West Nile Disease (WND) is one of the most spread zoonosis in Italy and Europe caused by a vector-borne virus. Its transmission cycle is well understood, with birds acting as the primary hosts and mosquito vectors transmitting the virus to other birds, while humans and horses are occasional dead-end hosts. Identifying suitable environmental conditions across large areas containing multiple species of potential hosts and vectors can be difficult. The recent and massive availability of Earth Observation data and the continuous development of innovative Machine Learning methods can contribute to automatically identify patterns in big datasets and to make highly accurate identification of areas at risk. In this paper, we investigated the West Nile Virus (WNV) circulation in relation to Land Surface Temperature, Normalized Difference Vegetation Index and Surface Soil Moisture collected during the 160 days before the infection took place, with the aim of evaluating the predictive capacity of lagged remotely sensed variables in the identification of areas at risk for WNV circulation. WNV detection in mosquitoes, birds and horses in 2017, 2018 and 2019, has been collected from the National Information System for Animal Disease Notification. An Extreme Gradient Boosting model was trained with data from 2017 and 2018 and tested for the 2019 epidemic, predicting the spatio-temporal WNV circulation two weeks in advance with an overall accuracy of 0.84. This work lays the basis for a future early warning system that could alert public authorities when climatic and environmental conditions become favourable to the onset and spread of WNV.

Since February 2020, Italy has been seriously affected by the SARS-CoV-2 pandemic. To support the National Health Care system, naso-pharyngeal/oropharyngeal swabs collected from suspected cases of Teramo province, Abruzzo region, are tested at Istituto Zooprofilattico Sperimentale dell’Abruzzo e del Molise G. Caporale, for the presence of SARS-CoV-2 RNA. Out of 12,446 tested individuals, 605 returned positive results at least once, with prevalence significantly higher in men. A reduction of the level of viral RNA in the first swab per each positive patient collected over time was also observed. Moreover, 81 patients had at least one positive sample and two final negative tests: positivity in swabs lasted from 14 to 63 days, with a median value of 30 days. This shows the potential for the virus to coexist with patients for a long time, although we highlighted intermittent positivity in several cases. The evolution of the SARS-CoV-2 epidemiological situation and knowledge on viral shedding should be closely monitored, to interpret the findings correctly and adjust accordingly the surveillance activities.

From 16 March to 15 December 2020, 132,357 naso-pharyngeal/oropharyngeal swabs were collected in the province of Teramo, Abruzzo Region, Italy, and tested for the presence of SARS-CoV-2 genomic RNA by a commercially available molecular assay. A total of 12,880 swabs resulted positive. For 8212 positive patients (4.150 women and 4.062 men) the median age was statistically different between women (median: 49.55 ± 23.9 of SD) and men (median: 48.35 ± 23.5 of SD) while no differences were found in the comparison between the cycle threshold for the N protein-encoding gene (C_T N) median values and gender. Differences were observed in the C_T N gene median values of swabs collected from March to September as well as in the pairwise comparison between September and October and between November and December. The C_T N gene median values observed in specific periods characterizing the SARS-CoV-2 epidemic in 2020 were also compared with the incidence of COVID-19 cases; a strong inverse correlation was highlighted (Pearson correlation coefficient = −0.978). Our findings confirm the usefulness of the C_T N values as an indirect detection parameter to monitor viral loads in the population.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved rapidly, leading to viral lineages characterized by multiple mutations in the spike protein, which could potentially confer to the virus the ability to avoid the vaccine-induced immune response, making the vaccines less effective or ineffective. Here, we initially evaluated the neutralization capabilities in vitro by serum neutralization (SN) of six serum samples collected from recipients of the BNT162b2 vaccine against 11 SARS-CoV-2 isolates belonging to the major SARS-CoV-2 lineages that had been circulating in Italy. Then, we considered 30 additional serum samples by SN assay against the dominant B.1.617.2 (Delta) variant. A B.1 lineage isolate was used as a reference. In the first analysis, significant differences when compared with the reference strain (p > 0.05) were not evidenced; instead, when the panel of 30 sera was tested against the B.1.617.2 (Delta) variant, a significant (p = 0.0015) 2.38-fold reduction in neutralizing titres compared with the reference after the first vaccine dose was demonstrated. After the second vaccine dose, the reduction was not significant (p = 0.1835). This study highlights that the BNT162b2 vaccine stimulates a humoral response able to neutralize all tested SARS-CoV-2 variants, thus suggesting a prominent role in mitigating the impact of the SARS-CoV-2 pandemic in real-world conditions. Long-term follow-up is currently ongoing.

West Nile virus (WNV) is a mosquito-borne virus potentially causing serious illness in humans and other animals. Since 2004, several studies have highlighted the progressive spread of WNV Lineage 2 (L2) in Europe, with Italy being one of the countries with the highest number of cases of West Nile disease reported. In this paper, we give an overview of the epidemiological and genetic features characterising the spread and evolution of WNV L2 in Italy, leveraging data obtained from national surveillance activities between 2011 and 2021, including 46 newly assembled genomes that were analysed under both phylogeographic and phylodynamic frameworks. In addition, to better understand the seasonal patterns of the virus, we used a machine learning model predicting areas at high-risk of WNV spread. Our results show a progressive increase in WNV L2 in Italy, clarifying the dynamics of interregional circulation, with no significant introductions from other countries in recent years. Moreover, the predicting model identified the presence of suitable conditions for the 2022 earlier and wider spread of WNV in Italy, underlining the importance of using quantitative models for early warning detection of WNV outbreaks. Taken together, these findings can be used as a reference to develop new strategies to mitigate the impact of the pathogen on human and other animal health in endemic areas and new regions.

Novel SARS-CoV-2 variants with potential impacts on diagnostics, antivirals, and vaccines are spreading in Italy. In this editorial, we highlight the role that veterinary public health institutes may have in this global crisis, as their expertise in genomic/antigenic surveillance and animal studies are crucial to tackle SARS-CoV-2 pandemic.