During the late summer of 1998, veterinary authorities in Tuscany, Italy, received reports of cases of neurologic disease among horses residing in a large wetland area located in the provinces of Florence and Pistoia. West Nile virus was isolated from two of the six horses that died or were euthanized. A retrospective epidemiologic study identified 14 clinical neurologic cases that occurred from August 20 to October 6 (attack rate of 2.8%). A serologic survey conducted over a 700-km2 area in stables with and without apparent clinical cases confirmed a wider spread of the infection, with an overall seroprevalence rate of 38% in the affected area. No significant differences in age-specific prevalence were observed, suggesting that the horses residing in the area had not been exposed previously to West Nile virus and supporting the hypothesis of its introduction in the wetland area during the first half of 1998.

Understanding how epidemics spread in a system is a crucial step to prevent and control outbreaks, with broad implications on the system’s functioning, health, and associated costs. This can be achieved by identifying the elements at higher risk of infection and implementing targeted surveillance and control measures. One important ingredient to consider is the pattern of disease-transmission contacts among the elements, however lack of data or delays in providing updated records may hinder its use, especially for time-varying patterns. Here we explore to what extent it is possible to use past temporal data of a system’s pattern of contacts to predict the risk of infection of its elements during an emerging outbreak, in absence of updated data. We focus on two real-world temporal systems; a livestock displacements trade network among animal holdings, and a network of sexual encounters in high-end prostitution. We define the node’s loyalty as a local measure of its tendency to maintain contacts with the same elements over time, and uncover important non-trivial correlations with the node’s epidemic risk. We show that a risk assessment analysis incorporating this knowledge and based on past structural and temporal pattern properties provides accurate predictions for both systems. Its generalizability is tested by introducing a theoretical model for generating synthetic temporal networks. High accuracy of our predictions is recovered across different settings, while the amount of possible predictions is system-specific. The proposed method can provide crucial information for the setup of targeted intervention strategies. Following the emergence of a transmissible disease epidemic, interventions and resources need to be prioritized to efficiently control its spread. While the knowledge of the pattern of disease-transmission contacts among hosts would be ideal for this task, the continuously changing nature of such pattern makes its use less practical in real public health emergencies (or otherwise highly resource-demanding when possible). We show that in such situations critical knowledge to assess the real-time risk of infection can be extracted from past temporal contact data. An index expressing the conservation of contacts over time is proposed as an effective tool to prioritize interventions, and its efficiency is tested considering real data on livestock movements and on human sexual encounters.

The interaction between living beings, including men, animals and pathogens, sharing the same environment, should be considered as a unique dynamic system, in which the health of each component is inextricably interconnected and dependent with the others. Nowadays, a new integrated One Health approach is reflecting this interdependence with a holistic view to the ecological system. The One Health approach can be defined as a collaborative and a multidisciplinary effort at local, national and global level to guarantee an optimal healthy status for humans, animals and environment. Strictly related to the One Health concept is to be considered the control of infectious diseases, which have influenced the course of human history. Four different components might be identified as key elements within the ‘One World – One Health’ (OWOH) approach: the geographical component, the ecological one, the human activities and the food‐agricultural ones.

In recent years, novel Bluetongue virus (BTV) serotypes have been isolated and/or sequenced by researchers within the field. During Bluetongue surveillance activities, we identified a putative novel BTV serotype in healthy goats from Sardinia, Italy. RNAs purified from blood and serum samples were positive for BTV by a generic real time RT-PCR and c-ELISA, respectively, whereas genotyping and serotyping were unsuccessful. By NGS, the whole genome sequence was obtained from two blood samples (BTV-X ITL2015 strains 34200 and 33531). Overall, Seg 2 of BTV-X ITL2015 shows the highest identity (75.3-75.5% nt/77.4-78.1% aa) with recently isolated BTV-27s from Corsica and with the last discovered BTV XJ1407 from China (75.9% nt /78.2% aa), whereas it is less related with BTV-25 from Switzerland (73.0% nt/75.0% aa) and BTV-26 from Kuwait (62.0% nt/60.5% aa). A specific RT-qPCR targeting Seg 2 of BTV-X ITL2015 was assessed in this study. Considering the Seg 2/VP2 identity of BTV-X ITL2015 with BTV-25, 26, 27s and BTV XJ1407 and that serum of BTV-X ITL2015 infected goats failed to neutralize all tested extant serotypes, we propose the existence of a novel BTV serotype circulating in goats in Sardinia. Isolation was so far unsuccessful thus hampering proper antigenic characterization.