Prevalence of Chronic Hepatitis B Virus Infection in the United States.

Avian influenza is one of the largest known threats to domestic poultry. Influenza outbreaks on poultry farms typically lead to the complete slaughter of the entire domestic bird population, causing severe economic losses worldwide. Moreover, there are highly pathogenic avian influenza (HPAI) strains that are able to infect the swine or human population in addition to their primary avian host and, as such, have the potential of being a global zoonotic and pandemic threat. Migratory birds, especially waterfowl, are a natural reservoir of the avian influenza virus; they carry and exchange different virus strains along their migration routes, leading to antigenic drift and antigenic shift, which results in the emergence of novel HPAI viruses. This requires monitoring over time and in different locations to allow for the upkeep of relevant knowledge on avian influenza virus evolution and the prevention of novel epizootic and epidemic outbreaks. In this review, we assess the role of migratory birds in the spread and introduction of influenza strains on a global level, based on recent data. Our analysis sheds light on the details of viral dissemination linked to avian migration, the viral exchange between migratory waterfowl and domestic poultry, virus ecology in general, and viral evolution as a process tightly linked to bird migration. We also provide insight into methods used to detect and quantify avian influenza in the wild. This review may be beneficial for the influenza research community and may pave the way to novel strategies of avian influenza and HPAI zoonosis outbreak monitoring and prevention.

https://www.mdpi.com/2076-0817/10/5/630/pdf

Avian Influenza in Wild Birds and Poultry: Dissemination Pathways, Monitoring Methods, and Virus Ecology

Better methods to predict and prevent the emergence of zoonotic viruses could support future efforts to reduce the risk of epidemics. We propose a network science framework for understanding and predicting human and animal susceptibility to viral infections. Related approaches have so far helped to identify basic biological rules that govern cross-species transmission and structure the global virome. We highlight ways to make modelling both accurate and actionable, and discuss the barriers that prevent researchers from translating viral ecology into public health policies that could prevent future pandemics.

The science of the host-virus network.

Leveraging artificial intelligence (AI) approaches in animal health (AH) makes it possible to address highly complex issues such as those encountered in quantitative and predictive epidemiology, animal/human precision-based medicine, or to study host × pathogen interactions. AI may contribute (i) to diagnosis and disease case detection, (ii) to more reliable predictions and reduced errors, (iii) to representing more realistically complex biological systems and rendering computing codes more readable to non-computer scientists, (iv) to speeding-up decisions and improving accuracy in risk analyses, and (v) to better targeted interventions and anticipated negative effects. In turn, challenges in AH may stimulate AI research due to specificity of AH systems, data, constraints, and analytical objectives. Based on a literature review of scientific papers at the interface between AI and AH covering the period 2009–2019, and interviews with French researchers positioned at this interface, the present study explains the main AH areas where various AI approaches are currently mobilised, how it may contribute to renew AH research issues and remove methodological or conceptual barriers. After presenting the possible obstacles and levers, we propose several recommendations to better grasp the challenge represented by the AH/AI interface. With the development of several recent concepts promoting a global and multisectoral perspective in the field of health, AI should contribute to defract the different disciplines in AH towards more transversal and integrative research.

/pdf/research-perspectives-on-animal-health-in-the-era-of-13mj6go967.pdf

Research perspectives on animal health in the era of artificial intelligence.

Introduction Hepatitis B virus (HBV) is a bloodborne virus which can be transmitted via percutaneous and mucocutaneous exposure to infected body fluid. Healthcare workers (HCWs) who are continuously exposed to different body fluids are at an increased risk of contracting and transmitting this virus. It is thus important to evaluate the knowledge and attitude of HCWs towards HBV and the prevalence of HBV infection among them. Methods This cross-sectional study was carried out between April and September 2017. Overall, 398 HCWs were recruited for this study. Knowledge on the route of HBV transmission and attitude towards HBV were evaluated using a well-structured questionnaire. Hepatitis B surface antigen (HBsAg) positivity was obtained using the Monolisa HBsAg ULTRA kit (Bio-Rad). Data were analysed using SPSS V.20. Results Among the HCWs who participated in this study, 338 (84.9%) had heard of HBV, and 269 (67.6%) of them had adequate knowledge on the route of HBV transmission. Medical doctors were the most knowledgeable among biomedical workers and students (76.5%). The rate of stigma was highest among nurses (87, 38.8%). The prevalence of HBsAg positivity was high (42, 10.6%) given that there is an efficient and available vaccine. Overall, over 70% of HCWs invited to participate in this study responded. Conclusion Knowledge on the route of HBV transmission was fair, and the level of stigmatisation of HBV-infected patients and the prevalence of HBV infection were high in this study. A sensitisation campaign should be carried out to educate HCWs on HBV, thus reducing the level of stigma associated with HBV as well as the probability of contracting HBV as a nosocomial infection.

https://bmjopen.bmj.com/content/bmjopen/10/3/e031075.full.pdf

Knowledge, attitude and prevalence of hepatitis B virus among healthcare workers: a cross-sectional, hospital-based study in Bamenda Health District, NWR, Cameroon.

Hepatitis B virus (HBV) is considered highly prevalent in West Africa. However, major gaps in surveillance exist in Sierra Leone. Although healthcare workers (HCWs) are at high risk for HBV infection, little is known about the prevalence and knowledge of hepatitis B among HCWs in Sierra Leone. A cross-sectional study of all HCWs at the No. 34 Military Hospital located in Freetown, Sierra Leone, was conducted from March 20 to April 10, 2017. Whole blood was collected and screened for HBV markers using a one-step rapid immunochromatographic test with positive samples tested for HBV DNA. Additionally, questionnaires assessing self-reported knowledge of HBV infections were administered to all participants. Data were processed and analyzed using SPSS (version 17.0) software. A total of 211 HCWs were included in this study with a median age of 39.0 years (range: 18–59). Of the participating HCWs, 172 (81.5%) participants were susceptible (all markers negative), 21(10.0%) were current HBV (HBsAg positive) and nine (4.3%) were considered immune because of past infection (HBsAg negative and anti-HBc positive; anti-HBs positive). Additionally, nine (4.3%) participants displayed immunity to the virus as a result of prior hepatitis B vaccination (only anti-HBs positive). Of the 21 HCWs with positive HBsAg, 13 (61.9%) had detectable HBV DNA. There was a significantly lower risk for current HBV infection among HCWs older than 39 years (OR 0.337, p = 0.046). In addition, only 14 (6.6%), 73 (34.6%) and 82 (38.9%) participants in this survey had adequate knowledge about the clinical outcome, routes of transmission, and correct preventive measures of HBV infection, respectively. HCWs in Sierra Leone lacked adequate knowledge of the hepatitis B virus. Additionally, the low coverage rate of hepatitis B vaccination among HCWs fails to meet WHO recommendations, leaving many of the sampled HCWs susceptible to infection. This study reaffirms the need for more intensive training for HCWs in addition to strengthening vaccination programmes to protect HCWs against HBV in Sierra Leone.

/pdf/prevalence-and-associated-knowledge-of-hepatitis-b-infection-40nihw4mq3.pdf

Prevalence and associated knowledge of hepatitis B infection among healthcare workers in Freetown, Sierra Leone

Each influenza pandemic was caused at least partly by avian- and/or swine-origin influenza A viruses (IAVs). The timing of and the potential IAVs involved in the next pandemic are currently unpredictable. We aim to build machine learning (ML) models to predict human-adaptive IAV nucleotide composition. A total of 217,549 IAV full-length coding sequences of the PB2 (polymerase basic protein-2), PB1, PA (polymerase acidic protein), HA (hemagglutinin), NP (nucleoprotein), and NA (neuraminidase) segments were decomposed for their codon position-based mononucleotides (12 nts) and dinucleotides (48 dnts). A total of 68,742 human sequences and 68,739 avian sequences (1:1) were resampled to characterize the human adaptation-associated (d)nts with principal component analysis (PCA) and other ML models. Then, the human adaptation of IAV sequences was predicted based on the characterized (d)nts. Respectively, 9, 12, 11, 13, 10 and 9 human-adaptive (d)nts were optimized for the six segments. PCA and hierarchical clustering analysis revealed the linear separability of the optimized (d)nts between the human-adaptive and avian-adaptive sets. The results of the confusion matrix and the area under the receiver operating characteristic curve indicated a high performance of the ML models to predict human adaptation of IAVs. Our model performed well in predicting the human adaptation of the swine/avian IAVs before and after the 2009 H1N1 pandemic. In conclusion, we identified the human adaptation-associated genomic composition of IAV segments. ML models for IAV human adaptation prediction using large IAV genomic data sets can facilitate the identification of key viral factors that affect virus transmission/pathogenicity. Most importantly, it allows the prediction of pandemic influenza.

Machine Learning Methods for Predicting Human-Adaptive Influenza A Viruses Based on Viral Nucleotide Compositions.

Objective: To assess Healthcare workers (HCWs) knowledge and experiences with infection prevention and control (IPC) practices and towards occupational exposure in Sierra Leone, after the 20142015 Ebola virus disease outbreak, which in turn can be used to identify areas that require additional training. Study Design: A cross-sectional study. Methods: From December 9 to 23, 2016, questionnaires contained 45 questions spanning five themes focusing on knowledge and practice and training were administered to all clinical HCWs at the No. 34 Military Hospital, Freetown, Sierra Leone. Main outcome measures are knowledge and practices score on occupational exposure and protection from infectious disease.

/pdf/self-reported-knowledge-and-practices-of-healthcare-workers-2r1nft3lrz.pdf

Self-Reported Knowledge and Practices of Healthcare Workers on Occupational Exposure and Protection from Infectious Disease at the Military Hospital in Sierra Leone

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Prevalence and associated knowledge of hepatitis B infection among healthcare workers in Freetown, Sierra Leone

Machine Learning Methods for Predicting Human-Adaptive Influenza A Viruses Based on Viral Nucleotide Compositions.

Self-Reported Knowledge and Practices of Healthcare Workers on Occupational Exposure and Protection from Infectious Disease at the Military Hospital in Sierra Leone