One health system supporting surveillance during COVID-19 epidemic in Abruzzo region, southern Italy

T-Racing: a modern tool for supporting epidemiological investigation in animal disease outbreaks in Italy

The One Health approach unites efforts across human-animal-environment interfaces against shared threats like zoonotic diseases. T-Racing is a Shiny web application, that supports epidemiological investigations and helps contain livestock-related disease spread, aligning with multidisciplinary principles to safeguard public health. The application uses Temporal Network Analysis techniques to address the dynamic nature of animal trade, facilitating backward and forward tracing strategies. T-Racing leverages web services to retrieve data from multiple sources simultaneously and in near real-time through the plumber package and is distributed using Shinyproxy. T-Racing manages and analyze extensive and diverse datasets within the same environment, including animal movement data, disease outbreak data, and genomic data, all obtained from Italian National databases. In this work, we show T-Racing's capabilities by simulating epidemiological investigations of brucellosis and tuberculosis outbreaks that occurred in non-endemic areas of Italy. To further highlight its capabilities, an interactive demo of T-Racing is available, showcasing its potential and features. This tool supports epidemiological investigations by adopting a data-driven approach, guiding users through the analysis via an iterative process while leveraging their expertise. Therefore, it enables faster data analysis, improves understanding of disease transmission patterns, and facilitates prompt and targeted interventions.

Surveillance strategies for SARS-CoV-2 infections through one health approach

Coronavirus disease-2019 (COVID-19), caused by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), is an emergent disease that threatens global health. Public health structures and economic activities have been disrupted globally by the COVID-19 pandemic. Over 556.3 million confirmed cases and 6.3 million deaths have been reported. However, the exact mechanism of its emergence in humans remains unclear. SARS-CoV-2 is believed to have a zoonotic origin, suggesting a spillover route from animals to humans, which is potentially facilitated by wildlife farming and trade. The COVID-19 pandemic highlighted the importance of the One Health approach in managing threats of zoonosis in the human-animal-environment interaction. Implementing vigilant surveillance programs by adopting the One Health concept at the interfaces between wildlife, livestock, and humans is the most pertinent, practical, and actionable strategy for preventing and preparing for future pandemics of zoonosis, such as COVID-19 infection. This review summarizes the updated evidence of CoV infections in humans and animals and provides an appropriate strategy for preventive measures focused on surveillance systems through an On Health approach.

Sheep and goat pathogen database: a pathogen data integration and analysis database of sheep and goat infectious diseases

The prevalence of infectious diseases in sheep and goats has a significant impact on the development of the sheep and goat industry and public health security. The identification and analysis of pathogens are crucial for infectious disease research; however, existing databases pay little attention to sheep and goat diseases, and pathogen data are relatively scattered. Therefore, the effective integration, analysis and visualization of these data will help us conduct in-depth research on sheep and goat infectious diseases and promote the formulation of disease prevention and control strategies. This article considered the pathogens of 44 infectious diseases in sheep and goats as the main research objects and collected and downloaded relevant scientific literature, pathogen genomes, pathogen transcriptomes, pathogen occurrence records, and other data. The C# programming language and an SQL Server database were used to construct and realize the functions of the Sheep and Goat Pathogen Database (SGPD) within a B/S architecture based on the ASP.NET platform. The SGPD mainly provides an integrated platform for sheep and goat pathogen data retrieval, auxiliary analysis, and user upload, including several functionalities: (1) a Disease Introduction module that queries basic information regarding the 44 recorded sheep and goat infectious diseases, such as epidemiology, clinical characteristics, diagnostic criteria, and prevention and control measures; (2) an Omics Information module that allows users to query and download the genome and transcriptome data related to the pathogens of sheep and goat infectious disease, and provide sequence alignment functionality; (3) a Pathogen Structure module that enables users to view electron micrographs of pathogen structure and tissue sections related to sheep and goat disease from publicly published research; (4) a Literature Search module based on the "Pathogen Dictionary" search strategy that facilitates searches for published research related to pathogens of infectious disease; (5) a Science Popularization module that allows users to view popular science materials related to sheep and goat infectious diseases; and (6) a Public Health module that allows users to query the risk factors of zoonotic disease transmission and the corresponding related literature, and realize the visualization of pathogen distribution. The SGPD is a specialized sheep and goat pathogen information database that provides comprehensive resources and technical support for sheep and goat infectious disease research, prevention, and control.

Consistent Healthcare Safety Recommendation System for Preventing Contagious Disease Infections in Human Crowds

The recent impact of COVID-19, as a contagious disease, led researchers to focus on designing and fabricating personal healthcare devices and systems. With the help of wearable sensors, sensing and communication technologies, and recommendation modules, personal healthcare systems were designed for ease of use. More specifically, personal healthcare systems were designed to provide recommendations for maintaining a safe distance and avoiding contagious disease spread after the COVID-19 pandemic. The personal recommendations are analyzed based on the wearable sensor signals and their consistency in sensing. This consistency varies with human movements or other activities that hike/cease the sensor values abruptly for a short period. Therefore, a consistency-focused recommendation system (CRS) for personal healthcare (PH) was designed in this research. The hardware sensing intervals for the system are calibrated per the conventional specifications from which abrupt changes can be observed. The changes are analyzed for their saturation and fluctuations observed from neighbors within the threshold distance. The saturation and fluctuation classifications are performed using random forest learning to differentiate the above data from the previously sensed healthy data. In this process, the saturated data and consistency data provide safety recommendations for the moving user. The consistency is verified for a series of intervals for the fluctuating sensed data. This alerts the user if the threshold distance for a contagious disease is violated. The proposed system was validated using a prototype model and experimental analysis through false rates, data analysis rates, and fluctuations.