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African swine fever (ASF) continues to spread worldwide, and has reached multiple countries across Asia, the Caribbean, Europe, and the Pacific, representing a serious economic burden threatening pig health and welfare, as well as food security. The disease affects domestic pigs and wild boar, and in several European countries the disease is endemic in wild boars. The lack of vaccines or effective treatments highlights the importance of effective control measures used to keep domestic and feral pigs separated to prevent the spread of the virus. However, the study of the livestock–wildlife interface is quite complex and has many aspects to consider, including the uncertainty of wild-boar population data. In this study, we determined the risk of spread of the ASF virus at the interface between domestic pigs and wild boars using indicators that can indirectly indicate the presence of wild boars in order to target specific control measures in the highest risk areas. The results of the study were compared with those obtained by Pittiglio, in which the population data for wild boars was estimated using a geostatistical method and similar results were obtained. However, the present study used specific information relating to the wild-boar population and this allowed us to use fewer variables.

A semi-quantitative risk assessment was developed to classify pig farms in terms of the probability of introduction of African swine fever virus (ASFV). Following on-farm data collection via a specific checklist, we applied a modified failure mode and effect analysis (FMEA) to calculate the risk priority codes (RPC's), indicating increasing risk levels ranging from 1 to 5. The importance of biosecurity measures was attributed by experts. To consider geographic risk factors, we classified pig farms based on local density of farmed pigs, and on the estimated wild boar population density. The combination of RPC's with geographical risk factors resulted into a final ranking of pig farms in terms of the risk of ASFV introduction. Furthermore, the estimation of frequency and levels of non-compliance with biosecurity measures was used to identify weak points in risk prevention at farm level. The outcome of the risk assessment was affected by choices in assigning non-compliance scores and importance to specific components of biosecurity. The method was applied in 60 commercial farms in major pig production areas in Italy. Furthermore, we applied a reduced version of our checklist in 12 non-commercial/small commercial (≤20 pigs) farms in the northern Apennines. In commercial farms, highest RPC's were obtained for biosecurity measures associated with personnel practices and farm buildings/planimetry. Intervention should be addressed to training of personnel on biosecurity and ASF, to avoid contacts with other pig herds, and to improve practices in the entrance into the farm. Sharing trucks with other farms, and loading/unloading of pigs were other weak points. Fencing was classified as insufficient in 70% of the commercial farms. Among these farms, breeding units were characterised by the lowest risk of ASFV introduction (although differences among median ranks were not statistically significant: P-value = 0.07; Kruskal–Wallis test), and increasing herd size was not significantly correlated with a higher risk (Kendall's τ = −0.13; P-value = 0.14). Density of farmed pig was greatest in the main pig production area in northern Italy. Conversely, exposure to wild boars was greatest for non-commercial/small commercial farms on the Apennines, which were also characterised by non-compliance with critical biosecurity measures.

In the last two years, there have been three major epidemic seasons in the territory of the European Union and the HPAI epizootic in 2021–2022 is the most severe in recent history. In Italy, the disease was introduced to dense poultry areas with serious economic consequences for the entire sector. In Lombardy, the analysis of the risk factors was carried out, also taking into account the density of domestic birds. In the most affected areas, 66.7% of the outbreaks occurred in the areas with the highest poultry density and the likelihood of an outbreak occurring increased with an increase in the density of birds per km2. In cells 10 × 10 km with a density greater than 10,000 birds/km2, the probability of outbreak occurrence was over 66.7%. The provinces involved in the last epidemic were the same involved in previous epidemics and, given the risk factors present in the area, it is plausible that the risk remains high also for future epidemic seasons. Therefore, to avoid the repetition of similar events, certain control measures shall be strengthened and vaccination considered as a complementary tool for the control of HPAI virus in risk areas.

Torque teno sus virus (TTSuV) is a non-enveloped circular ssDNA virus which frequently infects swine and has been associated with hepatic, respiratory, and autoimmune disorders. TTSuV’s pathogenic role is still uncertain, and clear data in the literature on virus reservoirs are lacking. The aims of this study were to investigate the presence of potentially zoonotic TTSuV in wild animals in Northern Italy and to evaluate their role as reservoirs. Liver samples were collected between 2016 and 2020 during four hunting seasons from wild boars (Sus scrofa), red deer (Cervus elaphus), roe deer (Capreolus capreolus), and chamois (Rupicapra rupicapra). Samples originated from areas in Northern Italy characterized by different traits, i.e., mountains and flatland with, respectively low and high farm density and anthropization. Viral identification was carried out by end-point PCR with specific primers for TTSuV1a and TTSuVk2a species. TTSuV prevalence in wild boars was higher in the mountains than in the flatland (prevalence of 6.2% and 2.3%, respectively). In wild ruminants only TTSuVk2a was detected (with a prevalence of 9.4%). Our findings shed light on the occurrence and distribution of TTSuV in some wild animal species, investigating their possible role as reservoirs.

The complex health problem of antimicrobial resistance (AMR) involves many host species, numerous bacteria and several routes of transmission. Extended-spectrum β-lactamase and AmpC (ESBL/AmpC)-producing Escherichia coli are among the most important strains. Moreover, wildlife hosts are of interest as they are likely antibiotics free and are assumed as environmental indicators of AMR contamination. Particularly, wild boar (Sus scrofa) deserves attention because of its increased population densities, with consequent health risks at the wildlife–domestic–human interface, and the limited data available on AMR. Here, 1504 wild boar fecal samples were microbiologically and molecularly analyzed to investigate ESBL/AmpC-producing E. coli and, through generalized linear models, the effects of host-related factors and of human population density on their spread. A prevalence of 15.96% of ESBL/AmpC-producing E. coli, supported by blaCTX-M (12.3%), blaTEM (6.98%), blaCMY (0.86%) and blaSHV (0.47%) gene detection, emerged. Young animals were more colonized by ESBL/AmpC strains than older subjects, as observed in domestic animals. Increased human population density leads to increased blaTEM prevalence in wild boar, suggesting that spatial overlap may favor this transmission. Our results show a high level of AMR contamination in the study area that should be further investigated. However, a role of wild boar as a maintenance host of AMR strains emerged.

In 2019, the area of the European Union (EU) affected by African swine fever (ASF) expanded progressively in a southwestern direction from Baltic and eastern countries. The disease can severely affect and disrupt regional and international trade of pigs and pork products with serious socioeconomic damages to the pig industry. Lombardy is one of the most important European pig producers and the introduction of ASF into the pig population could adversely affect the entire sector. A study was carried out to identify the farms and territories in the region most at risk of ASF introduction to plan preventive measures. The pig holdings were identified through a descriptive analysis of pig movements and Social Network Analysis (SNA), while, for the identification of the most exposed municipalities, an assessment of risk factors was carried out using the ranking of summed scores attributed to the Z-score. From the analysis, it was found that 109 municipalities and 297 pig holdings of the region were potentially more at risk, and these holdings were selected for target surveillance. This information was provided to veterinary authority to target surveillance in pig farms, in order to early detect a possible incursion of ASF and prevent its spread.