48 research outputs found
Serologic evidence of occupational exposure to avian influenza viruses at the wildfowl/poultry/human interface
Ecological interactions between wild aquatic birds and outdoor-housed poultry can enhance spillover events of avian influenza viruses (AIVs) from wild reservoirs to domestic birds, thus increasing the related zoonotic risk to occupationally exposed workers. To assess serological evidence of AIV infection in workers operating in Northern Italy at the wildfowl/poultry interface or directly exposed to wildfowl, serum samples were collected between April 2005 and November 2006 from 57 bird-exposed workers (BEWs) and from 7 unexposed controls (Cs), planning three sample collec-tions from each individual. Concurrently, AIV surveillance of 3587 reared birds identified 4 AIVs belonging to H10N7, H4N6 and H2N2 subtypes while serological analysis by hemagglutination inhibition (HI) assay showed recent infections caused by H1, H2, H4, H6, H10, H11, H12, and H13 subtypes. Human sera were analyzed for specific antibodies against AIVs belonging to antigenic subtypes from H1 to H14 by using HI and virus microneutralization (MN) assays as a screening and a confirmatory test, respectively. Overall, antibodies specific to AIV-H3, AIV-H6, AIV-H8, and AIV-H9 were found in three poultry workers (PWs) and seropositivity to AIV-11, AIV-H13âstill detectable in October 2017âin one wildlife professional (WP). Furthermore, seropositivity to AIV-H2, accounting for previous exposure to the âextinctâ H2N2 human influenza viruses, was found in both BEWs and Cs groups. These data further emphasize the occupational risk posed by zoonotic AIV strains and show the possible occurrence of long-lived antibody-based immunity following AIV infections in humans
Cross-protection against European swine influenza viruses in the context of infection immunity against the 2009 pandemic H1N1 virus : studies in the pig model of influenza
Pigs are natural hosts for the same influenza virus subtypes as humans and are a valuable model for cross-protection studies with influenza. In this study, we have used the pig model to examine the extent of virological protection between a) the 2009 pandemic H1N1 (pH1N1) virus and three different European H1 swine influenza virus (SIV) lineages, and b) these H1 viruses and a European H3N2 SIV. Pigs were inoculated intranasally with representative strains of each virus lineage with 6- and 17-week intervals between H1 inoculations and between H1 and H3 inoculations, respectively. Virus titers in nasal swabs and/or tissues of the respiratory tract were determined after each inoculation. There was substantial though differing cross-protection between pH1N1 and other H1 viruses, which was directly correlated with the relatedness in the viral hemagglutinin (HA) and neuraminidase (NA) proteins. Cross-protection against H3N2 was almost complete in pigs with immunity against H1N2, but was weak in H1N1/pH1N1-immune pigs. In conclusion, infection with a live, wild type influenza virus may offer substantial cross-lineage protection against viruses of the same HA and/or NA subtype. True heterosubtypic protection, in contrast, appears to be minimal in natural influenza virus hosts. We discuss our findings in the light of the zoonotic and pandemic risks of SIVs
Cross-protection against European swine influenza viruses in the context of infection immunity against the 2009 pandemic H1N1 virus: studies in the pig model of influenza
a cognitive future internet architecture
This Chapter proposes a novel Cognitive Framework as reference architecture for the Future Internet (FI), which is based on so-called Cognitive Managers. The objective of the proposed architecture is twofold. On one hand, it aims at achieving a full interoperation among the different entities constituting the ICT environment, by means of the introduction of Semantic Virtualization Enablers, in charge of virtualizing the heterogeneous entities interfacing the FI framework. On the other hand, it aims at achieving an inter-network and inter-layer cross-optimization by means of a set of so-called Cognitive Enablers, which are in charge of taking consistent and coordinated decisions according to a fully cognitive approach, availing of information coming from both the transport and the service/content layers of all networks. Preliminary test studies, realized in a home environment, confirm the potentialities of the proposed solution
Epidemiological characteristics of COVID-19 cases and estimates of the reproductive numbers 1 month into the epidemic, Italy, 28 January to 31 March 2020
BackgroundOn 20 February 2020, a locally acquired coronavirus disease (COVID-19) case was detected in Lombardy, Italy. This was the first signal of ongoing transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the country. The number of cases in Italy increased rapidly and the country became the first in Europe to experience a SARS-CoV-2 outbreak.AimOur aim was to describe the epidemiology and transmission dynamics of the first COVID-19 cases in Italy amid ongoing control measures.MethodsWe analysed all RT-PCR-confirmed COVID-19 cases reported to the national integrated surveillance system until 31 March 2020. We provide a descriptive epidemiological summary and estimate the basic and net reproductive numbers by region.ResultsOf the 98,716 cases of COVID-19 analysed, 9,512 were healthcare workers. Of the 10,943 reported COVID-19-associated deaths (crude case fatality ratio: 11.1%) 49.5% occurred in casesâolder thanâ80 years. Male sex and age were independent risk factors for COVID-19 death. Estimates of R0 varied between 2.50 (95% confidence interval (CI): 2.18-2.83) in Tuscany and 3.00 (95% CI: 2.68-3.33) in Lazio. The net reproduction number Rt in northern regions started decreasing immediately after the first detection.ConclusionThe COVID-19 outbreak in Italy showed a clustering onset similar to the one in Wuhan, China. R0 at 2.96 in Lombardy combined with delayed detection explains the high case load and rapid geographical spread. Overall, Rt in Italian regions showed early signs of decrease, with large diversity in incidence, supporting the importance of combined non-pharmacological control measures
2015/16 seasonal vaccine effectiveness against hospitalisation with influenza a(H1N1)pdm09 and B among elderly people in Europe: Results from the I-MOVE+ project
We conducted a multicentre test-negative caseù\u80\u93control study in 27 hospitals of 11 European countries to measure 2015/16 influenza vaccine effectiveness (IVE) against hospitalised influenza A(H1N1)pdm09 and B among people aged ù\u89„ 65 years. Patients swabbed within 7 days after onset of symptoms compatible with severe acute respiratory infection were included. Information on demographics, vaccination and underlying conditions was collected. Using logistic regression, we measured IVE adjusted for potential confounders. We included 355 influenza A(H1N1)pdm09 cases, 110 influenza B cases, and 1,274 controls. Adjusted IVE against influenza A(H1N1)pdm09 was 42% (95% confidence interval (CI): 22 to 57). It was 59% (95% CI: 23 to 78), 48% (95% CI: 5 to 71), 43% (95% CI: 8 to 65) and 39% (95% CI: 7 to 60) in patients with diabetes mellitus, cancer, lung and heart disease, respectively. Adjusted IVE against influenza B was 52% (95% CI: 24 to 70). It was 62% (95% CI: 5 to 85), 60% (95% CI: 18 to 80) and 36% (95% CI: -23 to 67) in patients with diabetes mellitus, lung and heart disease, respectively. 2015/16 IVE estimates against hospitalised influenza in elderly people was moderate against influenza A(H1N1)pdm09 and B, including among those with diabetes mellitus, cancer, lung or heart diseases
Active Routing in Green Home Networks
This paper proposes the idea of a new class of routing protocols, named active routing, in which the routing decisions are not only related to the choice of the best path given a topology, but they also concerns topology modifications in order to reduce energy consumption. The scenario is a hybrid home network, where different technologies, mainly wireless, are available, like in the home network envisaged within the ICT OMEGA project. In order for the proposed class of routing protocol to work properly, the home network is controlled by a network manager which is capable of driving the interfaces of the available devices, in such a way that links can be switched on/off to decide the home network topology in such a way it is possible to minimize the energy consumption of the communication interface
A MDP Approach to Fault-Tolerant Routing
This paper defines a theoretical framework based on Markov Decision Processes (MDP) to deal with fault-tolerant routing algorithms in heterogeneous home networks, which are realized through the integration of different wired and wireless telecommunication technologies. Such networks are characterized by fast dynamics of link availability, mainly due to the wide use of wireless technologies. The novelty of this paper is the MDP approach to the fault-tolerant routing problem, which is addressed by introducing a re-routing policy: when a path becomes unavailable, the flows transmitted over that path are re-routed on another available path; the new path is selected taking into consideration the probability that also the new path can become unavailable in the future, in order to minimize re-routing occurrences. Numerical simulations show the effectiveness of the proposed approach. ©2009 IEEE
A proposal for future internet architecture
This paper deals with an autonomous cognitive network management architecture which aims at achieving inter-network (horizontal) and inter-layer (vertical) cross-optimization. The proposed architecture is based on the so-called Cognitive Managers transparently embedded in properly selected network nodes. The core of each Cognitive Manager are the so-called thinking modules, which are in charge of taking consistent and coordinated decisions according to a fully cognitive approach. The thinking modules potentially avail of information coming from both the transport and the service/content layers of all networks and, based on all this inter-layer and inter-network information, take consistent and coordinated decisions impacting the different layers, aiming at the overall inter-layer, inter-network optimization. Copyright © 2010 The authors