aquaculture farms. The spread between farms is divided into several transmission pathways, including (i) distance related spread and (ii) other types of contagious contacts. The within-farm infection dynamics is modelled by a susceptible-infected-recovered (SIR) model. We apply this framework to model the spread of pancreas disease (PD) in salmon farming, using data covering all farms producing salmonids over 9 years in Norway. The motivation for the study was partly to unravel the spatio-temporal dynamics of PD in salmon farming and partly to use the model for scenario simulation of PD control strategies. We find, for example, that within-farm infection dynamics vary with season and we provide estimates of the timing from unobserved infection events to disease outbreaks on farms are detected. The simulations suggest that if a strategy involving culling of infectious cohorts is implemented, the number of detected disease outbreaks per year may be reduced by 57% after the full effect has been reached. We argue that the high detail and coverage of data on salmonid production and disease occurrence should encourage the use of simulation modelling as a means of testing effects of extensive control measures before they are implemented in the salmon farming industry.

A spatial simulation model for the dispersal of the bluetongue vector Culicoides brevitarsis in Australia

Background

The spread of Bluetongue virus (BTV) among ruminants is caused by movement of infected host animals or by movement of infected Culicoides midges, the vector of BTV. Biologically plausible models of Culicoides dispersal are necessary for predicting the spread of BTV and are important for planning control and eradication strategies.

Methods

A spatially-explicit simulation model which captures the two underlying population mechanisms, population dynamics and movement, was developed using extensive data from a trapping program for C. brevitarsis on the east coast of Australia. A realistic midge flight sub-model was developed and the annual incursion and population establishment of C. brevitarsis was simulated. Data from the literature was used to parameterise the model.

Results

The model was shown to reproduce the spread of C. brevitarsis southwards along the east Australian coastline in spring, from an endemic population to the north. Such incursions were shown to be reliant on wind-dispersal; Culicoides midge active flight on its own was not capable of achieving known rates of southern spread, nor was re-emergence of southern populations due to overwintering larvae. Data from midge trapping programmes were used to qualitatively validate the resulting simulation model.

Conclusions

The model described in this paper is intended to form the vector component of an extended model that will also include BTV transmission. A model of midge movement and population dynamics has been developed in sufficient detail such that the extended model may be used to evaluate the timing and extent of BTV outbreaks. This extended model could then be used as a platform for addressing the effectiveness of spatially targeted vaccination strategies or animal movement bans as BTV spread mitigation measures, or the impact of climate change on the risk and extent of outbreaks. These questions involving incursive Culicoides spread cannot be simply addressed with non-spatial models.

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.

Impact of temperature, feeding preference and vaccination on Schmallenberg virus transmission in Scotland

First identified in 2011, Schmallenberg virus (SBV) is principally transmitted by Culicoides midges and affects ruminants. Clinical presentation is typified by foetal abnormalities, but despite very high infection rates, relatively few animals present with clinical signs. In this paper we further develop a previously published stochastic mathematical model of SBV spread to investigate the optimal deployment of a vaccine for SBV in Scotland, a country that has experienced only sporadic and isolated cases of SBV. We consider the use of the vaccine under different temperatures and explore the effects of a vector preference for feeding on cattle. We demonstrate that vaccine impact is optimised by targeting it at the high risk areas in the south of Scotland, or vaccinating only cattle. At higher than average temperatures, and hence increased transmission potential, the relative impact of vaccination is considerably enhanced. Vaccine impact is also enhanced if vectors feed preferentially on cattle. These findings are of considerable importance when planning control strategies for SBV and also have important implications for management of other arboviruses such as Bluetongue virus. Environmental determinants and feeding preferences should be researched further to inform development of effective control strategies.

Culicoides (Diptera: Ceratopogonidae) host preferences and biting rates in the Netherlands: comparing cattle, sheep and the black-light suction trap

Host preference is an important determinant of feeding behaviour in biting insects and a critical component in the transmission of vector-borne diseases. The aim of the study was to quantify Culicoides (Diptera: Ceratopogonidae) host preferences and biting rates using tethered livestock at pasture (a dairy cow and a sheep) and to compare the numbers of biting midges aspirated off them to those captured simultaneously in a black-light suction trap acting as a surrogate host. Culicoides collections were made hourly over seven hours (from five hours before official sunset to two hours after) between 27 May and 19 June, 2013 at a dairy farm (eastern Netherlands). The study involved 13 replicates of a site × host randomised design. Culicoides collected by black-light suction trap and by direct aspiration were identified to species morphologically and age-graded. The C. obsoletus complex, C. dewulfi and C. pulicaris predominated on the back and flanks of the animals, C. punctatus on the belly, and C. chiopterus on the legs. Using comparable collection periods, 9.3 times (95% confidence interval: 8.6-10.0) more Culicoides were caught on the cow than on the sheep and 25.4 times (95% confidence interval: 18.4-35.1) less in the black-light suction trap compared to the sheep. Mean Culicoides biting rates on the cow across the 7-h collection period were 4.6, 3.5, 1.0, 1.0 and 0.5 min(-1) for C. dewulfi, the C. obsoletus complex, C. chiopterus, C. punctatus and C. pulicaris, respectively; for the sheep they were 0.6, 0.4 and 0.1 min(-1) for the C. obsoletus complex, C. dewulfi and C. punctatus, respectively. Though midges were aspirated off livestock during each of the seven hours, they only began to appear in the black-light suction trap 5h later, from sunset onwards. After sunset, its efficacy improved markedly, but occurred when midge activity overall had begun to decline. Though it was quite accurate in ranking Culicoides species abundance, the black-light suction trap proved to be of limited value for determining hours of peak biting activity, levels of abundance, and host preference, in Culicoides.

Inferences about the transmission of Schmallenberg virus within and between farms

In the summer of 2011 Schmallenberg virus (SBV), a Culicoides-borne orthobunyavirus, emerged in Germany and The Netherlands and subsequently spread across much of Europe. To draw inferences about the transmission of SBV we have developed two models to describe its spread within and between farms. The within-farm model was fitted to seroprevalence data for cattle and sheep farms in Belgium and The Netherlands, with parameters estimated using approximate Bayesian computation. Despite the short duration of viraemia in cattle and sheep (mean of 3–4 days) the within-farm seroprevalence can reach high levels (mean within-herd seroprevalence >80%), largely because the probability of transmission from host to vector is high (14%) and SBV is able to replicate quickly (0.03 per day-degree) and at relatively low temperatures (threshold for replication: 12.3 °C). Parameter estimates from the within-farm model were then used in a separate between-farm model to simulate the regional spread of SBV. This showed that the rapid spread of SBV at a regional level is primarily a consequence of the high probability of transmission from host to vector and the temperature requirements for virus replication. Our results, obtained for a region of the UK in a typical year with regard to animal movements, indicate that there is no need to invoke additional transmission mechanisms to explain the observed patterns of rapid spread of SBV in Europe. Moreover, the imposition of movement restrictions, even a total movement ban, has little effect on the spread of SBV at this scale.

Seasonal variation and impact of waste-water lagoons as larval habitat on the population dynamics of Culicoides sonorensis (Diptera:Ceratpogonidae) at two dairy farms in northern California

The Sacramento (northern Central) Valley of California (CA) has a hot Mediterranean climate and a diverse ecological landscape that is impacted extensively by human activities, which include the intensive farming of crops and livestock. Waste-water ponds, marshes, and irrigated fields associated with these agricultural activities provide abundant larval habitats for C. sonorensis midges, in addition to those sites that exist in the natural environment. Within this region, C. sonorensis is an important vector of bluetongue (BTV) and related viruses that adversely affect the international trade and movement of livestock, the economics of livestock production, and animal welfare. To characterize the seasonal dynamics of immature and adult C. sonorensis populations, abundance was monitored intensively on two dairy farms in the Sacramento Valley from August 2012- to July 2013. Adults were sampled every two weeks for 52 weeks by trapping (CDC style traps without light and baited with dry-ice) along N-S and E-W transects on each farm. One farm had large operational waste-water lagoons, whereas the lagoon on the other farm was drained and remained dry during the study. Spring emergence and seasonal abundance of adult C. sonorensis on both farms coincided with rising vernal temperature. Paradoxically, the abundance of midges on the farm without a functioning waste-water lagoon was increased as compared to abundance on the farm with a waste-water lagoon system, indicating that this infrastructure may not serve as the sole, or even the primary larval habitat. Adult midges disappeared from both farms from late November until May; however, low numbers of parous female midges were detected in traps set during daylight in the inter-seasonal winter period. This latter finding is especially critical as it provides a potential mechanism for the "overwintering" of BTV in temperate regions such as northern CA. Precise documentation of temporal changes in the annual abundance and dispersal of Culicoides midges is essential for the creation of models to predict BTV infection of livestock and to develop sound abatement strategies.

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.