Background Natural and vaccine-induced immunity will play a key role in controlling the SARS-CoV-2 pandemic. SARS-CoV-2 variants have the potential to evade natural and vaccine-induced immunity. Methods In a longitudinal cohort study of healthcare workers (HCWs) in Oxfordshire, UK, we investigated the protection from symptomatic and asymptomatic PCR-confirmed SARS-CoV-2 infection conferred by vaccination (Pfizer-BioNTech BNT162b2, Oxford-AstraZeneca ChAdOx1 nCOV-19) and prior infection (determined using anti-spike antibody status), using Poisson regression adjusted for age, sex, temporal changes in incidence and role. We estimated protection conferred after one versus two vaccinations and from infections with the B.1.1.7 variant identified using whole genome sequencing. Results 13,109 HCWs participated; 8285 received the Pfizer-BioNTech vaccine (1407 two doses) and 2738 the Oxford-AstraZeneca vaccine (49 two doses). Compared to unvaccinated seronegative HCWs, natural immunity and two vaccination doses provided similar protection against symptomatic infection: no HCW vaccinated twice had symptomatic infection, and incidence was 98% lower in seropositive HCWs (adjusted incidence rate ratio 0.02 [95%CI <0.01-0.18]). Two vaccine doses or seropositivity reduced the incidence of any PCR-positive result with or without symptoms by 90% (0.10 [0.02-0.38]) and 85% (0.15 [0.08-0.26]) respectively. Single-dose vaccination reduced the incidence of symptomatic infection by 67% (0.33 [0.21-0.52]) and any PCR-positive result by 64% (0.36 [0.26-0.50]). There was no evidence of differences in immunity induced by natural infection and vaccination for infections with S-gene target failure and B.1.1.7. Conclusion Natural infection resulting in detectable anti-spike antibodies and two vaccine doses both provide robust protection against SARS-CoV-2 infection, including against the B.1.1.7 variant.
Big data analytics provide valuable information allowing organizations to gain insights that grant them a competitive advantage in the market. However, it also provides access to data that compromise people's privacy. The development of sophisticated technologies for data analysis has resulted in a growing concern around privacy management in big data. While many sites (e.g. Facebook) require the user to provide personal information to access their services, others (e.g. Google search) can automatically capture or trace user activities and use that data to acquire personal demographic information. Therefore, Internet users are -willingly or unwillingly -constantly disclosing sensitive personal information. In addition, users do not get a complete picture of how their personal information is disseminated online. In this paper, we investigate information privacy through an experiment using large-scale disclosure of personal web activity data to track fragments of personal information released over a period of time. This experiment gives a clear picture of the potential privacy losses of individual users based on released personal information and activities at different websites. By devising an enterprise architecture using a privacy-by-design framework, this study provides a useful guide to addressing the managerial challenges of privacy management.
Mudharabah financing is demanded by the transparency of both parties. If one of the parties, especially customers, does not transparently communicate matters related to yield, adverse selection activities can occur and moral hazard is an asymmetric information problem that causes agency problems. Agency problems in mudharabah financing need to be sought to solve the problem so that the owners of capital (shahibul mal / principal) and business actors (mudarib / agents) both benefit in fostering and capturing business partners. This study aims to 1) find out the agency problem that arises in the mudharabah financing scheme in BMT in Jepara Regency, 2) find an effective solution strategy from agency problems in mudharabah financing in BMT in Jepara Regency. This study uses a qualitative approach. Data collection with interviews, observations and documentation. Data analysis techniques use data reduction, data displays and conclusion drawing/verification. Agency The problem that arises in the financing of Mudharobah in the Jepara Regency BMT is that it comes from BMT as an agency, that is, it has not fully utilized management, besides that it is caused by moral standards, financing aspects, technical aspects, and efficiency issues. Effective problem solving strategies of agency problems in Mudharobah financing in BMT in Jepara Regency are project screening, screening mudhorib, compliance with shahibul maal and mundharib on sharia rules in mudaraba contracts.
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