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The description of the pattern of livestock movements between herds provides essential information for both improving risk-based surveillance and to understand the likely spread of infectious diseases. This study provides a description of the temporal pattern of pig movements recorded in Italy on a 4-year period (2013–2016). Data, provided by the National Livestock registry, were described by social network analysis and the application of a walk-trap algorithm for community detection. Our results show a highly populated community located in Northern Italy, which is the focal point of the Italian industrial pig production and as a general pattern an overall decline of medium and backyard farms and an increase in the number of large farms, in agreement with the trend observed by other EU pig-producing countries. A seasonal pattern of all the parameters evaluated, including the number of active nodes in both the intensive and smaller production systems, emerged: that is characterized by a higher number of movements in spring and autumn, linked with the breeding and production cycle as pigs moved from the growing to the finishing phase and with periods of increased slaughtering at Christmas and Easter. The same pattern was found when restricting the analysis to imported pig batches. Outbreaks occurring during these periods would have a greater impact on the spread of infectious diseases; therefore, targeted surveillance may be appropriate. Finally, potential super-spreader nodes have been identified and represent 0.47% of the total number of pig holdings (n = 477). Those nodes are present during the whole study period with a similar ranking in their potential of being super-spreaders. Most of them were in Northern Italy, but super-spreaders with high mean out-degree centrality were also located in other Regions. Seasonality, communities and super-spreaders should be considered when planning surveillance activity and when applying disease control strategies.

Timely and reliable data and information availability and sharing is essential for early warning, prevention and control of transboundary diseases. While there are a growing number of global datasets capable of providing information for use in early warning systems and risk assessment (RA) tools, there are currently time-consuming data cleansing and harmonisation activities which need to be carried out before they can be reliably used and combined. Thus, using global datasets as they stand can lead to errors in RA parameterisation and results due to inherent biases in the data, e.g. missing disease prevalence data treated as a zero may inadvertently penalise those countries which do report disease outbreaks as opposed to those countries which are affected by a pathogen but do not report outbreak data. It is therefore of great importance that data are clearly provided and easy to understand and that data providers strive for greater harmonisation of database standards. In this paper the datasets utilised in the SPARE ('Spatial risk assessment framework for assessing exotic disease incursion and spread through Europe') project are described and discussed in terms of key criteria: accessibility, availability, completeness, consistency and quality. It is evident that most databases exist as information portals and not exclusively for RA purposes. Another striking issue from this assessment is the need for enhanced data sharing specifically with regards to data on illegal seizures, arthropod vector/wildlife abundance, intra-country livestock movement and national animal disease surveillance. It is hoped that the outcomes of this work will promote discussion and exchange between data providers, including the development of standardised data exchange protocols. The transformation of datasets to a common format is a considerable challenge but recommendations could and should be made on the standardisation of datasets and reporting in order to achieve a unified approach across Europe.

Background Caprine herpesvirus 1 (CpHV-1) causes neonatal mortality and reproductive failure in goats. Despite its impact on herd reproductive performance, few studies have investigated the risk factors associated with CpHV-1 infection. The aim of this cross-sectional study was to identify potential herd- and host-level risk factors associated with CpHV-1 prevalence in a goat population with heterogeneous seropositivity for CpHV-1. Results Blood samples and individual data from 4542 goats were collected from 255 herds in Piedmont, Italy. Enzyme-linked immunosorbent assay (ELISA) and serum neutralization tests were carried out to detect antibodies against CpHV-1. A mixed-effects model was applied to identify any statistical association between CpHV-1 seropositivity and a set of putative host-level and herd-level risk factors. A total of 630 samples tested were found positive by ELISA (prevalence = 13.9%; 95% confidence interval (CI) 12.9–14.9). Of the 255 tested herds, 85 were classified as positive for the presence of at least one gB-positive animal (herd prevalence 33.3%, 95% CI 27.5–39.2), with a within-herd prevalence between 0.7 and 100% (Q1 = 17.6%; median = 32.3%; Q3 = 50%) (Q = quartiles). The prevalence ratios showed a statistical association with the following risk factors: breeds other than Saanen, older age, larger herd size, meat and extensive herds, and co-existence of CAEV-infected animals. Conclusions Results from this cross sectional study may help to elucidate the natural history of the infection and inform targeted strategies to control a disease with a potentially important impact on animal health and goat farming economy.

Exotic animal diseases are transboundary hazards, characterized by their capability to cover global distances, affecting animal health and welfare with significant economic losses. Their prevention is complex and requires the dynamic management of potential entry points, transmission pathways, and preventative barriers. The well-timed detection of an undefined or unexpected (exotic or re-emerging) threat could minimize the consequences due to onward transmission. As a fit for purpose framework, OIE developed the import risk assessment i.e. a risk assessment model focusing on the entrance of an exotic disease into a geographical area with naive hosts. In this paper, we propose an improvement of the model by integrating it with Social Network Analysis (SNA) accounting for within-country animal movements. Our integrated model has been used as a combined tool to better estimate the spatial probability of the introduction of at least one affected animal in Italian provinces using Bluetongue (BT) as an example. Starting from international country-specific BT prevalence data, the model estimated the probability of introduction to Italy via two different routes of release i.e. the import of infected animals or the release of infected vectors either associated with imported livestock or through windborne dispersion from Africa. The conventional OIE model estimating the probability of BT entering Italy assuming the same release probability for every Italian province was paralleled by a model integrated with outputs from SNA to account for different release probability among provinces based on animal movements. The conventional model predicted a remarkable homogeneity in the risk among the provinces with some peaks only visible during the warmest months. The model incorporating the network analysis predicted the highest risk to be in the North Eastern region of Italy but also highlighted the likely occurrence in a couple of Southern provinces, an output mirroring past occurrence of BT in Italy. Moreover, the sensitivity analysis highlighted the main role for a couple of model parameters i.e. the probability for a vector to become infected and the vaccine coverage, thus suggesting that an extra effort in vaccine campaigns could be envisaged. The ability to measure animal movements by SNA can allow the identification of geographical risk hot spots and therefore the risk-based targeting of the surveillance system.

Modelling the range of prominent vectors of pathogens is a common technique used for planning surveys, building exposure assessment estimations or addressing the impact of future climate scenarios. In most cases the recorded distribution of a target species is used together several environmental variables to capture its potential distribution, ignoring the availability of adequate hosts for the arthropod. In this study we used satellite-recorded, Fourier transformed, environmental variables of ground temperature and vegetation (NDVI) and a large dataset of records of the tick Ixodes ricinus and their hosts. The aim is to evaluate the influence of the contact rates of the ticks with the different hosts and how they impact on the raw, environmental-based distribution of the tick. These effects may alter the estimations of the distribution of the tick under future climate scenarios. This study demonstrated that the relative contribution of the hosts supporting the feeding of the tick life stages dramatically altered the expected distribution of the tick. In the study case, results support that both Passeriformes (Aves) and Rodentia-Soricomorpha contribute in different ways to the colonization of the tick along a large latitudinal gradient. However, Artiodactyla are the main supporters of the tick and enhance its presence in the fringe of its distribution area. Thus, complete data on the distribution of each host contributing to the tick's life cycle and their relative importance for the tick, should be included in the assessment of exposure by arthropods. Other results of the study suggested that more information could be obtained exploring the association of the arthropod to the variables in the environmental niche, instead of the classic approach based on a crude evaluation on the geographical space (maps).

We present a novel approach of using the multi-criteria pathogen prioritisation methodology as a basis for selecting the most appropriate case studies for a generic risk assessment framework. The approach uses selective criteria to rank exotic animal health pathogens according to the likelihood of introduction and the impact of an outbreak if it occurred in the European Union (EU). Pathogens were evaluated based on their impact on production at the EU level and international trade. A subsequent analysis included criteria of relevance to quantitative risk assessment case study selection, such as the availability of data for parameterisation, the need for further research and the desire for the case studies to cover different routes of transmission. The framework demonstrated is flexible with the ability to adjust both the criteria and their weightings to the user’s requirements. A web based tool has been developed using the RStudio shiny apps software, to facilitate this.

Disease incursion and transmission modelling can play an important role in elucidating important pathways and dynamics of transboundary diseases. It is an important pre-requisite for preparedness and rapid response. A model framework has been developed which makes use of global datasets to predict the probability of entry of exotic animal pathogens to European Union (EU) member states (MSs) via some of the most likely routes of introduction: legal trade of livestock and meat products, illegal trade of red meat, wild animal dispersion, windborne vector dispersion and human introduction of pets. The model was designed to be applicable for a wide range of pathogens, many of which have limited data. We demonstrate its application through four case study pathogens: African swine fever, Classical swine fever, Bluetongue and classical rabies. The model results highlight the differences in probability between EU MSs; the absolute values for entry via a given route differed across MSs whilst different pathogens were predicted as having the highest probability of entry for the same route across MSs. Scenario analyses suggested that the probability of entry was heavily influenced by the pathogen prevalence in the country of origin and the extent to which EU MSs pose a risk to each other; the greatest risk was predominantly from countries within the EU. While we believe the input data are obtained from high quality sources, there are still big issues with regards uncertainty in some areas, in particular with regards to prevalence of pathogens in vector populations and consistency of reporting of pathogen prevalence in animals across all countries of the world. Thus, it is inevitable that there is a high degree of uncertainty associated with the absolute values. However, the main strength of the model is the broad range of analyses over pathogens, EU MSs and routes of entry. The model is also relatively easy to update with new data and a web based visualisation tool has been developed which allows users to interrogate the results of the model. As such, we believe that the model proposed here can be a useful quantitative complement to current qualitative early warning systems, helping to drive risk-based surveillance activities, by providing detailed quantitative comparisons to indicate which pathogens are most likely to enter the EU, by which route and into which areas within Europe.

Background Canine cancer registry data can be put to good use in epidemiological studies. Quantitative comparison of tumour types may reveal unusual cancer frequencies, providing directions for research and generation of hypotheses of cancer causation in a specific area, and suggest leads for identifying risk factors. Here we report canine cancer incidence rates calculated from a population-based registry in an area without any known specific environmental hazard. Results In its 90 months of operation from 2001 to 2008 (the observation period in this study), the population-based Piedmont Canine Cancer Registry collected data on 1175 tumours confirmed by histopathological diagnosis. The incidence rate was 804 per 100,000 dog-years for malignant tumours and 897 per 100,000 dog-years for benign tumours. Higher rates for all cancers were observed in purebred dogs, particularly in Yorkshire terrier and Boxer. The most prevalent malignant neoplasms were cutaneous mastocytoma and hemangiopericytoma, and mammary gland complex carcinoma and simplex carcinoma. Conclusions The Piedmont canine cancer registry is one of few of its kind whose operations have been consistently supported by long-term public funding. The registry-based cancer incidence rates were estimated with particular attention to the validity of data collection, thus minimizing the potential for bias. The findings on cancer incidence rates may provide a reliable reference for comparison studies. Researches conducted on dogs, used as sentinels for community exposure to environmental carcinogens, can be useful to detect excess risks in the incidence of malignant tumours in the human population.

West Nile virus and Usutu virus have established in different parts of Italy over the past 10 years. Piemonte and Liguria Regions (Northwestern Italy) are known to be nonendemic areas, despite the presence of competent vectors and environmental conditions conducive to maintaining infection. This work evidences for the first time, through an entomological surveillance implemented on the basis of risk factor approach, the presence of West Nile and Usutu viruses in Piemonte and Liguria Regions (Northwestern Italy).