Search

Culex pipiens, an important vector of many vector borne diseases, is a species capable to feeding on a wide variety of hosts and adapting to different environments. To predict the potential distribution of Cx. pipiens in central Italy, this study integrated presence/absence data from a four-year entomological survey (2019-2022) carried out in the Abruzzo and Molise regions, with a datacube of spectral bands acquired by Sentinel-2 satellites, as patches of 224 × 224 pixels of 20 meters spatial resolution around each site and for each satellite revisit time. We investigated three scenarios: the baseline model, which considers the environmental conditions at the time of collection; the multitemporal model, focusing on conditions in the 2 months preceding the collection; and the MultiAdjacency Graph Attention Network (MAGAT) model, which accounts for similarities in temperature and nearby sites using a graph architecture. For the baseline scenario, a deep convolutional neural network (DCNN) analyzed a single multi-band Sentinel-2 image. The DCNN in the multitemporal model extracted temporal patterns from a sequence of 10 multispectral images; the MAGAT model incorporated spatial and climatic relationships among sites through a graph neural network aggregation method. For all models, we also evaluated temporal lags between the multi-band Earth Observation datacube date of acquisition and the mosquito collection, from 0 to 50 days. The study encompassed a total of 2,555 entomological collections, and 108,064 images (patches) at 20 meters spatial resolution. The baseline model achieved an F1 score higher than 75.8% for any temporal lag, which increased up to 81.4% with the multitemporal model. The MAGAT model recorded the highest F1 score of 80.9%. The study confirms the widespread presence of Cx. pipiens throughout the majority of the surveyed area. Utilizing only Sentinel-2 spectral bands, the models effectively capture early in advance the temporal patterns of the mosquito population, offering valuable insights for directing surveillance activities during the vector season. The methodology developed in this study can be scaled up to the national territory and extended to other vectors, in order to support the Ministry of Health in the surveillance and control strategies for the vectors and the diseases they transmit. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]