Machine learning approach as an early warning system to prevent foodborne Salmonella outbreaks in northwestern Italy.

Salmonellosis, one of the most common foodborne infections in Europe, is monitored by food safety surveillance programmes, resulting in the generation of extensive databases. By leveraging tree-based machine learning (ML) algorithms, we exploited data from food safety audits to predict spatiotemporal patterns of salmonellosis in northwestern Italy. Data on human cases confirmed in 2015-2018 (n = 1969) and food surveillance data collected in 2014-2018 were used to develop ML algorithms. We integrated the monthly municipal human incidence with 27 potential predictors, including the observed prevalence of Salmonella in food. We applied the tree regression, random forest and gradient boosting algorithms considering different scenarios and evaluated their predictivity in terms of the mean absolute percentage error (MAPE) and R2. Using a similar dataset from the year 2019, spatiotemporal predictions and their relative sensitivities and specificities were obtained. Random forest and gradient boosting (R2 = 0.55, MAPE = 7.5%) outperformed the tree regression algorithm (R2 = 0.42, MAPE = 8.8%). Salmonella prevalence in food; spatial features; and monitoring efforts in ready-to-eat milk, fruits and vegetables, and pig meat products contributed the most to the models' predictivity, reducing the variance by 90.5%. Conversely, the number of positive samples obtained for specific food matrices minimally influenced the predictions (2.9%). Spatiotemporal predictions for 2019 showed sensitivity and specificity levels of 46.5% (due to the lack of some infection hotspots) and 78.5%, respectively. This study demonstrates the added value of integrating data from human and veterinary health services to develop predictive models of human salmonellosis occurrence, providing early warnings useful for mitigating foodborne disease impacts on public health.

First detection of an Italian human-to-cat outbreak of SARS-CoV-2 Alpha variant - lineage B.1.1.7.

The emergence of new SARS-CoV-2 variants and their rapid spread pose a threat to both human and animal health and may conceal unknown risks. This report describes an Italian human-to-cat outbreak of SARS-CoV-2 lineage B.1.1.7 (the Alpha variant) . On March 7th, 2021, approximately ten days after COVID-19 appeared in the family, the onset of respiratory signs in a cat by COVID-19-affected owners led to an in-depth diagnostic investigation, combining clinical and serological data with rt-qPCR-based virus detection and whole genome sequencing. The Alpha variant was confirmed first in the owners and a few days later in the cat that was then monitored weekly: the course was similar with one-week lag time in the cat. In addition, based on comparative analysis of genome sequences from our study and from 200 random Italian cases of Alpha variant, the familial cluster was confirmed. The temporal sequence along with the genomic data support a human-to-animal transmission. Such an event emphasizes the importance of studying the circulation and dynamics of SARS-CoV-2 variants in humans and animals to better understand and prevent potential spillover risks or unwarranted alerts involving our pet populations.

The Surveillance of Chikungunya Virus in a Temperate Climate: Challenges and Possible Solutions from the Experience of Lazio Region, Italy.

CHIKV has become an emerging public health concern in the temperate regions of the Northern Hemisphere as a consequenceof the expansion of the endemic areas of its vectors (mainly Aedes aegypti and Aedesalbopictus). In 2017, a new outbreak of CHIKV was detected in Italy with three clusters of autochthonous transmission in the Lazio Region (central Italy), in the cities of Anzio, Rome, and Latina and a secondary cluster in the Calabria Region (south Italy). Given the climate characteristics of Italy, sporadic outbreaks mostly driven by imported cases followed by autochthonous transmission could occur during the summer season. This highlights the importance of a well-designed surveillance system, which should promptly identify autochthonous transmission. The use of a surveillance system integrating different surveillance tools, including entomological surveillance in a one health approach, together with education of the health care professionals should facilitate the detection, response, and control of arboviruses spreading.

HIV incidence estimate among non-nationals in Italy.

We estimated the incidence of new HIV infections among non-nationals living in Italy for the period 1992-2004, calculated as the number of new diagnoses among legally and illegally residing non-nationals out of the number of new residence permits (which does not include illegal non-nationals). This incidence was compared to that among Italians by calculating the standardized incidence ratio (SIR) by age and gender. There were 17,309 new diagnoses; 19.0% were among non-nationals. The incidence of new diagnoses among non-nationals was 69 cases per 100,000 residence permits, compared to 8.7 per 100,000 population among Italians. The SIR confirmed the marked difference between the two populations, with the incidence being six times higher among non-nationals, compared to Italians. This difference increased over time: in 1992-1994, it was five times higher among non-nationals, compared to Italians, whereas it was eight times higher in 2002-2004. Although the incidence of infection among non-nationals seems to have decreased in the past 10 years, it is still high if compared to that among Italians, suggesting that non-nationals constitute a population subgroup with a high circulation of HIV. Furthermore, HIV is mainly transmitted through the sexual route among non-nationals, prevalently affecting younger persons and women. Access to testing, treatment, and care needs to be facilitated for non-nationals.