A web-based geographic information system monitoring wildlife diseases in Abruzzo and Molise regions, Southern Italy.

BACKGROUND: Nowadays there is a worldwide consensus on the importance of conducting wildlife disease surveillance. Indeed, 60% of emerging infectious diseases are zoonotic in nature, and the majority of these (71.8%) originate in wildlife. Surveillance of wildlife diseases is crucial to prevent negative effects on human and animal health. Data digitization and sharing are among the main goals for the present and coming years. Geographic Information Systems (GIS) are increasingly used to analyze the geographical distribution of diseases and the relationships between pathogenic factors and their geographic environments. METHODS: Wild animal's samples collected in the Abruzzo and Molise regions and delivered to our laboratory are entered in our Laboratory Information System and processed to be displayed through the Web-GIS mash-up presented in this paper. We built it using both open source and proprietary solutions, to produce data driven interactive maps, charts and tables to help to understand the epidemiology of wild animal diseases, their spread and trend. RESULTS: Since 2013, 9.606 samples collected from wild animals have been analyzed in the laboratories of the IZS-Teramo and have been recorded in the system, facilitating the reporting to the judicial authorities and the identification of highly risky areas to set up control and repression measures. Moreover, thanks to the monitoring health protocol, a canine distemper epidemic in wolves has been detected and monitored in its temporal and spatial evolution, as well as cases of bovine tuberculosis in wild boars. CONCLUSIONS: While it is more evident that the starting point is to choose the right sampling method, it is for sure less obvious that the information system in which data is stored is equally important. In fact, it should give the possibility to consult it in an easy and instructive way. GIS allows immediately grasping the spatial relationships between the data itself and those between the data and the territory; it is an important tool to support veterinary services in managing epidemic and non-epidemic emergencies and performing epidemiological investigations, but also to examine control plans and identify new gaps and challenges.

Lumpy Skin Disease.

Lumpy skin disease (LSD) is a contagious non-zoonotic viral disease of cattle. The disease raises great concern due to the recent rapid spread toward free countries and reoccurrence in countries where control and preventive measures had achieved eradication. Deep nodules involving skin, subcutaneous tissue, and occasionally muscles are localized mostly in the head, neck, perineum, genitalia, udder, and limbs. LSD can cause large economic losses mainly because of the decline in milk production and the decrease in hide value, in addition to the ban of movement of animals and animal products.

Epidemiological Significance of SARS-CoV-2 RNA Dynamic in Naso-Pharyngeal Swabs.

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 (CT N) median values and gender. Differences were observed in the CT 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 CT 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 CT N values as an indirect detection parameter to monitor viral loads in the population.