Background: On 9th January 2020, China CDC reported a novel coronavirus (later named SARS-CoV-2) as the causative agent of the coronavirus disease 2019 (COVID-19). Identifying the first appearance of virus is of epidemiological importance to tracking and mapping the spread of SARS-CoV-2 in a country. We therefore conducted a retrospective observational study to detect SARS-CoV-2 in oropharyngeal samples collected from hospitalized patients with a Severe Acute Respiratory Infection (SARI) enrolled in the DRIVE (Development of Robust and Innovative Vaccine Effectiveness) study in five Italian hospitals (CIRI-IT BIVE hospitals network) (1st November 2019 - 29th February 2020).
View Article and Find Full Text PDFConclusions: Despite some limits, our findings support the notion that deep learning methods can be used to simplify the diagnostic process and improve disease management.
Background: In order to help physicians and radiologists in diagnosing pneumonia, deep learning and other artificial intelligence methods have been described in several researches to solve this task. The main objective of the present study is to build a stacked hierarchical model by combining several models in order to increase the procedure accuracy.
Background: Influenza is a relevant public health problem, also due to the risk of complications. The most effective measure to prevent influenza is vaccination; therefore, at present, there is consensus among European countries, regarding the need for routine seasonal influenza vaccination of elderly and individuals at increased risk of severe influenza. At the same time, influenza surveillance is necessary to understand the viruses circulating and effectiveness of vaccination strategies.
View Article and Find Full Text PDFIn March 2020, the World Health Organization (WHO) declared that the COVID-19 outbreak recorded over the previous months could be characterized as a pandemic. The first known Italian SARS-CoV-2 positive case was reported on 21 February. In some countries, cases of suspected "COVID-19-like pneumonia" had been reported earlier than those officially accepted by health authorities.
View Article and Find Full Text PDFAim: To develop an instrument to investigate knowledge and predictive factors of needlestick and sharps injuries (NSIs) in nursing students during clinical placements.
Design: Instrument development and cross-sectional study for psychometric testing.
Methods: A self-administered instrument including demographic data, injury epidemiology and predictive factors of NSIs was developed between October 2018-January 2019.