Preparation for a potential outbreak of bluetongue virus in Ireland: surveillance design to estimate local prevalence after an initial case detection

Preparation for a potential outbreak of bluetongue virus in Ireland: surveillance design to estimate local prevalence after an initial case detection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Preparation for a potential outbreak of bluetongue virus in Ireland: surveillance design to estimate local prevalence after an initial case detection Miriam Casey Bryars, Jamie A. Tratalos, Jamie M. Madden, Guy McGrath This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7106740/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Bluetongue virus serotype 3, emerged in northern Europe in 2023 and 2024. As of June 2025, Ireland is bluetongue free. However, to inform control decisions in the event of a possible incursion, a surveillance plan to detect cases and estimate prevalence is required. We created an active surveillance plan for 20km radius temporary control zones (TCZs) after initial case detection. Potential TCZs (n = 1062) covering Ireland were generated, and surveillance sample sizes were estimated based on cattle data in each TCZ. A two-stage (herd and animal level) design accounted for within-herd clustering. We simulated implementation of the surveillance plan in each TCZ to understand surveillance performance in the Irish cattle population. With simulated between-herd prevalence of 5%, within-herd prevalence of 30%, and using reverse transcriptase polymerase chain reaction with perfect specificity and 99% sensitivity, surveillance to estimate prevalence was adequate when between 39 and 61 herds per TCZ were sampled. With simulated between-herd prevalence of 30%, 10-11 herds per TCZ needed to be sampled to estimate prevalence. Within herds, sampling 10–11 cattle was sufficient for prevalence estimation. We integrated Shiny and ArcGIS web applications to allow users to simulate different scenarios under different settings. These include different test sensitivity and specificity, and different within- and between- herd prevalence contexts. This interface presents infected herds sampled, and true- and false- positives and negatives in a variety of conditions. Evidence from this scenario analysis can be integrated into a multi-pronged early warning and potential follow-up surveillance programme to facilitate decision making in the event of an incursion of BTV into Ireland. The URL for the web application associated with this paper is: https://www.arcgis.com/apps/dashboards/16722dde78d240f4a96303173bc6da2c Bluetongue surveillance prevalence Ireland preparedness Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The emergence of bluetongue virus serotype 3 (BTV-3) in northern Europe has caused substantial economic and animal welfare impacts throughout 2023 and 2024 (1–3). Bluetongue (BT) is a disease of ruminants and new world camelids caused by infection with bluetongue virus (BTV), which is an Orbivirus from the Reoviridae family. It is primarily transmitted by midges of the genus Culicoides , but vertical transmission can also occur, as can transmission via needles or semen (4). Evidence of introduction and spread of the BTV-3 was reported in England in 2023, and again since August 2024. To date, Ireland has escaped the spread of BTV in northern Europe. However, the potential for introduction and spread of midge borne infection has been evidenced by the emergence of Schmallenberg orthobunyavirus (SBV) in Ireland over the past decade (5–7). BTV serotypes 1-24 are notifiable and considered category C diseases by the European Animal Health Law. This means that member states can implement an optional eradication programme recognised by the European Union (EU). The previous incursion of BTV-8 into northern Europe had a large impact on the livestock industry but it was eventually eradicated in many countries by a comprehensive vaccination programme (8). Evidence on the epidemiological and control features of BTV-3 in northern Europe is still emerging. It spread extensively in the Netherlands and surrounding countries in 2023 and 2024, and emergency authorisation of an inactivated BTV-3 vaccine as well as biosecurity advice about indoor housing and ventilation to avoid midges have been the main preventative measures (4). Evidence is pending on whether BTV-3 vaccination will control this outbreak and its impacts, and also whether transmission will be as extensive in Britain as it has been in the other affected countries. BTV-3 could be introduced into Ireland through (a) importation of an infected animal or foetus (b) importation of infected biological material such as blood, or germinal products such as semen or embryos (c) windborne (or other) movement of midges bearing BTV-3 from an infected region. Since BTV-3 cases were detected in England in 2023, Ireland has suspended the imports of susceptible species from Great Britain due to the inability to meet import certification requirements. Germinal products may still be imported from Great Britain into Ireland and the EU, but only if the relevant animal health requirement for BT can be certified. The movement of susceptible animals and germinal products from Northern Ireland is permitted when carried out according to usual conditions. Under EU law, the movement of susceptible species and germinal products from EU countries is permitted, but it is not without risk and can only take place where detailed certification requirements can be met. However, given the evolving disease situation in Europe and the risk that purchased animals may not meet intra-EU movement certification criteria, the Department of Agriculture, Food and the Marine’s (DAFMs), National Disease Control Centre (NDCC) advises farmers to avoid these movements if at all possible (9). Regardless of whether they are travelling from or through a country affected by BT, all ruminants and camelid animals originating from mainland Europe must be isolated on arrival and commence a programme of post-entry testing within 5 days of entry into Ireland, which is performed by DAFM. As well as enhanced passive surveillance, the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) atmospheric dispersion model (10) is used in real-time to estimate the likelihood of windborne transfer of midges from a BTV infected area, given weather conditions. DAFM uses this tool, described previously in the context of Schmallenberg virus incursion (6), to inform programmes of enhanced passive surveillance. As well as detection of typical clinical signs and post mortem findings, commonly used tests to diagnose BT include detection of viral RNA in whole blood samples by reverse transcriptase polymerase chain reaction RT-PCR (11,12), detection of antibodies against BTV by serology (12) (although previous infection and vaccination cannot be differentiated and the test is not serotype specific) and adaption of the serology ELISA to test for antibodies in either individual or bulk milk samples (13). Other more involved tests include virus isolation, genetic typing and sequencing, and virus neutralisation testing. It is unknown how extensive BTV transmission in Ireland would be after an incursion. Effective surveillance to detect cases in a potential temporary control zone ( TCZ) could assist Irish stakeholders in making informed decisions about control. Therefore, in this report, we explore a scenario where an incursion of BTV has already been detected, and detection of further cases in a 20km radius TCZ around the initial case is required to estimate local prevalence. The purpose of such surveillance would be to enable an understanding of the extent of BTV transmission and to subsequently inform control measures. Methods Definitions Sensitivity: The conditional probability of testing positive given the animal is truly infected. (Proportion of infected animals that test positive). Specificity : The conditional probability of testing negative given the animal is truly uninfected. (Proportion of uninfected animals that test negative). Between-herd prevalence: The proportion of infected herds (a herd is considered infected if it has at least one infected animal). Within-herd prevalence: The proportion of infected animals within infected herds. Apparent prevalence: The proportion of test positive units (animals or herds) out of all units tested. Positive predictive value: The probability that an animal with a positive test result has the disease. This depends on test sensitivity, test specificity and disease prevalence. Negative predictive value: The probability that an animal with a negative test result does not have the disease. This depends on test sensitivity, test specificity and disease prevalence. Surveillance design Potential targeted surveillance design for a 20km radius TCZ after the detection of an initial case of BTV-3 is described here. This surveillance would be in addition to (a) enhanced passive surveillance for clinical and postmortem signs of BT (b) post import testing and (c) targeted surveillance based on the likely sites of windborne midge dispersion. There is also the possibility for additional national surveillance approaches including bulk milk and syndromic surveillance in dairy herds. These additional surveillance approaches are not described here. Annex V, Part II, Chapter 1, Section 4 of Commission Delegated Regulation (EU) 2020/689, a regulation supplementing the overarching European Animal Health Law (Regulation (EU) 2016/429), in the context of an approved eradication programme, describes the requirements for the recognition of the active surveillance for infection with BTV. These are based on geographical units of either grid squares of 45km * 45km but can be adapted to fit with natural geographical boundaries, such as counties or national administrative zones. These may include the TCZs planned by Ireland in the event of a BTV-3 outbreak. Surveillance must be carried out at least annually, or monthly during the season of vector activity where regular information is needed due to the risk of the infection spreading. The Irish vector active season is defined as from April to December in Ireland (14,15). According to EU legislation, the surveillance programme must have the capacity to detect, with a 95% level of confidence, infection with BTV with a target prevalence rate of 5%. This requirement does not distinguish between within- and between- herd prevalence. However, despite being a vector borne disease, herd level clustering of BT is recognised (16,17). A case study in France showed that an animal-level only surveillance programme, based on a 20% target overall animal level prevalence, may not have detected BTV-8 present between 2013 and 2015, and the programme was subsequently updated to consider herd level clustering and lower target prevalence (8). Surveillance in the UK also took herd level clustering into account and translated the EU target of 5% overall animal-level prevalence into 50% between-herd prevalence and 10% within-herd prevalence (18). These surveillance programmes face the challenge of expected low prevalence (requiring more sampling to detect cases) with the desire to limit the number of farms and animals that would need to be sampled due to resource constraints (8,18). For these reasons, we design a two stage (herd and animal level) surveillance plan, considering expected between-herd and within-herd prevalence of BTV-3. For logistical reasons, we aim to minimise the numbers of herds requiring visits. Our geographical sampling unit is the 20km radius TCZ. Assumptions about expected prevalence in the absence of historical BTV prevalence data from Ireland As Ireland has never had a BT outbreak, assumptions for prevalence are based on data from the BTV-8 outbreak in England in 2007 and 2008 (19) and on the recent BTV-3 outbreak in 2023 in the Netherlands (2,13). We supply a summary of these data in the Supplementary Material. Transmission characteristics of BTV-3 may be different in Ireland due to temperature, weather, environmental, and livestock management conditions (8,16–18) as well as the ecology and vector competence of the Culicoides species present in Ireland (8,14,15). Based on English and Dutch data (2,13,19) we tentatively consider estimates of between-herd prevalence of 5% to be reasonable for an early stage BTV-3 spread, and 30% as BTV-3 is becoming established in a region. If considering 30% of animals in 5% of herds, the overall animal level prevalence is 1.5%, well within the EU target of 5% and closer to aligning with European Food Safety Authority (EFSA) advice to consider animal level prevalence as low as 1% (8). As our understanding of expected BTV-3 prevalence in Ireland may evolve, or stakeholders may opt to use an alternative diagnostic test, we have built a web application to explore a range of expected within- and between-herd prevalences, as well as different diagnostic test sensitivity and specificity values (described in more detail later). Irish cattle data Herd locations were derived from computing the centroid of the largest fragment of land for each farm based on DAFM’s Land Parcel Information System (LPIS) for herds registered in 2024 (20). Herd size was estimated from the number of animals per herd subjected to a bTB test in 2024 and filtered to remove repeat tests. These data were extracted from DAFM's Animal Health Computer System (AHCS). Definition of TCZs Overlapping circles (N = 1064) with a radius of 20km were designed to cover the island of Ireland, representing hypothetical TCZs (Figure 1). Based on the herd centroids, a list of all herds and herd sizes in each TCZ was generated for use in sample size estimation and scenario exploration. Having overlapping zones allows for estimates to be generated in the zone with its centre closest to an area of interest. Species selection Cattle attract more midges than sheep (8,21,22). Within herd apparent prevalence has been found to be higher in cattle than sheep (2). Early reports from the Netherlands suggest that BTV-3 nucleic acid can be detected beyond 180 days in cattle compared to 100 days in sheep (4). A large review by the EFSA estimated that live BTV could also be isolated for a longer period from cattle (75 th percentile 50 days for cattle compared to 30 days for sheep), whilst suggesting a detection window of 4-5 months for detection of BTV nucleic acid by RT-PCR in both species (8). Reports on the BTV-3 epidemic in the Netherlands suggest a similar RT-PCR detection window, describing PCR positivity in cattle 180 days after infection (4). Based on this evidence, we considered cattle surveillance to better enable case detection and made our estimates based on sampling this species only. Testing for evidence of bluetongue virus infection Diagnostic characteristics for RT-PCR For our main report, we assume that animals selected for testing are tested with a RT-PCR to detect BTV genome (11). Similarly to Grace et al. (2017), we assumed a sensitivity of 99% and a specificity of 100% (18). We chose to conduct simulations using these diagnostic characteristics as high specificity may be desirable when ascertaining prevalence in the early stages of an outbreak. Diagnostic characteristics for competition ELISA A competition ELISA to detect antibodies against BTV (ID SCREEN® BLUETONGUE COMPETITION[1]) is also available in Ireland. An EFSA review of papers published up to 2016, estimated competition ELISA (cELISA) sensitivity in cattle of 89.2% (range 83.0% –100%) and specificity of 98.4% (range 95.8%–99.5%). We report positive and negative predictive values based on EFSA reported cELISA diagnostic characteristics, but do not report full simulation results, as the lower specificity may not be appropriate for the low prevalence / early stage outbreak context, given the likelihood of false positive results. Web application to explore alternative scenarios To allow exploration of a range of scenarios with different between- and within- herd prevalences, and for alternative diagnostic test sensitivities and specificities, we translated our simulation code into a Shiny web application (23). We used an ArcGIS dashboard (24) to present our TCZs and integrated both applications to be presented alongside each other online. Users can select a 20km radius hexagon and model surveillance outcomes. These include infected herds tested, true and false positive herds, false negative herds, as well as a range of other measures. The joint application can accessed here: https://www.arcgis.com/apps/dashboards/16722dde78d240f4a96303173bc6da2c Sampling logistics We sought to minimise the number of farms visited and did not limit how many animals within the herd were required to be sampled. Further targeting Larger cattle herds were more likely to test positive for BT in the English 2007-2008 outbreak (19). At animal level, risk factor studies outside of Europe reported either no effect (25) or a protective effect (26) of increasing herd size on the probability of an individual animal testing positive. Given that minimising herds visited carries lower costs than minimising animals sampled, and also given that larger herds are often associated with better facilities for sampling, we selected herds of greater than 100 cattle in size. A similar logistics-based rationale was given for a previous BT survey in England (18). Our choice of 20km TCZ based geographical surveillance units is supported by the literature on BTV transmission distances and Culicoides biology. A previous study of insect vector dispersal stratified it into (a) short-distance movements, independent of wind, and (b) long-distance wind-aided migratory movements (27). Hendrickx et al. (2008), in their analysis of the BT epidemic in northern Europe in 2006, estimated that half of the new weekly cases were distributed within 5 km of the closest case reported in the previous week, and 95% of new cases were distributed within 31 km (28). Using data from Germany, France, Belgium, the Netherlands and Luxembourg, Sedda et al. (2012) similarly estimated that 54% of outbreaks occurred through transmission of BTV within 5 km or less, and 92% of outbreaks were due to transmission within 31 km. No differences between upwind or downwind movement of infection were reported (29). Gubbins et al. (2014), using data from Belgium and the Netherlands, predicted regional spread of BTV with a mean radius of 23.2km with no movement restrictions, and 9.4km with movement restrictions (30). These data suggest that the planned 20km radius TCZ around the first case identified is a reasonable geographical unit for surveillance. Separately, and outside the remit of this current paper, the risk of rarer longer distance wind-associated midge migrations, as described by Hendrickx et al. (2008) (28), can be assessed using the “Hysplit” wind-dispersion modelling tool (6). Sample size estimation We used the “EpiR” package (31) to estimate the sample size required in each TCZ. The two-stage representative survey design tool in the “EpiR” package allowed consideration of both within- and between- herd prevalence. We calculated how many herds and how many animals from within each herd need to be sampled to be 95% confident of detecting disease at the herd and individual animal level. Based on our summaries of English and Dutch data (summarised in Supplementary Materials). We consider estimates of between-herd prevalence of 5% to be reasonable for an early stage BTV-3 spread, and, conservatively, 30% if BTV-3 has become established in a region. Within herd prevalence of 30% was assumed. Scenario simulation to explore effectiveness of surveillance To explore the effectiveness of our planned surveillance, including levels of false negative and false positive results under different test interpretation conditions, we conducted a simulation study. Static between- and within herd prevalence was assumed. The steps in the simulation were as follows. For each TCZ, infected herds were simulated based on the expected between herd prevalence and the total count of cattle herds. For each potential TCZ, herds with 100 cattle or more were selected. The number of herds required to be sampled, based on the “EpiR” based sample size calculation was randomly selected from the herds defined in point (ii). If any of the selected herds had been simulated as infected herds, infected animals within that herd were simulated based on expected within herd prevalence and herd size. From each selected herd, the count of animals required to be sampled, based on sample size calculation, was randomly selected. Test results in selected animals were simulated based on the animal’s simulated true infection status, and test sensitivity and specificity. Simulated true and apparent prevalence was compared, and the levels of false positives and false negatives under different test and prevalence conditions were reviewed. Our simulation code and an example subset of data are available here: https://github.com/miriamcasey/BTV_surveillance Software Data processing was performed in Microsoft SQL Server 2012. The GIS software package ArcGIS Pro 3.3.0 was used to generate the 20km buffers representing the TCZs and to assign intersecting herds to them. All other analyses were conducted in the R statistical environment (32) and as previously highlighted, the “EpiR” package (31) was used for sample size calculations. Web interfaces were built using Shiny (23) for simulations and the ArcGIS Dashboard (24) for the TCZ map. These were integrated so that the TCZ map and the simulation tool were visible side-by-side to users. [1] Innovative Diagnostics , 310 rue Louis Pasteur – 34790 Grabels, France Results Population summary 6,852,137 cattle in 101,355 herds were present in the 2022 data. Herd size ranged from a single bovine to 2,654 cattle, with a mean of 68, a median of 36 and an interquartile range (IQR) of 16 to 82. More than 100 cattle were present in 19.9% of the herds. In the 1,064 hypothetical 20km radius TCZs herd counts ranged from 68 to 3,548 (median = 1,658, IQR = 1,125–2,115), and cattle counts ranged from 1,372 to 242,297 (median = 120,536, IQR = 65,162–158,920). Exploration of positive and negative predictive values with different animal-level disease prevalence. RT-PCR testing positive and negative predictive values A 5% between herd prevalence and a 30% within herd prevalence is consistent with a 1.5% overall animal level prevalence. A 30% between herd prevalence and a 30% within herd prevalence is consistent with a 9% overall animal prevalence. In our main scenario, we assume perfect diagnostic test specificity and therefore perfect Positive Predictive Value (PPV). Given our sensitivity of 99% and maximum 9% overall animal level prevalence, the negative predictive value (NPV) was also almost perfect. Competition ELISA positive and negative predictive values. Our web-application can demonstrate how PPV and NPV decline in some prevalence and diagnostic test scenarios. For example, EFSA reported specificity of 98.4% for the cELISA ( 12 ) which would be associated with high levels of false positives in a low prevalence context. With a specificity of 98.4% and a sensitivity of 89.2%, the PPV is 45.9% for between-herd prevalence of 5% and within-herd prevalence of 30%. However, if the prevalence increased to levels seen in the Netherlands’ BTV-3 epidemic, false-positives would be a lesser issue. For example, with 50% between and within-herd prevalence, the PPV, based on EFSA reported cELISA sensitivity and specificity estimates, is 94.9%. With 5% between-herd prevalence and 30% within-herd prevalence, NPV related to the EFSA reported cELISA diagnostic characteristics was 99.8%. With the 50% between and within herd prevalence cELISA NPV was 96.4%. Sample size estimation for RT-PCR Using the “EpiR” function for two stage surveillance design (“rsu.ssep.rs2st”), for a between-herd prevalence of 5%, and a within-herd prevalence of 30%, unspecified large herd and animal population sizes, 95% confidence of detecting disease at the herd and individual animal level, the sample size required was 62 herds per TCZ with 9 randomly selected animals per herd sampled. When between- and within- herd prevalence were set to 30%, 9 herds and 9 animals per herd were required to be sampled. Figure 2 shows the count of herds to be tested given a range of between herd prevalences. Figure 3 shows the count of cattle within selected herds to be tested, given a range of within-herd prevalences. When specific lists of herds and herd sizes of herds containing more than 100 cattle each of the 862 selected TCZs were inputted into the “EpiR” tool, with between-herd prevalence of 5%, ranged between 39 and 61 herds were required to be sample per TCZ. Equivalent estimates for a between-herd prevalence of 30%, were between 10 and 11 herds. With either scenario the within-herd prevalence was set at 30%, and between 10 and 11 cattle per selected herd required sampling. Scenario simulation to explore the effectiveness of surveillance with 5% between herd prevalence Of the 1064 TCZs, 862 (81%) had greater than 70 herds with more than 100 cattle within them. Because our surveillance design was focussed on herds with 100 cattle or more, and to enable a standardised approach for each TCZ, we selected these TCZs (> 70 herds with ≥ 100 cattle) for our simulation to explore the application of the sample size estimation to Irish TCZs. The count of herds in these selected TCZs ranged from 203 to 2489. With between-herd prevalence set at 5%, and within herd prevalence set at 30%, 100 simulations of each of these 862 hypothetical TCZs yielded the following results, reported as median measures from 100 simulations for each TCZ. Simulated between-herd and within-herd prevalence were 5% and 30% respectively, as expected. Based on this and the total herd count, between 10 and 127 herds per TCZ were infected. The number of herds tested per TCZ (based on inputting the list of herds and herd sizes from each TCZ into the “EpiR” tool) ranged from 39 to 61 and a median of between 1 and 3 infected herds were sampled per TCZ. Amongst all the repeat simulation experiments, of the 862 TCZs, few had infected herds sampled in all 100 repeat simulations. The median proportion of simulations with zero infected herds sampled was 5% (interquartile range (IQR) 4% – 8%) (Fig. 4 ). Overall, simulation outputs aligned with “EpiR” tool calculations, with the surveillance programme detecting the expected between- and within- herd prevalence. Amongst the cattle selected for testing, median counts of test positive infected cattle (true positives) in each TCZ ranged from 4 to 10 per TCZ and counts of test negative uninfected cattle ranged from 385 to 604. There were no false positive or false negative results simulated. Within test positive herds, mean apparent animal level prevalence ranged from 25–32%. Apparent herd level prevalence was between 3% and 6% (Fig. 5 ). Scenario simulation to explore effectiveness of surveillance with 30% between herd prevalence When between- and within-herd prevalence were both set to 30%, between 62 and 745 infected herds, with between 2,144 and 21,563 infected cattle, per TCZ were simulated. At this higher between-herd prevalence, between 10 and 11 herds per TCZ were required to be tested for case detection with both parallel and serial interpretation. Fewer TCZ simulations had zero infected herds sampled (a median of 2 simulations out of 100 replicates). No false positive or false negative cattle or herds were simulated. Apparent between-herd prevalence (based on median estimates from 100 simulations per TCZ) ranged from 20–36% (Fig. 6 ). Discussion Our paper describes a potential scenario of a BT case detection in Ireland and subsequent surveillance to detect cases in a surrounding 20km control zone. The BTV-8 outbreak in Europe between 2006 and 2009, and the more recent emergence of BTV-3 in northern Europe and England demonstrate that BTV can disperse widely ( 8 , 9 ) but it is challenging to predict potential BTV-3 transmission patterns in Ireland, given that temperature, wind conditions, season, midge and virus ecology and livestock management practices will all affect potential conditions for spread ( 16 , 17 , 29 , 30 ). Understanding the extent of transmission after an incursion will assist stakeholders’ decision making on control measures, and this requires effective surveillance. This study demonstrates that surveillance to estimate local prevalence, with an assumed between-herd prevalence of 5% and within-herd prevalence of 30%, performs well when test specificity is perfect and test sensitivity is 99%, and when between 39 and 61 herds per TCZ (depending on total herds in the TCZ) are tested. Apparent herd and animal level prevalence from test results were close to the simulated true values. When a higher between-herd prevalence of 30% is assumed, which would be consistent with more extensive transmission, sampling between 10 and 11 herds per TCZ provided an adequate estimate of prevalence. Our 5% and 30% between-herd prevalence assumptions are low compared to some other European contexts, as potential transmission patterns of BTV-3 in Ireland are unknown, and we wished to provide sample size estimates appropriate for the earlier stages of BTV spread, with relatively lower between-herd prevalence. Between-herd prevalence of BTV-3 increased above 60% in the Netherlands ( 3 , 13 ) and for BTV-8 in parts of East Anglia in 2007 and 2008 ( 19 ). We selected RT-PCR diagnostic characteristics for our main analysis, as EFSA suggests a detection window of 4–5 months for detection of BTV nucleic acid in cattle after infection ( 8 ) and it is a highly sensitive and specific assay ( 33 ). Assuming perfect specificity of the RT-PCR meant no issues with false-positives. Our calculation of PPV with a lower diagnostic specificity highlights potential issues with false positives in a low prevalence context and can be explored further using our web application. This can be used in the context of evolving prevalence and understanding of test characteristics. Diagnostic test results are rarely interpreted in isolation. For example, we must integrate consideration that an animal is likely to have BTV RNA detectable in their blood by RT-PCR in advance of generating antibodies against BTV. A positive serological result may reflect exposure to BTV or a related virus, or vaccination against BTV, at any time in the past ( 8 ). Vaccination against BTV has never been implemented in Ireland. However, imported animals may have been vaccinated against or exposed to BTV. Consideration of the local epidemiological situation, including animals with clinical signs, is also necessary. Additionally, there may be increased confidence in a positive result within a cluster of positive results, compared to an isolated positive result in a cluster of negative results. We explored the effects of assuming either lower or higher between-herd prevalence in our scenarios. This is because, given that Ireland has never had a BT outbreak, we cannot predict the characteristics of transmission here. Transmission suitability for BTV varies according to weather and environmental conditions ( 16 , 17 ). Möhlmann et al. ( 17 ) related midge catch data from Italy, Sweden and the Netherlands to BTV risk and reproductive ratio. They reported that midge catches were higher in farm habitats than in wetland or peri-urban habitats, and increased with the previous week’s mean daily precipitation. Within wetlands, variation (in the previous 30 days) in precipitation was associated with higher midge catches (possibly associated with larval sites escaping flooding in these areas). In Italy, the Netherlands and Sweden, midge catches were highest at a temperature of 20–21°C. Catches decreased with increasing mean wind velocity 24 hours before collection. Midge catches also varied more with season in the Netherlands and Sweden than in Italy ( 17 ). It is unknown how the ecology of the midge species in Ireland ( 14 , 15 ) may differ from this. Experiences with SBV over the past decade show that a midge borne virus infecting livestock can establish itself in Ireland ( 5 – 7 ). However, Gubbins et al. ( 30 ) argued that the more extensive spread of SBV compared to BTV in England was due to a higher probability of SBV transmission from host to vector and differing temperature requirements for virus replication, highlighting that virus and vector ecology, including vector competence under different conditions, play an important role in transmission dynamics. These uncertainties about the potential transmission patterns of BTV in Ireland highlight the importance of effective surveillance after an incursion to detect additional cases. If, for example, we are confident that prevalence is low and BTV is not spreading extensively, testing and culling may be a feasible control mechanism. In contrast, mass vaccination, as is currently used in several countries to reduce BTV-3 transmission and impacts, may be more appropriate if BTV reaches a high prevalence. The evolution of European legislation on BTV controls reflects that BTV is making increasing incursions into, and, in some cases, becoming established in, many EU countries. There is recognition that aggressive “stamping out” based eradication policies may not be possible in all cases. The Animal Health Law currently defines BT as a Category C disease allowing countries to engage in optional control programmes, in contrast to its previous status as a “class A” disease requiring strict controls described by the old Council Directive 2000/75/EC, which has been superseded by the Animal Health Law. In whichever way the potential transmission patterns and policy context may evolve, case detection to better understand the extent of BTV spread after a known introduction will help inform the Irish response to an incursion. Our simulation study provides evidence to inform the Irish programme for detection of potential incursion and spread of BTV-3. Declarations Funding This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. Competing interests None declared. Data availability The datasets analysed during this study are available from the Department of Agriculture, Food and the Marine (DAFM), and summarise for each TCZ and be viewed on our web application. The full data are subject to data protection regulations and limitations (https://www.gov.ie/en/organisation-information/ef9f6-data-protection/). All R code for the simulation and an example dataset are available here: https://github.com/miriamcasey/BTV_surveillance Acknowledgements We would like to thank Dr. David Beehan, Superintending Research Officer in the Virology Division of DAFM’s Central Veterinary Research laboratory for sharing information about the diagnostic assays used for BTV detection in Ireland and on laboratory processes. We also acknowledge the inputs of staff at DAFM’s National Disease Control Centre, in particular Veterinary Inspectors Dr. Bernadette Doyle and Dt. Rachael Moran, during the initiation stage of the project and for their comments on the manuscript. We are grateful to Daniel Collins and Dr. Nicola Harvey in the Centre for Veterinary Epidemiology and Risk Analysis for their helpful feedback on our web-application. References Santman-Berends I, van den Brink K, Dijkstra E, van Schaik G, Spierenburg MAH, van den Brom R. The impact of the bluetongue serotype 3 outbreak on sheep and goat mortality in the Netherlands in 2023. Prev Vet Med. 2024;231(May):106289. Van den Brink KMJA, Santman-Berends IMGA, Harkema L, Scherpenzeel CGM, Dijkstra E, Bisschop PIH, et al. Bluetongue virus serotype 3 in the Netherlands; clinical signs, seroprevalences and pathological findings in ruminants. Veterinary Record. 2024;Accepted(July):1–10. Holwerda M, Santman-berends IMGA, Harders F, Engelsma M, Vloet RPM, Dijkstra E, et al. Emergence of Bluetongue Virus. 2024;30(8). Royal GD. Online presentation. 2024 [cited 2024 Sep 7]. Bluetongue 3 outbreak in the Netherlands: Experiences so far. Available from: https://royalgd.webinargeek.com/replay-webinar-bluetongue-3-outbreak-in-the-netherlands-1 Bradshaw B, Mooney J, Ross PJ, Furphy C, O’Donovan J, Sanchez C, et al. Schmallenberg virus cases identified in Ireland. Veterinary Record. 2012;171(21):540–1. McGrath G, More SJ, O’Neill R. Hypothetical route of the introduction of Schmallenberg virus into Ireland using two complementary analyses. Veterinary Record. 2018;182(8):226. Collins AB, Barrett D, Doherty ML, Larska M, Mee JF. Post-epidemic Schmallenberg virus circulation: Parallel bovine serological and Culicoides virological surveillance studies in Ireland. BMC Vet Res [Internet]. 2016;12(1):1–11. Available from: http://dx.doi.org/10.1186/s12917-016-0865-7 EFSA. Bluetongue: Control, surveillance and safe movement of animals. Vol. 15, EFSA Journal. 2017. National Disease Control Centre. Bluetongue Virus Update Number 1 of 2025 [Internet]. 2025. Available from: https://www.animalhealthsurveillance.agriculture.gov.ie/media/animalhealthsurveillance/Bluetongue update no 1 of 2025.pdf [Internet]. 2025. Stein AF, Draxler RR, Rolph GD, Stunder BJB, Cohen MD, Ngan F. Noaa’s hysplit atmospheric transport and dispersion modeling system. Bull Am Meteorol Soc. 2015;96(12):2059–77. Wernike K, Hoffmann B, Beer M. Simultaneous detection of five notifiable viral diseases of cattle by single-tube multiplex real-time RT-PCR. J Virol Methods [Internet]. 2015;217:28–35. Available from: http://dx.doi.org/10.1016/j.jviromet.2015.02.023 More S, Bicout D, Bøtner A, Butterworth A, Depner K, Edwards S, et al. Assessment of listing and categorisation of animal diseases within the framework of the Animal Health Law (Regulation (EU) No 2016/429): bluetongue. EFSA Journal. 2017;15(8). Santman-Berends I, van den Brink K, Mars J, Veldhuis A, Bogt-Kappert C ter. Royal GD netherlands. 2024 [cited 2024 Sep 6]. Prevalence of bluetongue virus serotype 3 in Dutch cattle population. Available from: https://www.gddiergezondheid.nl/Diergezondheid/Onderzoek/Onderzoek-antistoffen-blauwtongvirus McCarthy TK, Bateman A, Nowak D, Lawless A. National BTV Vector Surveillance Programme 2007-2009 Annual Report 2009/2010. Vector Ecology Unit School of Natural Sciences and Martin Ryan Institute, National University of Ireland, Galway. 2010. Collins ÁB, Mee JF, Doherty ML, Barrett DJ, England ME. Culicoides species composition and abundance on Irish cattle farms: Implications for arboviral disease transmission. Parasit Vectors. 2018;11(1):1–13. El Moustaid F, Thornton Z, Slamani H, Ryan SJ, Johnson LR. Predicting temperature-dependent transmission suitability of bluetongue virus in livestock. Parasit Vectors. 2021;14(1):1–14. Möhlmann TWR, Keeling MJ, Wennergren U, Favia G, Santman-Berends I, Takken W, et al. Biting midge dynamics and bluetongue transmission: a multiscale model linking catch data with climate and disease outbreaks. Sci Rep [Internet]. 2021;11(1):1–16. Available from: https://doi.org/10.1038/s41598-021-81096-9 Grace KEF, Papadopoulou C, Floyd T, Avigad R, Collins S, White E, et al. Risk-based surveillance for bluetongue virus in cattle on the south coast of England in 2017 and 2018. Veterinary Record. 2020;187(11):96. DEFRA. Food and Farming Group Veterinary Sciences Core team. 2008 [cited 2024 Sep 5]. p. 1–31 Report on the distribution of bluetongue infection in Great Britain on 15 March 2008. Available. Available from: https://webarchive.nationalarchives.gov.uk/ukgwa/20090731155903/http:/www.defra.gov.uk/animalh/diseases/notifiable/bluetongue/pdf/epi-report080508.pdf Zimmermann J, Fealy RM, Lydon K, Mockler EM, O’Brien P, Packham I, et al. The Irish land-parcels identification system (LPIS) – experiences in ongoing and recent environmental research and land cover mapping. Biology and Environment. 2016;116B(1):53–62. Ayllón T, Nijhof AM, Weiher W, Bauer B, Allène X, Clausen PH. Feeding behaviour of Culicoides spp. (Diptera: Ceratopogonidae) on cattle and sheep in northeast Germany. Parasit Vectors. 2014;7(1):1–9. Elbers ARW, Meiswinkel R. Culicoides (Diptera: Ceratopogonidae) host preferences and biting rates in the Netherlands: Comparing cattle, sheep and the black-light suction trap. Vet Parasitol. 2014;205(1–2):330–7. Chang W, Cheng J, Allaire J, Sievert C, Schloerke B, Xie Y, et al. Shiny: Web Application Framework for R. R package version 1.11.0.9000. [Internet]. 2025. Available from: https://github.com/rstudio/shiny ESRI. ArcGIS Dashboard [Internet]. 2025. Available from: https://developers.arcgis.com/documentation/app-builders/no-code/arcgis-dashboards/introduction-to-arcgis-dashboards/ Ishaq M, Shah SAA, Khan N, Jamal SM. Prevalence and risk factors of bluetongue in small and large ruminants maintained on Government farms in North-western Pakistan. Res Vet Sci. 2023;161(March 2022):38–44. Hwang JM, Kim JG, Yeh JY. Serological evidence of bluetongue virus infection and serotype distribution in dairy cattle in South Korea. BMC Vet Res. 2019;15(1):1–11. Reynolds DR, Chapman JW, Harrington R. The Migration of Insect Vectors of Plant and Animal Viruses. Adv Virus Res. 2006;67(06):453–517. Hendrickx G, Gilbert M, Staubach C, Elbers A, Mintiens K, Gerbier G, et al. A wind density model to quantify the airborne spread of Culicoides species during north-western Europe bluetongue epidemic, 2006. Prev Vet Med. 2008;87(1–2):162–81. Sedda L, Brown HE, Purse B V., Burgin L, Gloster J, Rogers DJ. A new algorithm quantifies the roles of wind and midge flight activity in the bluetongue epizootic in northwest Europe. Proceedings of the Royal Society B: Biological Sciences. 2012;279(1737):2354–62. Gubbins S, Turner J, Baylis M, van der Stede Y, van Schaik G, Abrahantes JC, et al. Inferences about the transmission of Schmallenberg virus within and between farms. Prev Vet Med. 2014;116(4):380–90. Stevenson M, Sergeant E, Heuer C, Marshall J, Sanchez J, Thornton R, et al. Package ‘ epiR .’ 2024. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2024. Available from: http://www.r-project.org. Flannery J, Rajko-Nenow P, Hicks H, Hill H, Gubbins S, Batten C. Evaluating the most appropriate pooling ratio for EDTA blood samples to detect Bluetongue virus using real-time RT-PCR. Vet Microbiol [Internet]. 2018;217(March):58–63. Available from: https://doi.org/10.1016/j.vetmic.2018.03.001 Additional Declarations No competing interests reported. Supplementary Files SupplementarymaterialsIVJbluetonguepaperCaseyetal2025.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 12 Aug, 2025 Reviews received at journal 10 Aug, 2025 Reviewers agreed at journal 30 Jul, 2025 Reviews received at journal 29 Jul, 2025 Reviewers agreed at journal 25 Jul, 2025 Reviewers agreed at journal 22 Jul, 2025 Reviewers invited by journal 15 Jul, 2025 Editor assigned by journal 14 Jul, 2025 Submission checks completed at journal 13 Jul, 2025 First submitted to journal 12 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7106740","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486457163,"identity":"2a2dc241-b760-4abb-87a1-caaae96f0bf6","order_by":0,"name":"Miriam Casey Bryars","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYDACZgaGA3DOBwYGxgZmUrQwziBKC4p2HpAWQqp023kfHviZwyDP33/4mLRtm51sAzvzAbxazA6zGxzs3cZgOONGWpp0bluycQMzWwIBLWwMB3i3MSQw3OAxA2o5kNjAzGNAUMvBv0At8ufPf5O2BGvh/0BQy2GQLQYHctikGSG24NUB0SK7TcJw4400Y8uec8nGbcxsBBx2/hjzx7fbbOTlzh9+eONHmZ1sP//hB/itgQAJEMECJtmIUQ8DzPi9PQpGwSgYBSMWAACRqkIxkPJZbAAAAABJRU5ErkJggg==","orcid":"","institution":"University College Dublin","correspondingAuthor":true,"prefix":"","firstName":"Miriam","middleName":"Casey","lastName":"Bryars","suffix":""},{"id":486457164,"identity":"1ffbee33-2f95-4860-8522-77c0e12a559f","order_by":1,"name":"Jamie A. Tratalos","email":"","orcid":"","institution":"University College Dublin","correspondingAuthor":false,"prefix":"","firstName":"Jamie","middleName":"A.","lastName":"Tratalos","suffix":""},{"id":486457165,"identity":"616909ef-ffd7-4406-a91f-377a22c3c73c","order_by":2,"name":"Jamie M. Madden","email":"","orcid":"","institution":"University College Dublin","correspondingAuthor":false,"prefix":"","firstName":"Jamie","middleName":"M.","lastName":"Madden","suffix":""},{"id":486457167,"identity":"0a566d6f-bfef-45ab-94f0-d55bdfe80bb8","order_by":3,"name":"Guy McGrath","email":"","orcid":"","institution":"University College Dublin","correspondingAuthor":false,"prefix":"","firstName":"Guy","middleName":"","lastName":"McGrath","suffix":""}],"badges":[],"createdAt":"2025-07-12 08:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7106740/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7106740/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87320553,"identity":"5be47221-1732-4835-bd32-8f7d51f2cbd2","added_by":"auto","created_at":"2025-07-22 16:29:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":764934,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHypothetical 20km radius temporary control zones (TCZs).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7106740/v1/7c1fdab984ce02407f7e2131.png"},{"id":87320551,"identity":"70b12fdc-17f3-4042-95c4-781721fa7707","added_by":"auto","created_at":"2025-07-22 16:29:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":89022,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCount of herds required to be tested to detect a range of between herd prevalences. The vertical dotted lines represent between herd prevalences of 5 % and 30% respectively.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7106740/v1/903c2016212dd675f6acd2b7.png"},{"id":87319425,"identity":"acb0a953-ce57-4239-a60a-5a52f765a517","added_by":"auto","created_at":"2025-07-22 16:21:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96663,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCount of cattle within selected herds required to be tested to detect a range of within herd prevalences. The vertical dotted line represents within herd prevalence of 30%.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7106740/v1/821a7b7cbc500fa73d8aced8.png"},{"id":87320556,"identity":"28e8559d-e58e-4ad4-9b35-598fb90a3580","added_by":"auto","created_at":"2025-07-22 16:29:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":63492,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA bar-plot showing counts of simulations out of 100 replicates which had zero infected herds sampled within the temporary control zone (TCZ) on the x-axis and count of TCZs (out of 862) on the y-axis. Simulated true between-herd prevalence was set at 5 %.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7106740/v1/f2534d4ccc27787a55e8731f.png"},{"id":87321383,"identity":"ade96174-288c-487f-b7a3-b566e4a48fee","added_by":"auto","created_at":"2025-07-22 16:37:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":112131,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eApparent between herd prevalence in 50 randomly selected temporary control zones (TCZs) and simulated true between herd prevalence of five percent (purple line). The ends of the whiskers show the maximum and minimum, the box shows the interquartile range. The plot is ordered by median apparent herd prevalence. Only a sample of TCZs are shown, to improve plot clarity.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7106740/v1/072c37d20917d51bb5613ee2.png"},{"id":87319428,"identity":"3e360904-8fbf-4062-84b0-f2a3930ac70f","added_by":"auto","created_at":"2025-07-22 16:21:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":91620,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eApparent between herd prevalence in 50 randomly selected temporary control zones (TCZs) and simulated true between herd prevalence of 30% (purple line). The ends of the whiskers show the maximum and minimum, the box shows the interquartile range. The plot is ordered by median apparent herd prevalence. Only a sample of TCZs are shown, to improve plot clarity.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7106740/v1/a3414668e27e7add0c979a92.png"},{"id":87323074,"identity":"07b0813f-11f0-41c5-8709-f46ac84f9c82","added_by":"auto","created_at":"2025-07-22 16:53:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2368802,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7106740/v1/328ef9b2-fbbc-46ae-8b54-b2212e657b81.pdf"},{"id":87319426,"identity":"96b69a92-9af5-497b-a763-b11b1dc282ff","added_by":"auto","created_at":"2025-07-22 16:21:56","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":40754,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialsIVJbluetonguepaperCaseyetal2025.docx","url":"https://assets-eu.researchsquare.com/files/rs-7106740/v1/b6128e081a7149fb624e1924.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Preparation for a potential outbreak of bluetongue virus in Ireland: surveillance design to estimate local prevalence after an initial case detection","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe emergence of bluetongue virus serotype 3 (BTV-3) in northern Europe has caused substantial economic and animal welfare impacts throughout 2023 and 2024 (1\u0026ndash;3). Bluetongue (BT) is a disease of ruminants and new world camelids caused by infection with bluetongue virus (BTV), which is an \u003cem\u003eOrbivirus\u003c/em\u003e from the \u003cem\u003eReoviridae\u003c/em\u003e family. It is primarily transmitted by midges of the genus \u003cem\u003eCulicoides\u003c/em\u003e, but vertical transmission can also occur, as can transmission via needles or semen (4). Evidence of introduction and spread of the BTV-3 was reported in England in 2023, and again since August 2024. To date, Ireland has escaped the spread of BTV in northern Europe. However, the potential for introduction and spread of midge borne infection has been evidenced by the emergence of Schmallenberg orthobunyavirus (SBV) in Ireland over the past decade (5\u0026ndash;7).\u003c/p\u003e\n\u003cp\u003eBTV serotypes 1-24 are notifiable and considered category C diseases by the European Animal Health Law. This means that member states can implement an optional eradication programme recognised by the European Union (EU). The previous incursion of BTV-8 into northern Europe had a large impact on the livestock industry but it was eventually eradicated in many countries by a comprehensive vaccination programme (8).\u003c/p\u003e\n\u003cp\u003eEvidence on the epidemiological and control features of BTV-3 in northern Europe is still emerging. It spread extensively in the Netherlands and surrounding countries in 2023 and 2024, and emergency authorisation of an inactivated BTV-3 vaccine as well as biosecurity advice about indoor housing and ventilation to avoid midges have been the main preventative measures \u0026nbsp;(4). Evidence is pending on whether BTV-3 vaccination will control this outbreak and its impacts, and also whether transmission will be as extensive in Britain as it has been in the other affected countries.\u003c/p\u003e\n\u003cp\u003eBTV-3 could be introduced into Ireland through (a) importation of an infected animal or foetus (b) importation of infected biological material such as blood, or germinal products such as semen or embryos (c) windborne (or other) movement of midges bearing BTV-3 from an infected region. Since BTV-3 cases were detected in England in 2023, Ireland has suspended the imports of susceptible species from Great Britain due to the inability to meet import certification requirements. Germinal products may still be imported from Great Britain into Ireland and the EU, but only if the relevant animal health requirement for BT can be certified. The movement of susceptible animals and germinal products from Northern Ireland is permitted when carried out according to usual conditions. Under EU law, the movement of susceptible species and germinal products from EU countries is permitted, but it is not without risk and can only take place where detailed certification requirements can be met. However, given the evolving disease situation in Europe and the risk that purchased animals may not meet intra-EU movement certification criteria, the Department of Agriculture, Food and the Marine\u0026rsquo;s (DAFMs), National Disease Control Centre (NDCC)\u0026nbsp;advises farmers to avoid these movements if at all possible (9).\u003c/p\u003e\n\u003cp\u003eRegardless of whether they are travelling from or through a country affected by BT, all ruminants and camelid animals originating from mainland Europe must be isolated on arrival and commence a programme of post-entry testing within 5 days of entry into Ireland, which is performed by DAFM.\u003c/p\u003e\n\u003cp\u003eAs well as enhanced passive surveillance, the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) atmospheric dispersion model (10) is used in real-time to estimate the likelihood of windborne transfer of midges from a BTV infected area, given weather conditions. DAFM uses this tool, described previously in the context of Schmallenberg virus incursion (6), to inform programmes of enhanced passive surveillance.\u003c/p\u003e\n\u003cp\u003eAs well as detection of typical clinical signs and post mortem findings, commonly used tests to diagnose BT include detection of viral RNA in whole blood samples by reverse transcriptase polymerase chain reaction RT-PCR (11,12), detection of antibodies against BTV by serology (12) (although previous infection and vaccination cannot be differentiated and the test is not serotype specific) and adaption of the serology ELISA to test for antibodies in either individual or bulk milk samples (13). Other more involved tests include virus isolation, genetic typing and sequencing, and virus neutralisation testing.\u003c/p\u003e\n\u003cp\u003eIt is unknown how extensive BTV transmission in Ireland would be after an incursion. Effective surveillance to detect cases in a potential temporary control zone\u003cstrong\u003e (\u003c/strong\u003eTCZ) could assist Irish stakeholders in making informed decisions about control. Therefore, in this report, we explore a scenario where an incursion of BTV has already been detected, and detection of further cases in a 20km radius TCZ around the initial case is required to estimate local prevalence. The purpose of such surveillance would be to enable an understanding of the extent of BTV transmission and to subsequently inform control measures.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eDefinitions\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity: \u003c/strong\u003eThe conditional probability of testing positive given the animal is truly infected. (Proportion of infected animals that test positive).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e: The conditional probability of testing negative given the animal is truly uninfected. (Proportion of uninfected animals that test negative).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBetween-herd prevalence: \u003c/strong\u003eThe proportion of infected herds (a herd is considered infected if it has at least one infected animal).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWithin-herd prevalence: \u003c/strong\u003eThe proportion of infected animals within infected herds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eApparent prevalence:\u003c/strong\u003e The proportion of test positive units (animals or herds) out of all units tested.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePositive predictive value:\u003c/strong\u003e The probability that an animal with a positive test result has the disease. This depends on test sensitivity, test specificity and disease prevalence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNegative predictive value:\u003c/strong\u003e The probability that an animal with a negative test result does not have the disease. This depends on test sensitivity, test specificity and disease prevalence.\u003c/p\u003e\n\u003ch2\u003eSurveillance design\u003c/h2\u003e\n\u003cp\u003ePotential targeted surveillance design for a 20km radius TCZ after the detection of an initial case of BTV-3 is described here. This surveillance would be in addition to (a) enhanced passive surveillance for clinical and postmortem signs of BT (b) post import testing and (c) targeted surveillance based on the likely sites of windborne midge dispersion. There is also the possibility for additional national surveillance approaches including bulk milk and syndromic surveillance in dairy herds. These additional surveillance approaches are not described here.\u003c/p\u003e\n\u003cp\u003eAnnex V, Part II, Chapter 1, Section 4 of Commission Delegated Regulation (EU) 2020/689, a regulation supplementing the overarching European Animal Health Law (Regulation (EU) 2016/429), in the context of an approved eradication programme, describes the requirements for the recognition of the active surveillance for infection with BTV. These are based on geographical units of either grid squares of 45km * 45km but can be adapted to fit with natural geographical boundaries, such as counties or national administrative zones. These may include the TCZs planned by Ireland in the event of a BTV-3 outbreak. Surveillance must be carried out at least annually, or monthly during the season of vector activity where regular information is needed due to the risk of the infection spreading. The Irish vector active season is defined as from April to December in Ireland (14,15).\u003c/p\u003e\n\u003cp\u003eAccording to EU legislation, the surveillance programme must have the capacity to detect, with a 95% level of confidence, infection with BTV with a target prevalence rate of 5%. This requirement does not distinguish between within- and between- herd prevalence. However, despite being a vector borne disease, herd level clustering of BT is recognised (16,17). A case study in France showed that an animal-level only surveillance programme, based on a 20% target overall animal level prevalence, may not have detected BTV-8 present between 2013 and 2015, and the programme was subsequently updated to consider herd level clustering and lower target prevalence (8). Surveillance in the UK also took herd level clustering into account and translated the EU target of 5% overall animal-level prevalence into 50% between-herd prevalence and 10% within-herd prevalence (18). These surveillance programmes face the challenge of expected low prevalence (requiring more sampling to detect cases) with the desire to limit the number of farms and animals that would need to be sampled due to resource constraints (8,18).\u003c/p\u003e\n\u003cp\u003eFor these reasons, we design a two stage (herd and animal level) surveillance plan, considering expected between-herd and within-herd prevalence of BTV-3. For logistical reasons, we aim to minimise the numbers of herds requiring visits. Our geographical sampling unit is the 20km radius TCZ.\u003c/p\u003e\n\u003ch2\u003eAssumptions about expected prevalence in the absence of historical BTV prevalence data from Ireland\u003c/h2\u003e\n\u003cp\u003eAs Ireland has never had a BT outbreak, assumptions for prevalence are based on data from the BTV-8 outbreak in England in 2007 and 2008 (19) and on the recent BTV-3 outbreak in 2023 in the Netherlands (2,13). We supply a summary of these data in the Supplementary Material.\u003c/p\u003e\n\u003cp\u003eTransmission characteristics of BTV-3 may be different in Ireland due to temperature, weather, environmental, and livestock management conditions (8,16\u0026ndash;18) as well as the ecology and vector competence of the \u003cem\u003eCulicoides\u003c/em\u003e species present in Ireland (8,14,15). Based on English and Dutch data (2,13,19) we tentatively consider estimates of between-herd prevalence of 5% to be reasonable for an early stage BTV-3 spread, and 30% as BTV-3 is becoming established in a region. If considering 30% of animals in 5% of herds, the overall animal level prevalence is 1.5%, well within the EU target of 5% and closer to aligning with European Food Safety Authority (EFSA) advice to consider animal level prevalence as low as 1% (8). As our understanding of expected BTV-3 prevalence in Ireland may evolve, or stakeholders may opt to use an alternative diagnostic test, we have built a web application to explore a range of expected within- and between-herd prevalences, as well as different diagnostic test sensitivity and specificity values (described in more detail later).\u003c/p\u003e\n\u003ch2\u003eIrish cattle data\u003c/h2\u003e\n\u003cp\u003eHerd locations were derived from computing the centroid of the largest fragment of land for each farm based on DAFM\u0026rsquo;s Land Parcel Information System (LPIS) for herds registered in 2024 (20). Herd size was estimated from the number of animals per herd subjected to a bTB test in 2024 and filtered to remove repeat tests. These data were extracted from DAFM's Animal Health Computer System (AHCS).\u003c/p\u003e\n\u003ch2\u003eDefinition of TCZs\u003c/h2\u003e\n\u003cp\u003eOverlapping circles (N = 1064) with a radius of 20km were designed to cover the island of Ireland, representing hypothetical TCZs (Figure 1). Based on the herd centroids, a list of all herds and herd sizes in each TCZ was generated for use in sample size estimation and scenario exploration. Having overlapping zones allows for estimates to be generated in the zone with its centre closest to an area of interest.\u003c/p\u003e\n\u003ch2\u003eSpecies selection\u003c/h2\u003e\n\u003cp\u003eCattle attract more midges than sheep (8,21,22). Within herd apparent prevalence has been found to be higher in cattle than sheep (2). Early reports from the Netherlands suggest that BTV-3 nucleic acid can be detected beyond 180 days in cattle compared to 100 days in sheep (4). A large review by the EFSA estimated that live BTV could also be isolated for a longer period from cattle (75\u003csup\u003eth\u003c/sup\u003e percentile 50 days for cattle compared to 30 days for sheep), whilst suggesting a detection window of 4-5 months for detection of BTV nucleic acid by RT-PCR in both species (8). Reports on the BTV-3 epidemic in the Netherlands suggest a similar RT-PCR detection window, describing PCR positivity in cattle 180 days after infection (4). Based on this evidence, we considered cattle surveillance to better enable case detection and made our estimates based on sampling this species only.\u003c/p\u003e\n\u003ch2\u003eTesting for evidence of bluetongue virus infection\u003c/h2\u003e\n\u003ch3\u003eDiagnostic characteristics for RT-PCR\u003c/h3\u003e\n\u003cp\u003eFor our main report, we assume that animals selected for testing are tested with a RT-PCR to detect BTV genome (11). Similarly to Grace et al. (2017), we assumed a sensitivity of 99% and a specificity of 100% (18). We chose to conduct simulations using these diagnostic characteristics as high specificity may be desirable when ascertaining prevalence in the early stages of an outbreak.\u003c/p\u003e\n\u003ch3\u003eDiagnostic characteristics for competition ELISA\u003c/h3\u003e\n\u003cp\u003eA competition ELISA to detect antibodies against BTV (ID SCREEN\u0026reg; BLUETONGUE COMPETITION[1]) is also available in Ireland. An EFSA review of papers published up to 2016, estimated competition ELISA (cELISA) sensitivity in cattle of 89.2% (range 83.0% \u0026ndash;100%) and specificity of 98.4% (range 95.8%\u0026ndash;99.5%). We report positive and negative predictive values based on EFSA reported cELISA diagnostic characteristics, but do not report full simulation results, as the lower specificity may not be appropriate for the low prevalence / early stage outbreak context, given the likelihood of false positive results.\u003c/p\u003e\n\u003ch3\u003eWeb application to explore alternative scenarios\u003c/h3\u003e\n\u003cp\u003eTo allow exploration of a range of scenarios with different between- and within- herd prevalences, and for alternative diagnostic test sensitivities and specificities, we translated our simulation code into a Shiny web application (23). We used an ArcGIS dashboard (24) to present our TCZs and integrated both applications to be presented alongside each other online. Users can select a 20km radius hexagon and model surveillance outcomes. These include infected herds tested, true and false positive herds, false negative herds, as well as a range of other measures. The joint application can accessed here:\u003c/p\u003e\n\u003ch2\u003ehttps://www.arcgis.com/apps/dashboards/16722dde78d240f4a96303173bc6da2c\u003c/h2\u003e\n\u003ch2\u003eSampling logistics\u003c/h2\u003e\n\u003cp\u003eWe sought to minimise the number of farms visited and did not limit how many animals within the herd were required to be sampled.\u003c/p\u003e\n\u003ch2\u003eFurther targeting\u003c/h2\u003e\n\u003cp\u003eLarger cattle herds were more likely to test positive for BT in the English 2007-2008 outbreak (19). At animal level, risk factor studies outside of Europe reported either no effect (25) or a protective effect (26) of increasing herd size on the probability of an individual animal testing positive. Given that minimising herds visited carries lower costs than minimising animals sampled, and also given that larger herds are often associated with better facilities for sampling, we selected herds of greater than 100 cattle in size. A similar logistics-based rationale was given for a previous BT survey in England (18).\u003c/p\u003e\n\u003cp\u003eOur choice of 20km TCZ based geographical surveillance units is supported by the literature on BTV transmission distances and \u003cem\u003eCulicoides\u003c/em\u003e biology. A previous study of insect vector dispersal stratified it into (a) short-distance movements, independent of wind, and (b) long-distance wind-aided migratory movements (27). Hendrickx et al. (2008), in their analysis of the BT epidemic in northern Europe in 2006, estimated that half of the new weekly cases were distributed within 5 km of the closest case reported in the previous week, and 95% of new cases were distributed within 31 km (28). Using data from Germany, France, Belgium, the Netherlands and Luxembourg, Sedda et al. (2012) similarly estimated that 54% of outbreaks occurred through transmission of BTV within 5 km or less, and 92% of outbreaks were due to transmission within 31 km. No differences between upwind or downwind movement of infection were reported (29). Gubbins et al. (2014), using data from Belgium and the Netherlands, predicted regional spread of BTV with a mean radius of 23.2km with no movement restrictions, and 9.4km with movement restrictions (30). These data suggest that the planned 20km radius TCZ around the first case identified is a reasonable geographical unit for surveillance. Separately, and outside the remit of this current paper, the risk of rarer longer distance wind-associated midge migrations, as described by Hendrickx et al. (2008) (28), can be assessed using the \u0026ldquo;Hysplit\u0026rdquo; wind-dispersion modelling tool (6).\u003c/p\u003e\n\u003ch2\u003eSample size estimation\u003c/h2\u003e\n\u003cp\u003eWe used the \u0026ldquo;EpiR\u0026rdquo; package (31) to estimate the sample size required in each TCZ. The two-stage representative survey design tool in the \u0026ldquo;EpiR\u0026rdquo; package allowed consideration of both within- and between- herd prevalence. We calculated how many herds and how many animals from within each herd need to be sampled to be 95% confident of detecting disease at the herd and individual animal level. Based on our summaries of English and Dutch data (summarised in Supplementary Materials). We consider estimates of between-herd prevalence of 5% to be reasonable for an early stage BTV-3 spread, and, conservatively, 30% if BTV-3 has become established in a region. Within herd prevalence of 30% was assumed.\u003c/p\u003e\n\u003ch2\u003eScenario simulation to explore effectiveness of surveillance\u003c/h2\u003e\n\u003cp\u003eTo explore the effectiveness of our planned surveillance, including levels of false negative and false positive results under different test interpretation conditions, we conducted a simulation study. Static between- and within herd prevalence was assumed. The steps in the simulation were as follows.\u003c/p\u003e\n\u003col style=\"list-style-type: lower-roman;\"\u003e\n\u003cli\u003eFor each TCZ, infected herds were simulated based on the expected between herd prevalence and the total count of cattle herds.\u003c/li\u003e\n\u003cli\u003eFor each potential TCZ, herds with 100 cattle or more were selected.\u003c/li\u003e\n\u003cli\u003eThe number of herds required to be sampled, based on the \u0026ldquo;EpiR\u0026rdquo; based sample size calculation was randomly selected from the herds defined in point (ii).\u003c/li\u003e\n\u003cli\u003eIf any of the selected herds had been simulated as infected herds, infected animals within that herd were simulated based on expected within herd prevalence and herd size.\u003c/li\u003e\n\u003cli\u003eFrom each selected herd, the count of animals required to be sampled, based on sample size calculation, was randomly selected.\u003c/li\u003e\n\u003cli\u003eTest results in selected animals were simulated based on the animal\u0026rsquo;s simulated true infection status, and test sensitivity and specificity.\u003c/li\u003e\n\u003cli\u003eSimulated true and apparent prevalence was compared, and the levels of false positives and false negatives under different test and prevalence conditions were reviewed.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eOur simulation code and an example subset of data are available here: https://github.com/miriamcasey/BTV_surveillance\u003c/p\u003e\n\u003ch2\u003eSoftware\u003c/h2\u003e\n\u003cp\u003eData processing was performed in Microsoft SQL Server 2012. The GIS software package ArcGIS Pro 3.3.0 was used to generate the 20km buffers representing the TCZs and to assign intersecting herds to them. All other analyses were conducted in the R statistical environment (32) and as previously highlighted, the \u0026ldquo;EpiR\u0026rdquo; package (31) was used for sample size calculations. Web interfaces were built using Shiny (23) for simulations and the ArcGIS Dashboard (24) for the TCZ map. These were integrated so that the TCZ map and the simulation tool were visible side-by-side to users.\u003c/p\u003e\n\u003cp\u003e[1] \u003cstrong\u003eInnovative Diagnostics\u003c/strong\u003e, 310 rue Louis Pasteur \u0026ndash; 34790 Grabels, France\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003ePopulation summary\u003c/p\u003e\u003cp\u003e6,852,137 cattle in 101,355 herds were present in the 2022 data. Herd size ranged from a single bovine to 2,654 cattle, with a mean of 68, a median of 36 and an interquartile range (IQR) of 16 to 82. More than 100 cattle were present in 19.9% of the herds.\u003c/p\u003e\u003cp\u003eIn the 1,064 hypothetical 20km radius TCZs herd counts ranged from 68 to 3,548 (median\u0026thinsp;=\u0026thinsp;1,658, IQR\u0026thinsp;=\u0026thinsp;1,125\u0026ndash;2,115), and cattle counts ranged from 1,372 to 242,297 (median\u0026thinsp;=\u0026thinsp;120,536, IQR\u0026thinsp;=\u0026thinsp;65,162\u0026ndash;158,920).\u003c/p\u003e\u003cp\u003eExploration of positive and negative predictive values with different animal-level disease prevalence.\u003c/p\u003e\u003cp\u003eRT-PCR testing positive and negative predictive values\u003c/p\u003e\u003cp\u003eA 5% between herd prevalence and a 30% within herd prevalence is consistent with a 1.5% overall animal level prevalence. A 30% between herd prevalence and a 30% within herd prevalence is consistent with a 9% overall animal prevalence. In our main scenario, we assume perfect diagnostic test specificity and therefore perfect Positive Predictive Value (PPV). Given our sensitivity of 99% and maximum 9% overall animal level prevalence, the negative predictive value (NPV) was also almost perfect.\u003c/p\u003e\u003cp\u003eCompetition ELISA positive and negative predictive values.\u003c/p\u003e\u003cp\u003eOur web-application can demonstrate how PPV and NPV decline in some prevalence and diagnostic test scenarios. For example, EFSA reported specificity of 98.4% for the cELISA (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) which would be associated with high levels of false positives in a low prevalence context. With a specificity of 98.4% and a sensitivity of 89.2%, the PPV is 45.9% for between-herd prevalence of 5% and within-herd prevalence of 30%. However, if the prevalence increased to levels seen in the Netherlands\u0026rsquo; BTV-3 epidemic, false-positives would be a lesser issue. For example, with 50% between and within-herd prevalence, the PPV, based on EFSA reported cELISA sensitivity and specificity estimates, is 94.9%. With 5% between-herd prevalence and 30% within-herd prevalence, NPV related to the EFSA reported cELISA diagnostic characteristics was 99.8%. With the 50% between and within herd prevalence cELISA NPV was 96.4%.\u003c/p\u003e\u003cp\u003eSample size estimation for RT-PCR\u003c/p\u003e\u003cp\u003eUsing the \u0026ldquo;EpiR\u0026rdquo; function for two stage surveillance design (\u0026ldquo;rsu.ssep.rs2st\u0026rdquo;), for a between-herd prevalence of 5%, and a within-herd prevalence of 30%, unspecified large herd and animal population sizes, 95% confidence of detecting disease at the herd and individual animal level, the sample size required was 62 herds per TCZ with 9 randomly selected animals per herd sampled. When between- and within- herd prevalence were set to 30%, 9 herds and 9 animals per herd were required to be sampled.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the count of herds to be tested given a range of between herd prevalences. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the count of cattle within selected herds to be tested, given a range of within-herd prevalences.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhen specific lists of herds and herd sizes of herds containing more than 100 cattle each of the 862 selected TCZs were inputted into the \u0026ldquo;EpiR\u0026rdquo; tool, with between-herd prevalence of 5%, ranged between 39 and 61 herds were required to be sample per TCZ. Equivalent estimates for a between-herd prevalence of 30%, were between 10 and 11 herds. With either scenario the within-herd prevalence was set at 30%, and between 10 and 11 cattle per selected herd required sampling.\u003c/p\u003e\u003cp\u003eScenario simulation to explore the effectiveness of surveillance with 5% between herd prevalence\u003c/p\u003e\u003cp\u003eOf the 1064 TCZs, 862 (81%) had greater than 70 herds with more than 100 cattle within them. Because our surveillance design was focussed on herds with 100 cattle or more, and to enable a standardised approach for each TCZ, we selected these TCZs (\u0026gt;\u0026thinsp;70 herds with \u0026ge;\u0026thinsp;100 cattle) for our simulation to explore the application of the sample size estimation to Irish TCZs. The count of herds in these selected TCZs ranged from 203 to 2489.\u003c/p\u003e\u003cp\u003eWith between-herd prevalence set at 5%, and within herd prevalence set at 30%, 100 simulations of each of these 862 hypothetical TCZs yielded the following results, reported as median measures from 100 simulations for each TCZ.\u003c/p\u003e\u003cp\u003eSimulated between-herd and within-herd prevalence were 5% and 30% respectively, as expected. Based on this and the total herd count, between 10 and 127 herds per TCZ were infected. The number of herds tested per TCZ (based on inputting the list of herds and herd sizes from each TCZ into the \u0026ldquo;EpiR\u0026rdquo; tool) ranged from 39 to 61 and a median of between 1 and 3 infected herds were sampled per TCZ.\u003c/p\u003e\u003cp\u003eAmongst all the repeat simulation experiments, of the 862 TCZs, few had infected herds sampled in all 100 repeat simulations. The median proportion of simulations with zero infected herds sampled was 5% (interquartile range (IQR) 4% \u0026ndash; 8%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOverall, simulation outputs aligned with \u0026ldquo;EpiR\u0026rdquo; tool calculations, with the surveillance programme detecting the expected between- and within- herd prevalence.\u003c/p\u003e\u003cp\u003eAmongst the cattle selected for testing, median counts of test positive infected cattle (true positives) in each TCZ ranged from 4 to 10 per TCZ and counts of test negative uninfected cattle ranged from 385 to 604. There were no false positive or false negative results simulated. Within test positive herds, mean apparent animal level prevalence ranged from 25\u0026ndash;32%. Apparent herd level prevalence was between 3% and 6% (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eScenario simulation to explore effectiveness of surveillance with 30% between herd prevalence\u003c/p\u003e\u003cp\u003eWhen between- and within-herd prevalence were both set to 30%, between 62 and 745 infected herds, with between 2,144 and 21,563 infected cattle, per TCZ were simulated. At this higher between-herd prevalence, between 10 and 11 herds per TCZ were required to be tested for case detection with both parallel and serial interpretation. Fewer TCZ simulations had zero infected herds sampled (a median of 2 simulations out of 100 replicates). No false positive or false negative cattle or herds were simulated. Apparent between-herd prevalence (based on median estimates from 100 simulations per TCZ) ranged from 20\u0026ndash;36% (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur paper describes a potential scenario of a BT case detection in Ireland and subsequent surveillance to detect cases in a surrounding 20km control zone. The BTV-8 outbreak in Europe between 2006 and 2009, and the more recent emergence of BTV-3 in northern Europe and England demonstrate that BTV can disperse widely (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) but it is challenging to predict potential BTV-3 transmission patterns in Ireland, given that temperature, wind conditions, season, midge and virus ecology and livestock management practices will all affect potential conditions for spread (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Understanding the extent of transmission after an incursion will assist stakeholders\u0026rsquo; decision making on control measures, and this requires effective surveillance.\u003c/p\u003e\u003cp\u003eThis study demonstrates that surveillance to estimate local prevalence, with an assumed between-herd prevalence of 5% and within-herd prevalence of 30%, performs well when test specificity is perfect and test sensitivity is 99%, and when between 39 and 61 herds per TCZ (depending on total herds in the TCZ) are tested. Apparent herd and animal level prevalence from test results were close to the simulated true values. When a higher between-herd prevalence of 30% is assumed, which would be consistent with more extensive transmission, sampling between 10 and 11 herds per TCZ provided an adequate estimate of prevalence.\u003c/p\u003e\u003cp\u003eOur 5% and 30% between-herd prevalence assumptions are low compared to some other European contexts, as potential transmission patterns of BTV-3 in Ireland are unknown, and we wished to provide sample size estimates appropriate for the earlier stages of BTV spread, with relatively lower between-herd prevalence. Between-herd prevalence of BTV-3 increased above 60% in the Netherlands (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) and for BTV-8 in parts of East Anglia in 2007 and 2008 (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe selected RT-PCR diagnostic characteristics for our main analysis, as EFSA suggests a detection window of 4\u0026ndash;5 months for detection of BTV nucleic acid in cattle after infection (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) and it is a highly sensitive and specific assay (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Assuming perfect specificity of the RT-PCR meant no issues with false-positives. Our calculation of PPV with a lower diagnostic specificity highlights potential issues with false positives in a low prevalence context and can be explored further using our web application. This can be used in the context of evolving prevalence and understanding of test characteristics. Diagnostic test results are rarely interpreted in isolation. For example, we must integrate consideration that an animal is likely to have BTV RNA detectable in their blood by RT-PCR in advance of generating antibodies against BTV. A positive serological result may reflect exposure to BTV or a related virus, or vaccination against BTV, at any time in the past (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Vaccination against BTV has never been implemented in Ireland. However, imported animals may have been vaccinated against or exposed to BTV. Consideration of the local epidemiological situation, including animals with clinical signs, is also necessary. Additionally, there may be increased confidence in a positive result within a cluster of positive results, compared to an isolated positive result in a cluster of negative results.\u003c/p\u003e\u003cp\u003eWe explored the effects of assuming either lower or higher between-herd prevalence in our scenarios. This is because, given that Ireland has never had a BT outbreak, we cannot predict the characteristics of transmission here. Transmission suitability for BTV varies according to weather and environmental conditions (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). M\u0026ouml;hlmann et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) related midge catch data from Italy, Sweden and the Netherlands to BTV risk and reproductive ratio. They reported that midge catches were higher in farm habitats than in wetland or peri-urban habitats, and increased with the previous week\u0026rsquo;s mean daily precipitation. Within wetlands, variation (in the previous 30 days) in precipitation was associated with higher midge catches (possibly associated with larval sites escaping flooding in these areas). In Italy, the Netherlands and Sweden, midge catches were highest at a temperature of 20\u0026ndash;21\u0026deg;C. Catches decreased with increasing mean wind velocity 24 hours before collection. Midge catches also varied more with season in the Netherlands and Sweden than in Italy (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). It is unknown how the ecology of the midge species in Ireland (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) may differ from this. Experiences with SBV over the past decade show that a midge borne virus infecting livestock can establish itself in Ireland (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). However, Gubbins et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) argued that the more extensive spread of SBV compared to BTV in England was due to a higher probability of SBV transmission from host to vector and differing temperature requirements for virus replication, highlighting that virus and vector ecology, including vector competence under different conditions, play an important role in transmission dynamics.\u003c/p\u003e\u003cp\u003eThese uncertainties about the potential transmission patterns of BTV in Ireland highlight the importance of effective surveillance after an incursion to detect additional cases. If, for example, we are confident that prevalence is low and BTV is not spreading extensively, testing and culling may be a feasible control mechanism. In contrast, mass vaccination, as is currently used in several countries to reduce BTV-3 transmission and impacts, may be more appropriate if BTV reaches a high prevalence. The evolution of European legislation on BTV controls reflects that BTV is making increasing incursions into, and, in some cases, becoming established in, many EU countries. There is recognition that aggressive \u0026ldquo;stamping out\u0026rdquo; based eradication policies may not be possible in all cases. The Animal Health Law currently defines BT as a Category C disease allowing countries to engage in optional control programmes, in contrast to its previous status as a \u0026ldquo;class A\u0026rdquo; disease requiring strict controls described by the old Council Directive 2000/75/EC, which has been superseded by the Animal Health Law.\u003c/p\u003e\u003cp\u003eIn whichever way the potential transmission patterns and policy context may evolve, case detection to better understand the extent of BTV spread after a known introduction will help inform the Irish response to an incursion. Our simulation study provides evidence to inform the Irish programme for detection of potential incursion and spread of BTV-3.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eFunding\u003c/h3\u003e\n\u003ch3\u003eThis research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.\u003c/h3\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003ch3\u003eData availability\u003c/h3\u003e\n\u003cp\u003eThe datasets analysed during this study are available from the Department of Agriculture, Food and the Marine (DAFM), and summarise for each TCZ and be viewed on our web application. The full data are subject to data protection regulations and limitations (https://www.gov.ie/en/organisation-information/ef9f6-data-protection/). All R code for the simulation and an example dataset are available here:\u003c/p\u003e\n\u003cp\u003ehttps://github.com/miriamcasey/BTV_surveillance\u003c/p\u003e\n\u003ch3\u003eAcknowledgements\u003c/h3\u003e\n\u003cp\u003eWe would like to thank Dr. David Beehan, Superintending Research Officer in the Virology Division of DAFM\u0026rsquo;s Central Veterinary Research laboratory for sharing information about the diagnostic assays used for BTV detection in Ireland and on laboratory processes. We also acknowledge the inputs of staff at DAFM\u0026rsquo;s National Disease Control Centre, in particular Veterinary Inspectors Dr. Bernadette Doyle and Dt. Rachael Moran, during the initiation stage of the project and for their comments on the manuscript. We are grateful to Daniel Collins and Dr. Nicola Harvey in the Centre for Veterinary Epidemiology and Risk Analysis for their helpful feedback on our web-application.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSantman-Berends I, van den Brink K, Dijkstra E, van Schaik G, Spierenburg MAH, van den Brom R. The impact of the bluetongue serotype 3 outbreak on sheep and goat mortality in the Netherlands in 2023. Prev Vet Med. 2024;231(May):106289.\u003c/li\u003e\n\u003cli\u003eVan den Brink KMJA, Santman-Berends IMGA, Harkema L, Scherpenzeel CGM, Dijkstra E, Bisschop PIH, et al. Bluetongue virus serotype 3 in the Netherlands; clinical signs, seroprevalences and pathological findings in ruminants. Veterinary Record. 2024;Accepted(July):1\u0026ndash;10.\u003c/li\u003e\n\u003cli\u003eHolwerda M, Santman-berends IMGA, Harders F, Engelsma M, Vloet RPM, Dijkstra E, et al. Emergence of Bluetongue Virus. 2024;30(8).\u003c/li\u003e\n\u003cli\u003eRoyal GD. 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Vector Ecology Unit School of Natural Sciences and Martin Ryan Institute, National University of Ireland, Galway. 2010.\u003c/li\u003e\n\u003cli\u003eCollins \u0026Aacute;B, Mee JF, Doherty ML, Barrett DJ, England ME. Culicoides species composition and abundance on Irish cattle farms: Implications for arboviral disease transmission. Parasit Vectors. 2018;11(1):1\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003eEl Moustaid F, Thornton Z, Slamani H, Ryan SJ, Johnson LR. Predicting temperature-dependent transmission suitability of bluetongue virus in livestock. Parasit Vectors. 2021;14(1):1\u0026ndash;14.\u003c/li\u003e\n\u003cli\u003eM\u0026ouml;hlmann TWR, Keeling MJ, Wennergren U, Favia G, Santman-Berends I, Takken W, et al. Biting midge dynamics and bluetongue transmission: a multiscale model linking catch data with climate and disease outbreaks. Sci Rep [Internet]. 2021;11(1):1\u0026ndash;16. 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Available from: https://developers.arcgis.com/documentation/app-builders/no-code/arcgis-dashboards/introduction-to-arcgis-dashboards/\u003c/li\u003e\n\u003cli\u003eIshaq M, Shah SAA, Khan N, Jamal SM. Prevalence and risk factors of bluetongue in small and large ruminants maintained on Government farms in North-western Pakistan. Res Vet Sci. 2023;161(March 2022):38\u0026ndash;44.\u003c/li\u003e\n\u003cli\u003eHwang JM, Kim JG, Yeh JY. Serological evidence of bluetongue virus infection and serotype distribution in dairy cattle in South Korea. BMC Vet Res. 2019;15(1):1\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eReynolds DR, Chapman JW, Harrington R. The Migration of Insect Vectors of Plant and Animal Viruses. Adv Virus Res. 2006;67(06):453\u0026ndash;517.\u003c/li\u003e\n\u003cli\u003eHendrickx G, Gilbert M, Staubach C, Elbers A, Mintiens K, Gerbier G, et al. A wind density model to quantify the airborne spread of Culicoides species during north-western Europe bluetongue epidemic, 2006. Prev Vet Med. 2008;87(1\u0026ndash;2):162\u0026ndash;81.\u003c/li\u003e\n\u003cli\u003eSedda L, Brown HE, Purse B V., Burgin L, Gloster J, Rogers DJ. A new algorithm quantifies the roles of wind and midge flight activity in the bluetongue epizootic in northwest Europe. Proceedings of the Royal Society B: Biological Sciences. 2012;279(1737):2354\u0026ndash;62.\u003c/li\u003e\n\u003cli\u003eGubbins S, Turner J, Baylis M, van der Stede Y, van Schaik G, Abrahantes JC, et al. Inferences about the transmission of Schmallenberg virus within and between farms. Prev Vet Med. 2014;116(4):380\u0026ndash;90.\u003c/li\u003e\n\u003cli\u003eStevenson M, Sergeant E, Heuer C, Marshall J, Sanchez J, Thornton R, et al. Package \u0026lsquo; epiR .\u0026rsquo; 2024.\u003c/li\u003e\n\u003cli\u003eR Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2024. Available from: http://www.r-project.org.\u003c/li\u003e\n\u003cli\u003eFlannery J, Rajko-Nenow P, Hicks H, Hill H, Gubbins S, Batten C. Evaluating the most appropriate pooling ratio for EDTA blood samples to detect Bluetongue virus using real-time RT-PCR. Vet Microbiol [Internet]. 2018;217(March):58\u0026ndash;63. Available from: https://doi.org/10.1016/j.vetmic.2018.03.001\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"irish-veterinary-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Irish Veterinary Journal](https://irishvetjournal.biomedcentral.com/)","snPcode":"13620","submissionUrl":"https://submission.springernature.com/new-submission/13620/3?","title":"Irish Veterinary Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Bluetongue, surveillance, prevalence, Ireland, preparedness","lastPublishedDoi":"10.21203/rs.3.rs-7106740/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7106740/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBluetongue virus serotype 3, emerged in northern Europe in 2023 and 2024. As of June 2025, Ireland is bluetongue free. However, to inform control decisions in the event of a possible incursion, a surveillance plan to detect cases and estimate prevalence is required. We created an active surveillance plan for 20km radius temporary control zones (TCZs) after initial case detection. Potential TCZs (n = 1062) covering Ireland were generated, and surveillance sample sizes were estimated based on cattle data in each TCZ. A two-stage (herd and animal level) design accounted for within-herd clustering. We simulated implementation of the surveillance plan in each TCZ to understand surveillance performance in the Irish cattle population. With simulated between-herd prevalence of 5%, within-herd prevalence of 30%, and using reverse transcriptase polymerase chain reaction with perfect specificity and 99% sensitivity, surveillance to estimate prevalence was adequate when between 39 and 61 herds per TCZ were sampled. With simulated between-herd prevalence of 30%, 10-11 herds per TCZ needed to be sampled to estimate prevalence. Within herds, sampling 10–11 cattle was sufficient for prevalence estimation. We integrated Shiny and ArcGIS web applications to allow users to simulate different scenarios under different settings. These include different test sensitivity and specificity, and different within- and between- herd prevalence contexts. This interface presents infected herds sampled, and true- and false- positives and negatives in a variety of conditions. Evidence from this scenario analysis can be integrated into a multi-pronged early warning and potential follow-up surveillance programme to facilitate decision making in the event of an incursion of BTV into Ireland. The URL for the web application associated with this paper is: https://www.arcgis.com/apps/dashboards/16722dde78d240f4a96303173bc6da2c\u003c/p\u003e","manuscriptTitle":"Preparation for a potential outbreak of bluetongue virus in Ireland: surveillance design to estimate local prevalence after an initial case detection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-22 16:21:51","doi":"10.21203/rs.3.rs-7106740/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-12T07:38:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-10T04:39:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"296313896646544954593610975784825957038","date":"2025-07-30T12:13:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-29T16:27:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"327105164306539312449212954293961967129","date":"2025-07-26T02:31:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"327510910485281693828194404572110484865","date":"2025-07-22T14:35:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-15T14:00:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-14T08:18:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-14T03:55:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Irish Veterinary Journal","date":"2025-07-12T08:13:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"irish-veterinary-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Irish Veterinary Journal](https://irishvetjournal.biomedcentral.com/)","snPcode":"13620","submissionUrl":"https://submission.springernature.com/new-submission/13620/3?","title":"Irish Veterinary Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2c53b4f1-3824-416d-b380-1a03d20a45f2","owner":[],"postedDate":"July 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-14T11:08:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-22 16:21:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7106740","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7106740","identity":"rs-7106740","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}