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

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

Effects of long-term exposure to outdoor air pollution on COVID-19 incidence: A population-based cohort study accounting for SARS-CoV-2 exposure levels in the Netherlands.

Several studies have linked air pollution to COVID-19 morbidity and severity. However, these studies do not account for exposure levels to SARS-CoV-2, nor for different sources of air pollution. We analyzed individual-level data for 8.3 million adults in the Netherlands to assess associations between long-term exposure to ambient air pollution and SARS-CoV-2 infection (i.e., positive test) and COVID-19 hospitalisation risks, accounting for spatiotemporal variation in SARS-CoV-2 exposure levels during the first two major epidemic waves (February 2020-February 2021). We estimated average annual concentrations of PM10, PM2.5 and NO2 at residential addresses, overall and by PM source (road traffic, industry, livestock, other agricultural sources, foreign sources, other Dutch sources), at 1 × 1 km resolution, and weekly SARS-CoV-2 exposure at municipal level. Using generalized additive models, we performed interval-censored survival analyses to assess associations between individuals' average exposure to PM10, PM2.5 and NO2 in the three years before the pandemic (2017-2019) and COVID-19-outcomes, adjusting for SARS-CoV-2 exposure, individual and area-specific confounders. In single-pollutant models, per interquartile (IQR) increase in exposure, PM10 was associated with 7% increased infection risk and 16% increased hospitalisation risk, PM2.5 with 8% increased infection risk and 18% increased hospitalisation risk, and NO2 with 3% increased infection risk and 11% increased hospitalisation risk. Bi-pollutant models suggested that effects were mainly driven by PM. Associations for PM were confirmed when stratifying by urbanization degree, epidemic wave and testing policy. All emission sources of PM, except industry, showed adverse effects on both outcomes. Livestock showed the most detrimental effects per unit exposure, whereas road traffic affected severity (hospitalisation) more than infection risk. This study shows that long-term exposure to air pollution increases both SARS-CoV-2 infection and COVID-19 hospitalisation risks, even after controlling for SARS-CoV-2 exposure levels, and that PM may have differential effects on these COVID-19 outcomes depending on the emission source.

Combining a parsimonious mathematical model with infection data from tailor-made experiments to understand environmental transmission.

Although most infections are transmitted through the environment, the processes underlying the environmental stage of transmission are still poorly understood for most systems. Improved understanding of the environmental transmission dynamics is important for effective non-pharmaceutical intervention strategies. To study the mechanisms underlying environmental transmission we formulated a parsimonious modelling framework including hypothesised mechanisms of pathogen dispersion and decay. To calibrate and validate the model, we conducted a series of experiments studying distance-dependent transmission of Campylobacter jejuni in broilers. We obtained informative simultaneous estimates for all three model parameters: the parameter of C. jejuni inactivation, the diffusion coefficient describing pathogen dispersion, and the transmission rate parameter. The time and distance dependence of transmission in the fitted model is quantitatively consistent with marked spatiotemporal patterns in the experimental observations. These results, for C. jejuni in broilers, show that the application of our modelling framework to suitable transmission data can provide mechanistic insight in environmental pathogen transmission.

Increased risk of pneumonia amongst residents living near goat farms in different livestock-dense regions in the Netherlands.

BACKGROUND: Previous studies, performed between 2009-2019, in the Netherlands observed an until now still unexplained increased risk for pneumonia among residents living close to goat farms. Since data were collected in the provinces Noord-Brabant and Limburg (NB-L), an area with relatively high air pollution levels and proximity to large industrial areas in Europe, the question remains whether the results are generalizable to other regions. In this study, a different region, covering the provinces Utrecht, Gelderland, and Overijssel (UGO) with a similar density of goat farms, was included to assess whether the association between goat farm proximity and pneumonia is consistently observed across the Netherlands. METHODS: Data for this study were derived from the Electronic Health Records (EHR) of 21 rural general practices (GPs) in UGO, for 2014-2017. Multi-level analyses were used to compare annual pneumonia prevalence between UGO and data derived from rural reference practices ('control area'). Random-effects meta-analysis (per GP practice) and kernel analyses were performed to study associations of pneumonia with the distance between goat farms and patients' home addresses. RESULTS: GP diagnoses of pneumonia occurred 40% more often in UGO compared to the control area. Meta-analysis showed an association at a distance of less than 500m (~70% more pneumonia compared to >500m) and 1000m (~20% more pneumonia compared to >1000m). The kernel-analysis for three of the four individual years showed an increased risk up to a distance of one or two kilometers (2-36% more pneumonia; 10-50 avoidable cases per 100,000 inhabitants per year). CONCLUSIONS: The positive association between living in the proximity of goat farms and pneumonia in UGO is similar to the previously found association in NB-L. Therefore, we concluded that the observed associations are relevant for regions with goat farms in the entire country.

Common features in spatial livestock disease transmission parameters.

The risk of epidemic spread of diseases in livestock poses a threat to animal and often also human health. Important for the assessment of the effect of control measures is a statistical model quantification of between-farm transmission during epidemics. In particular, quantification of the between-farm transmission kernel has proven its importance for a range of different diseases in livestock. In this paper we explore if a comparison of the different transmission kernels yields further insight. Our comparison identifies common features that connect across the different pathogen-host combinations analyzed. We conjecture that these features are universal and thereby provide generic insights. Comparison of the shape of the spatial transmission kernel suggests that, in absence of animal movement bans, the distance dependence of transmission has a universal shape analogous to Lévy-walk model descriptions of human movement patterns. Also, our analysis suggests that interventions such as movement bans and zoning, through their impact on these movement patterns, change the shape of the kernel in a universal fashion. We discuss how the generic insights suggested can be of practical use for assessing risks of spread and optimizing control measures, in particular when outbreak data is scarce.

Respiratory pathogens in veal calves: Inventory of circulating pathogens.

In the veal industry in The Netherlands, each year around 1.2 million "white" veal calves are produced on around 1100 farms. Bovine respiratory disease (BRD) causes serious health issues in these calves, also resulting in high usage of antimicrobials. To reduce antimicrobial usage, a more targeted treatment regime is needed, for which it is necessary to identify the causative agent. This study aimed at determining associations between pathogens and clinical disease, between prevalence of pathogens and BRD outbreaks, and BRD and performance. A cohort study was conducted involving ten veal farms, in which calf respiratory health was evaluated for the first 12 weeks. Whenever there was an outbreak of BRD, as determined by the farm veterinary surgeon, samples were taken from diseased and control calves through broncho-alveolar lavage. From these samples a broad spectrum of micro-organisms were isolated. Performance data were also collected. A total of 23 outbreaks happened during the 12 week study period, mostly in the first six weeks. BRD associated pathogens found were: BHV1, BPI3V, BRSV, BVDV, Pasteurella multocida, Mannheimia haemolytica, Trueperella pyogenes, Histophilus somni, Mycoplasma bovis, Mycoplasma bovirhinis and Mycoplasma dispar. For most BRD associated pathogens, there was no clear association between presence or prevalence of the micro-organisms and clinical issues. Only T. pyogenes (7.4% in healthy, 14.6% in diseased calves, p 0.013), M. bovis (37.6% and 63.2% respectively, p 0.001) and BVDV (9.9% and 16.9% respectively, p 0.03) were found more often in diseased animals. BPI3V was found in a few early outbreaks, which might suggest involvement in early outbreaks. It appears to be difficult to associate specific pathogens to outbreaks at the species level. BRD is the major reason for treatment with antimicrobials. More specific knowledge about the association between pathogens and health/disease could help to reduce antimicrobial use.

The African swine fever modelling challenge: Model comparison and lessons learnt.

Robust epidemiological knowledge and predictive modelling tools are needed to address challenging objectives, such as: understanding epidemic drivers; forecasting epidemics; and prioritising control measures. Often, multiple modelling approaches can be used during an epidemic to support effective decision making in a timely manner. Modelling challenges contribute to understanding the pros and cons of different approaches and to fostering technical dialogue between modellers. In this paper, we present the results of the first modelling challenge in animal health - the ASF Challenge - which focused on a synthetic epidemic of African swine fever (ASF) on an island. The modelling approaches proposed by five independent international teams were compared. We assessed their ability to predict temporal and spatial epidemic expansion at the interface between domestic pigs and wild boar, and to prioritise a limited number of alternative interventions. We also compared their qualitative and quantitative spatio-temporal predictions over the first two one-month projection phases of the challenge. Top-performing models in predicting the ASF epidemic differed according to the challenge phase, host species, and in predicting spatial or temporal dynamics. Ensemble models built using all team-predictions outperformed any individual model in at least one phase. The ASF Challenge demonstrated that accounting for the interface between livestock and wildlife is key to increasing our effectiveness in controlling emerging animal diseases, and contributed to improving the readiness of the scientific community to face future ASF epidemics. Finally, we discuss the lessons learnt from model comparison to guide decision making.

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

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

Mathematical Quantification of Transmission in Experiments: FMDV Transmission in Pigs Can Be Blocked by Vaccination and Separation.

Quantitative understanding of transmission with and without control measures is important for the control of infectious diseases because it helps to determine which of these measures (or combinations thereof) will be effective to reduce transmission. In this paper, the statistical methods used to estimate transmission parameters are explained. To show how these methods can be used we reviewed literature for papers describing foot-and-mouth disease virus (FMDV) transmission in pigs and we used the data to estimate transmission parameters. The analysis showed that FMDV transmits very well when pigs have direct contact. Transmission, however, is reduced when a physical barrier separates infected and susceptible non-vaccinated pigs. Vaccination of pigs can prevent infection when virus is administered by a single intradermal virus injection in the bulb of the heel, but it cannot prevent infection when pigs are directly exposed to either non-vaccinated or vaccinated FMDV infected pigs. Physical separation combined with vaccination is observed to block transmission. Vaccination and separation can make a significant difference in the estimated number of new infections per day. Experimental transmission studies show that the combined effect of vaccination and physical separation can significantly reduce transmission (R < 1), which is a very relevant result for the control of between-farm transmission.

Estimating the introduction time of highly pathogenic avian influenza into poultry flocks.

The estimation of farm-specific time windows for the introduction of highly-pathogenic avian influenza (HPAI) virus can be used to increase the efficiency of disease control measures such as contact tracing and may help to identify risk factors for virus introduction. The aims of this research are to (1) develop and test an accurate approach for estimating farm-specific virus introduction windows and (2) evaluate this approach by applying it to 11 outbreaks of HPAI (H5N8) on Dutch commercial poultry farms during the years 2014 and 2016. We used a stochastic simulation model with susceptible, infectious and recovered/removed disease stages to generate distributions for the period from virus introduction to detection. The model was parameterized using data from the literature, except for the within-flock transmission rate, which was estimated from disease-induced mortality data using two newly developed methods that describe HPAI outbreaks using either a deterministic model (A) or a stochastic approach (B). Model testing using simulated outbreaks showed that both method A and B performed well. Application to field data showed that method A could be successfully applied to 8 out of 11 HPAI H5N8 outbreaks and is the most generally applicable one, when data on disease-induced mortality is scarce.

Spatial transmission risk during the 2007-2010 Q fever epidemic in The Netherlands: Analysis of the farm-to-farm and farm-to-resident transmission.

Between 2007 and 2010 a Q fever epidemic in Dutch dairy goat farms caused a large Q fever outbreak in human residents in the southern part of the Netherlands. Here we characterize the transmission of Coxiella burnetii, the aetiological agent of Q fever, between infected and susceptible dairy goat farms by estimating a spatial transmission kernel. In addition, we characterize the zoonotic transmission of C. burnetii by estimating the spatial kernel for transmission from infected farms to neighbouring residents. Whereas the range of between-farm transmission is comparable to the scale of the Netherlands, likely due to long-range between-farm contacts such as animal transport, the transmission risk from farms to humans is more localized, although still extending to 10 km and beyond. Within a range of about 10 km, the transmission risk from an infected goat farm to a single resident is of the same order of magnitude as the farm-to-farm transmission risk per animal in a receiving farm. We illustrate how, based on the estimated kernels, spatial patterns of transmission risks between farms and from farms to residents can be calculated and visualized by means of risk maps, offering further insight relevant to policy making in a one-health context.

Risk of pneumonia among residents living near goat and poultry farms during 2014-2016.

In the Netherlands, an association was found between the prevalence of pneumonia and living near goat and poultry farms in 2007-2013. This association then led to regulatory decisions to restrict the building of new goat farms and to reduce emissions of poultry farms. Confirmation of these results, however, is required because the period of previous analyses overlapped a Q-fever epidemic in 2007-2010. To confirm the association, we performed a population-based study during 2014-2016 based on general practitioner (GP) data. Electronic medical records of 90,183 persons were used to analyze the association between pneumonia and the population living in the proximity (within 500-2000 m distance) of goat and poultry farms. Data were analyzed with three types of logistic regression (with and without GP practice as a random intercept and with stratified analyses per GP practice) and a kernel model to discern the influence of different statistical methods on the outcomes. In all regression analyses involving adults, a statistically significant association between pneumonia and residence within 500 meters of goat farms was found (odds ratio [OR] range over all analyses types: 1.33-1.60), with a decreasing OR for increasing distances. In kernel analyses (including all ages), a population-attributable risk between 6.0 and 7.8% was found for a distance of 2000 meters in 2014-2016. The associations were consistent across all years and robust for mutual adjustment for proximity to other animals and for several other sensitivity analyses. However, associations with proximity to poultry farms are not supported by the present study. As the causes of the elevated pneumonia incidence in persons living close to goat farms remain unknown, further research into potential mechanisms is required for adequate prevention.

Virus Shedding of Avian Influenza in Poultry: A Systematic Review and Meta-Analysis.

Understanding virus shedding patterns of avian influenza virus (AIV) in poultry is important for understanding host-pathogen interactions and developing effective control strategies. Many AIV strains were studied in challenge experiments in poultry, but no study has combined data from those studies to identify general AIV shedding patterns. These systematic review and meta-analysis were performed to summarize qualitative and quantitative information on virus shedding levels and duration for different AIV strains in experimentally infected poultry species. Methods were designed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Four electronic databases were used to collect literature. A total of 1155 abstract were screened, with 117 studies selected for the qualitative analysis and 71 studies for the meta-analysis. A large heterogeneity in experimental methods was observed and the quantitative analysis showed that experimental variables such as species, virus origin, age, inoculation route and dose, affect virus shedding (mean, peak and duration) for highly pathogenic AIV (HPAIV), low pathogenic AIV (LPAIV) or both. In conclusion, this study highlights the need to standardize experimental procedures, it provides a comprehensive summary of the shedding patterns of AIV strains by infected poultry and identifies the variables that influence the level and duration of AIV shedding.

Endotoxin and particulate matter emitted by livestock farms and respiratory health effects in neighboring residents.

BACKGROUND: Living in livestock-dense areas has been associated with health effects, suggesting airborne exposures to livestock farm emissions to be relevant for public health. Livestock farm emissions involve complex mixtures of various gases and particles. Endotoxin, a pro-inflammatory agent of microbial origin, is a constituent of livestock farm emitted particulate matter (PM) that is potentially related to the observed health effects. Quantification of livestock associated endotoxin exposure at residential addresses in relation to health outcomes has not been performed earlier. OBJECTIVES: We aimed to assess exposure-response relations for a range of respiratory endpoints and atopic sensitization in relation to livestock farm associated PM10 and endotoxin levels. METHODS: Self-reported respiratory symptoms of 12,117 persons participating in a population-based cross-sectional study were analyzed. For 2494 persons, data on lung function (spirometry) and serologically assessed atopic sensitization was additionally available. Annual-average PM10 and endotoxin concentrations at home addresses were predicted by dispersion modelling and land-use regression (LUR) modelling. Exposure-response relations were analyzed with generalized additive models. RESULTS: Health outcomes were generally more strongly associated with exposure to livestock farm emitted endotoxin compared to PM10. An inverse association was observed for dispersion modelled exposure with atopic sensitization (endotoxin: p = .004, PM10: p = .07) and asthma (endotoxin: p = .029, PM10: p = .022). Prevalence of respiratory symptoms decreased with increasing endotoxin concentration at the lower range, while at the higher range prevalence increased with increasing concentration (p < .05). Associations between lung function parameters with exposure to PM10 and endotoxin were not statistically significant (p > .05). CONCLUSIONS: Exposure to livestock farm emitted particulate matter is associated with respiratory health effects and atopic sensitization in non-farming residents. Results indicate endotoxin to be a potentially plausible etiologic agent, suggesting non-infectious aspects of microbial emissions from livestock farms to be important with respect to public health.

Associations between pneumonia and residential distance to livestock farms over a five-year period in a large population-based study.

In a recent study of electronic health records (EHR) of general practitioners in a livestock-dense area in The Netherlands in 2009, associations were found between residential distance to poultry farms and the occurrence of community-acquired pneumonia (CAP). In addition, in a recent cross-sectional study in 2494 adults in 2014/2015 an association between CAP and proximity to goat farms was observed. Here, we extended the 2009 EHR analyses across a wider period of time (2009-2013), a wider set of health effects, and a wider set of farm types as potential risk sources. A spatial (transmission) kernel model was used to investigate associations between proximity to farms and CAP diagnosis for the period from 2009 to 2013, obtained from EHR of in total 140,059 GP patients. Also, associations between proximity to farms and upper respiratory infections, inflammatory bowel disease, and (as a control disease) lower back pain were analysed. Farm types included as potential risk sources in these analyses were cattle, (dairy) goats, mink, poultry, sheep, and swine. The previously found association between CAP occurrence and proximity to poultry farms was confirmed across the full 5-year study period. In addition, we found an association between increased risk for pneumonia and proximity to (dairy) goat farms, again consistently across all years from 2009 to 2013. No consistent associations were found for any of the other farm types (cattle, mink, sheep and swine), nor for the other health effects considered. On average, the proximity to poultry farms corresponds to approximately 119 extra patients with CAP each year per 100,000 people in the research area, which accounts for approximately 7.2% extra cases. The population attributable risk percentage of CAP cases in the research area attributable to proximity to goat farms is approximately 5.4% over the years 2009-2013. The most probable explanation for the association of CAP with proximity to poultry farms is thought to be that particulate matter and its components are making people more susceptible to respiratory infections. The causes of the association with proximity to goat farms is still unclear. Although the 2007-2010 Q-fever epidemic in the area probably contributed Q-fever related pneumonia cases to the observed additional cases in 2009 and 2010, it cannot explain the association found in later years 2011-2013.

Modelling of strategies for genetic control of scrapie in sheep: The importance of population structure.

Scrapie is a transmissible spongiform encephalopathy in sheep and an example of a disease that may be controlled through breeding for disease resistance. Member states of the European Union have introduced strategies for breeding against scrapie based on the selection of genetically resistant breeding rams. An ambitious strategy adopted in The Netherlands consisted of selecting resistant rams for breeding throughout both breeding and production sectors. Mathematical modelling of the effect of a breeding program on the spreading capacity of scrapie in a national flock is needed for making assessments on how long a breeding strategy needs to be maintained to achieve disease control. Here we describe such a model applied to the Dutch situation, with the use of data on the genetic content of the Dutch sheep population as well as on scrapie occurrence in this population. We show that the time needed for obtaining scrapie control depends crucially on two parameters measuring sheep population structure: the between-flock heterogeneity in genotype frequencies, and the heterogeneity of mixing (contact rates) between sheep flocks. Estimating the first parameter from Dutch genetic survey data and assuming scenario values for the second one, enables model prediction of the time needed to achieve scrapie control in The Netherlands.

Increased risk of pneumonia in residents living near poultry farms: does the upper respiratory tract microbiota play a role?

BACKGROUND: Air pollution has been shown to increase the susceptibility to community-acquired pneumonia (CAP). Previously, we observed an increased incidence of CAP in adults living within 1 km from poultry farms, potentially related to particulate matter and endotoxin emissions. We aim to confirm the increased risk of CAP near poultry farms by refined spatial analyses, and we hypothesize that the oropharyngeal microbiota composition in CAP patients may be associated with residential proximity to poultry farms. METHODS: A spatial kernel model was used to analyze the association between proximity to poultry farms and CAP diagnosis, obtained from electronic medical records of 92,548 GP patients. The oropharyngeal microbiota composition was determined in 126 hospitalized CAP patients using 16S-rRNA-based sequencing, and analyzed in relation to residential proximity to poultry farms. RESULTS: Kernel analysis confirmed a significantly increased risk of CAP when living near poultry farms, suggesting an excess risk up to 1.15 km, followed by a sharp decline. Overall, the oropharyngeal microbiota composition differed borderline significantly between patients living <1 km and ≥1 km from poultry farms (PERMANOVA p = 0.075). Results suggested a higher abundance of Streptococcus pneumoniae (mean relative abundance 34.9% vs. 22.5%, p = 0.058) in patients living near poultry farms, which was verified by unsupervised clustering analysis, showing overrepresentation of a S. pneumoniae cluster near poultry farms (p = 0.049). CONCLUSION: Living near poultry farms is associated with an 11% increased risk of CAP, possibly resulting from changes in the upper respiratory tract microbiota composition in susceptible individuals. The abundance of S. pneumoniae near farms needs to be replicated in larger, independent studies.

Investigation of a Simple Model for Within-Flock Transmission of Scrapie.

Genetic control programs for scrapie in sheep build on solid knowledge of how susceptibility to scrapie is modulated by the prion protein genotype at the level of an individual sheep. In order to satisfactorily analyze the effectivity of control programs at the population level, insight is needed at the flock level, i.e., how the grouping of sheep in flocks affects the population-level transmission risk. In particular, one would like to understand how this risk is affected by between-flock differences in genotype frequency distribution. A first step is to model the scrapie transmission risk within a flock as a function of the flock genotype profile. Here we do so by estimating parameters for a model of within-flock transmission using genotyping data on Dutch flocks affected by scrapie. We show that the data are consistent with a relatively simple transmission model assuming horizontal transmission and homogeneous mixing between animals. The model expresses the basic reproduction number for within-flock scrapie as a weighted average of genotype-specific susceptibilities, multiplied by a single overall transmission parameter. The value of the overall transmission parameter may vary between flocks to account for random between-flock variation in non-genetic determinants such as management practice. Here we provide an estimate of its mean value and variation for Dutch flocks.

Confirmation of spatial patterns and temperature effects in Bluetongue virus serotype-8 transmission in NW-Europe from the 2007 reported case data.

Two separate analyses were carried out to understand the epidemiology of Bluetongue virus serotype 8 (BTV-8) in 2007 in North West Europe: First, the temporal change in transmission rates was compared to the evolution of temperature during that season. Second, we evaluated the spatio-temporal dynamics of newly reported outbreaks, to estimate a spatial transmission kernel. For both analyses, the approach as used before in analysing the 2006 BTV-8 epidemic had to be adapted in order to take into account the fact that the 2007 epidemic was not a newly arising epidemic, but one advancing from whereto it had already spread in 2006. We found that within the area already affected by the 2006 outbreak, the pattern of newly infected farms in 2007 cannot be explained by between-farm transmission, but rather by local re-emergence of the virus throughout that region. This indicates that persistence through winter was ubiquitous for BTV-8. Just like in 2006, we also found that the temperature at which the infection starts to spread lies close to 15 °C. Finally, we found that the shape of the transmission kernel is in line with the one from the 2006 epidemic. In conclusion, despite the substantial differences between 2006 and 2007 in temperature patterns (2006 featured a heat wave in July, whereas 2007 was more regular) and spatial epidemic extent, both the minimum temperature required for transmission and the transmission kernel were similar to those estimated for the 2006 outbreak, indicating that they are robust properties, suitable for extrapolation to other years and similar regions.